From a51b0f7bd69e0f9e60375ca7df4184fc6eab46f0 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 14:38:09 -0600 Subject: [PATCH 01/25] feat(signal-score): project setup Add CLAUDE.md, basic-memory context, data import script, .env.local.example, and gitignore updates. Refs: #591 --- .env.local.example | 2 + .gitignore | 3 + CLAUDE.md | 113 +++++++++++++++++ context/memory/README.md | 13 ++ .../decisions/signal-score-architecture.md | 33 +++++ .../research/pattern-discovery-findings.md | 37 ++++++ .../research/trust-equation-framework.md | 54 ++++++++ scripts/signal-score/import_data.rb | 119 ++++++++++++++++++ 8 files changed, 374 insertions(+) create mode 100644 .env.local.example create mode 100644 CLAUDE.md create mode 100644 context/memory/README.md create mode 100644 context/memory/decisions/signal-score-architecture.md create mode 100644 context/memory/research/pattern-discovery-findings.md create mode 100644 context/memory/research/trust-equation-framework.md create mode 100644 scripts/signal-score/import_data.rb diff --git a/.env.local.example b/.env.local.example new file mode 100644 index 00000000..e8702016 --- /dev/null +++ b/.env.local.example @@ -0,0 +1,2 @@ +# Copy to .env.local and fill in your keys +ANTHROPIC_API_KEY=sk-ant-... diff --git a/.gitignore b/.gitignore index f60b08fa..39349260 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,10 @@ .ruby-gemset .bundle .env +.env.local .envrc +.scratch/ +context/memory/.basic-memory/ db/*.sqlite3 log/*.log tmp/ diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..bcefbc0d --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,113 @@ +# CLAUDE.md — Instructions for Claude Code Sessions + +This file is read automatically at the start of every Claude Code session in this workspace. + +## Project + +**Awesomebits** — the web application for the Awesome Foundation, a global micro-granting community that gives $1,000 grants to "awesome" projects. This is a fork (`divideby0/awesomebits`) of the upstream repo (`awesomefoundation/awesomebits`). + +We're building **Signal Score** — an AI-assisted application pre-screening system that helps trustees prioritize grant applications by surfacing quality signals. See GitHub issue #590. + +## Project Structure + +``` +. +├── app/ # Rails application +│ ├── extras/ # SpamChecker, SpamClassifier +│ ├── models/ # ActiveRecord models (Project, Chapter, etc.) +│ └── ... +├── context/ +│ └── memory/ # basic-memory knowledge base for Signal Score +├── scripts/ +│ └── signal-score/ # Signal Score tooling (Ruby) +├── .scratch/ # Local data, never committed +│ └── data/ # Parquet, CSV, DuckDB files +├── CLAUDE.md # This file +└── ... +``` + +## Tech Stack + +- **Ruby on Rails** — existing app (Ruby 3.3.6, Rails 7.2.3) +- **PostgreSQL** — production database +- **Ruby** — Signal Score scripts (same toolchain, future Rails integration) +- **DuckDB** — local analytical queries on historical grant data + +## Secrets & Configuration + +- **`.env.local`** — personal API keys and secrets (gitignored). Never use `.env` for secrets. +- `dotenv-rails` is in the Gemfile and loads `.env.local` automatically. +- Required keys: + ``` + ANTHROPIC_API_KEY=sk-ant-... + ``` + +## Git & Commits + +### Commit Lint +- **Header max 50 characters** — this is strict, plan for it +- **Body lines max 72 characters** +- **Format:** `type(scope): description` +- **Types:** feat, fix, refactor, chore, docs, test, perf, ci +- **Valid scopes:** signal-score, spam, data, scripts, context, infra, docs, deps, repo +- **References required:** Include `Refs: #591` or `Closes: #N` in the body +- **Subject must be lowercase** — no sentence-case, start-case, pascal-case, or upper-case + +### Branch Convention +- Feature branches: `feat/-` (e.g. `feat/0591-signal-score-scripts`) +- Always branch from `master` (upstream default) + +### Git Remotes +- `origin` → `awesomefoundation/awesomebits` (upstream, read-only for us) +- `fork` → `divideby0/awesomebits` (our fork, push here) +- Push to `fork`, PR against `origin` + +## Data + +### Historical Grant Data +Historical application data is exported from the Awesome Foundation production database and stored in a privileged Google Drive folder. Contact @divideby0 for access. + +### Local Data Setup +Data files live in `.scratch/data/` (gitignored). Expected files: +- `projects.csv` → `projects.parquet` → loaded into `awesomebits.duckdb` +- `chapters.csv` → `chapters.parquet` → loaded into `awesomebits.duckdb` +- Additional: `comments.csv`, sample sets, validation scores + +### DuckDB +```bash +duckdb .scratch/data/awesomebits.duckdb +``` + +Use for analytical queries on historical data. Never connect to production. + +## Signal Score Architecture + +### Scoring Pipeline +1. Application text → LLM batch API (Anthropic) +2. Structured JSON output with Trust Equation dimensions +3. Composite score 0.0–1.0 + feature breakdown + flags + +### Trust Equation +`T = (Credibility + Reliability + Intimacy) / (1 + Self-Interest)` + +### Key Files +- `scripts/signal-score/score_grants.rb` — batch scoring via Anthropic API +- `scripts/signal-score/import_data.rb` — CSV → Parquet → DuckDB +- `context/memory/` — research notes, analysis, pattern discovery + +## Context: Existing Spam Detection + +Two existing systems (bot detection only, not content analysis): +- `app/extras/spam_checker.rb` — blocklist + identical fields +- `app/extras/spam_classifier.rb` — behavioral JS metadata analysis + +Signal Score is complementary — it analyzes content, not form behavior. + +## Notifying Evie + +When you finish a task, get stuck, or need feedback: +```bash +openclaw agent --agent main --message "[CC: ] " --timeout 30 +``` + +Keep messages concise — they'll be read on a phone. diff --git a/context/memory/README.md b/context/memory/README.md new file mode 100644 index 00000000..f8933253 --- /dev/null +++ b/context/memory/README.md @@ -0,0 +1,13 @@ +# Signal Score — Knowledge Base + +This directory is a [basic-memory](https://github.com/basicmachines-co/basic-memory) project containing research notes, analysis reports, and pattern discovery for the Signal Score feature. + +## Structure + +``` +context/memory/ +├── README.md # This file +├── research/ # Pattern discovery, validation results +├── decisions/ # Architecture and design decisions +└── reference/ # AF handbook excerpts, scoring rubrics +``` diff --git a/context/memory/decisions/signal-score-architecture.md b/context/memory/decisions/signal-score-architecture.md new file mode 100644 index 00000000..186c82e0 --- /dev/null +++ b/context/memory/decisions/signal-score-architecture.md @@ -0,0 +1,33 @@ +# Signal Score Architecture Decisions + +## LLM-as-Judge over Embeddings +**Decision:** Use LLM direct evaluation, not embedding similarity. +**Rationale:** Embeddings add infrastructure for a problem that doesn't need it. LLMs can read and reason about full application text. Cost: ~$0.02/app with Haiku 4.5. + +## Trust Equation as Framework +**Decision:** Score using `T = (C + R + I) / (1 + S)` mapped to AF's own evaluation criteria. +**Rationale:** The Trusted Advisor framework maps directly to the Awesome Foundation's New Chapter Handbook criteria. Validated empirically with zero false negatives on 181 labeled apps. + +## Content Analysis, Not Bot Detection +**Decision:** Signal Score analyzes *what* was written. Existing SpamChecker/SpamClassifier handle *how* the form was filled out. +**Rationale:** Clean separation of concerns. No overlap. Signal Score runs async after submission. + +## Batch API for Scoring +**Decision:** Use Anthropic Batch API (not real-time). +**Rationale:** 50% cost reduction, no rate limit pressure, results within 24h. Good fit since scoring doesn't need to be instant. + +## Pattern Discovery Before Rubric +**Decision:** Use LLM as qualitative researcher first, then build rubric from discovered patterns. +**Rationale:** Avoids encoding assumptions. Patterns emerged from data, not preconceptions. + +## "Signal Score" Naming +**Decision:** Renamed from "Auto-Awesome Score" to "Signal Score." +**Rationale:** "Auto-Awesome" implies bypassing human review. Signal Score communicates that it surfaces signals for trustees to interpret. + +## Data Location +**Decision:** Historical data in `.scratch/data/` (gitignored). Source CSVs from privileged Google Drive folder. +**Rationale:** Data contains PII (applicant names, emails, project descriptions). Must not be committed to a public fork. + +## Secrets in .env.local +**Decision:** Use `.env.local` for personal API keys, not `.env`. +**Rationale:** `.env` could be checked in by accident. `.env.local` is the Rails/dotenv convention for personal secrets. `dotenv-rails` already in the Gemfile loads it automatically. diff --git a/context/memory/research/pattern-discovery-findings.md b/context/memory/research/pattern-discovery-findings.md new file mode 100644 index 00000000..7b34af89 --- /dev/null +++ b/context/memory/research/pattern-discovery-findings.md @@ -0,0 +1,37 @@ +# Pattern Discovery Findings + +## Method + +3 independent batches of 40 labeled Chicago applications analyzed via Claude Sonnet for qualitative patterns. Cross-batch synthesis to identify stable signals. + +## Stable Signals (consistent across all 3 batches) + +1. **Money destination is #1 discriminator** — Materials/supplies = funded. Self-payment/living expenses = hidden. If >50% budget goes to applicant → almost always hidden. + +2. **Local specificity signals authenticity** — Funded apps mention specific streets, organizations, aldermen. Hidden apps are geographically vague. + +3. **Writing quality barely matters** — Quirky, unpolished but authentic applications outperform polished corporate ones. + +4. **Scale fit is critical** — Projects perfectly sized for $1K succeed. "Drop in the bucket" requests get filtered. + +5. **Community connector density** — Funded apps average 3-5 named local partnerships. Hidden apps have 0-1. + +6. **Professional credentials hurt** — Academic degrees, awards, institutional affiliations correlate with HIDDEN status. + +## Additional Signals from Video Archive Analysis (39 videos, 4 analysis groups) + +### 4/4 Consensus (universal across all groups) +- **Catalytic Potential** — Does $1K unlock something bigger? +- **Creativity/Surprise** — Core AF identity, not a nice-to-have + +### 3/4 Consensus +- **Funding Orphan Score** — Too weird/small for traditional funders = more awesome +- **Agency Framing** — "I will build" vs. "please help us" + +### 2/4 Consensus +- **Boring Detection** — Not spam, just generic and uninspired +- **Validation Hunger** — First-time applicants where AF's belief matters most +- **Community Creation** — Builds new connections, not just serves existing + +## Calibration +Multiple chapters independently report ~28% of applications are review-worthy regardless of volume. diff --git a/context/memory/research/trust-equation-framework.md b/context/memory/research/trust-equation-framework.md new file mode 100644 index 00000000..e8251827 --- /dev/null +++ b/context/memory/research/trust-equation-framework.md @@ -0,0 +1,54 @@ +# Trust Equation Framework + +## Overview + +Signal Score uses Maister's Trust Equation as its scoring backbone: + +``` +T = (Credibility + Reliability + Intimacy) / (1 + Self-Interest) +``` + +Each dimension maps to observable signals in grant applications. + +## Dimension Mapping + +### Credibility (numerator) +- Clear budget with specific line items +- Realistic plan with timeline +- Relevant expertise or demonstrated competence +- Named vendors, partners, suppliers + +### Reliability (numerator) +- Track record: prior events, projects, grants +- Organizational backing or institutional support +- Evidence of follow-through (photos, links to past work) +- Repeat applicants with history + +### Intimacy (numerator) +- Connection to cause/community +- Local specificity: named neighborhoods, streets, aldermen +- Personal anecdotes and authentic voice +- Sub-city precision (ward, zip, not just city name) + +### Self-Interest (denominator — higher = worse) +- Money primarily benefits applicant (living expenses, tuition, salary) +- Business startup where applicant is primary beneficiary +- Self-payment > 50% of budget → almost always hidden +- Professional credential emphasis (awards, degrees) + +## Validation Results (Chicago Chapter, n=181) + +| Metric | Value | +|--------|-------| +| Funded avg score | 0.717 | +| Hidden avg score | 0.277 | +| Funded > 0.7 | 88% | +| Hidden < 0.3 | 65% | +| False negatives (funded < 0.3) | **0** | + +## Key Insight + +The Trust Equation maps directly to AF's own grantee evaluation criteria from the New Chapter Handbook: +1. Likelihood of short-timespan completion → Reliability + Specificity +2. Perceived ability of applicant → Credibility +3. Ability of grant to make a difference → Budget Alignment diff --git a/scripts/signal-score/import_data.rb b/scripts/signal-score/import_data.rb new file mode 100644 index 00000000..012080c3 --- /dev/null +++ b/scripts/signal-score/import_data.rb @@ -0,0 +1,119 @@ +#!/usr/bin/env ruby +# frozen_string_literal: true + +# Import CSV data into Parquet and DuckDB for Signal Score analysis. +# +# Prerequisites: +# - DuckDB CLI: `brew install duckdb` or `mise install duckdb` +# - CSV files in .scratch/data/ (see DATA SOURCES below) +# +# DATA SOURCES: +# Historical application data is exported from the Awesome Foundation +# production database and stored in a privileged Google Drive folder. +# Contact @divideby0 for access. +# +# Expected CSV files: +# - projects.csv (~64K rows) — all applications across all chapters +# - chapters.csv (~220 rows) — chapter metadata +# - comments.csv — trustee comments on applications +# +# Usage: +# ruby scripts/signal-score/import_data.rb +# ruby scripts/signal-score/import_data.rb --csv-only # Skip DuckDB +# ruby scripts/signal-score/import_data.rb --force # Overwrite existing + +require "fileutils" +require "open3" + +DATA_DIR = File.expand_path("../../.scratch/data", __dir__) +DB_PATH = File.join(DATA_DIR, "awesomebits.duckdb") + +TABLES = { + "projects" => "projects.csv", + "chapters" => "chapters.csv", +}.freeze + +def run(cmd) + puts " $ #{cmd}" + stdout, stderr, status = Open3.capture3(cmd) + unless status.success? + warn "ERROR: #{stderr}" + exit 1 + end + stdout +end + +def csv_to_parquet(csv_file, parquet_file, force: false) + if File.exist?(parquet_file) && !force + puts " ✓ #{File.basename(parquet_file)} exists (skip)" + return + end + + sql = <<~SQL + COPY (SELECT * FROM read_csv_auto('#{csv_file}')) + TO '#{parquet_file}' (FORMAT PARQUET); + SQL + run("duckdb -c \"#{sql}\"") + puts " ✓ #{File.basename(csv_file)} → #{File.basename(parquet_file)}" +end + +def load_to_duckdb(parquet_file, table_name, force: false) + sql = if force + "DROP TABLE IF EXISTS #{table_name}; CREATE TABLE #{table_name} AS SELECT * FROM read_parquet('#{parquet_file}');" + else + "CREATE TABLE IF NOT EXISTS #{table_name} AS SELECT * FROM read_parquet('#{parquet_file}');" + end + run("duckdb #{DB_PATH} -c \"#{sql}\"") + count = run("duckdb #{DB_PATH} -c \"SELECT count(*) FROM #{table_name};\"").strip.split("\n").last.strip + puts " ✓ #{table_name}: #{count} rows" +end + +def main + force = ARGV.include?("--force") + csv_only = ARGV.include?("--csv-only") + + puts "Signal Score Data Import" + puts "========================" + puts "Data dir: #{DATA_DIR}" + puts + + # Check for CSV files + missing = TABLES.values.reject { |f| File.exist?(File.join(DATA_DIR, f)) } + unless missing.empty? + warn "Missing CSV files in #{DATA_DIR}:" + missing.each { |f| warn " - #{f}" } + warn "\nCSV data must be downloaded from the privileged Google Drive folder." + warn "Contact @divideby0 for access." + exit 1 + end + + # CSV → Parquet + puts "Converting CSV → Parquet..." + TABLES.each do |table, csv| + csv_path = File.join(DATA_DIR, csv) + parquet_path = File.join(DATA_DIR, "#{table}.parquet") + csv_to_parquet(csv_path, parquet_path, force: force) + end + puts + + return if csv_only + + # Parquet → DuckDB + puts "Loading into DuckDB (#{DB_PATH})..." + TABLES.each do |table, _| + parquet_path = File.join(DATA_DIR, "#{table}.parquet") + load_to_duckdb(parquet_path, table, force: force) + end + + # Also load any additional parquet files + %w[chicago_validation sample_100].each do |extra| + parquet = File.join(DATA_DIR, "#{extra}.parquet") + next unless File.exist?(parquet) + load_to_duckdb(parquet, extra, force: force) + end + + puts + puts "Done. Query with: duckdb #{DB_PATH}" +end + +main From 5cda7072a7e33b597c2d38f8d7d7ff82c9484569 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 14:38:45 -0600 Subject: [PATCH 02/25] chore(infra): add .env.example Non-secret configuration defaults for local dev. Personal secrets go in .env.local (see .env.local.example). Refs: #591 --- .env.example | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 .env.example diff --git a/.env.example b/.env.example new file mode 100644 index 00000000..c7d6eb9e --- /dev/null +++ b/.env.example @@ -0,0 +1,11 @@ +# Non-secret configuration defaults +# Copy to .env and override as needed + +# Database (defaults work for local dev) +# DB_NAME=awesomefoundation_development +# POSTGRES_HOST=localhost +# POSTGRES_USER= +# POSTGRES_PASSWORD= + +# Spam detection +# SPAM_REGEXP= From b6f21cd032ed6241cc77291d8287598a1c60626c Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 14:40:12 -0600 Subject: [PATCH 03/25] docs(infra): align env examples with README README documents .env as standard location for secrets (AWS keys, etc). Add note that .env.local takes precedence for personal API keys. Refs: #591 --- .env.example | 13 +++++++++++-- .env.local.example | 11 +++++++++-- 2 files changed, 20 insertions(+), 4 deletions(-) diff --git a/.env.example b/.env.example index c7d6eb9e..82c7fd1a 100644 --- a/.env.example +++ b/.env.example @@ -1,5 +1,14 @@ -# Non-secret configuration defaults -# Copy to .env and override as needed +# Environment variables for Awesomebits +# Copy to .env for local development (gitignored) +# +# The README documents .env as the standard location. +# For extra safety, dotenv-rails also loads .env.local +# (which takes precedence) — use that for personal API keys. + +# AWS (S3 uploads) +# AWS_ACCESS_KEY_ID=XXX +# AWS_SECRET_ACCESS_KEY=YYY +# AWS_BUCKET=your-bucket-name # Database (defaults work for local dev) # DB_NAME=awesomefoundation_development diff --git a/.env.local.example b/.env.local.example index e8702016..1a368f89 100644 --- a/.env.local.example +++ b/.env.local.example @@ -1,2 +1,9 @@ -# Copy to .env.local and fill in your keys -ANTHROPIC_API_KEY=sk-ant-... +# Personal API keys — takes precedence over .env +# Copy to .env.local and fill in your keys (gitignored) +# +# dotenv-rails loads .env.local after .env, so values here +# override anything in .env. Use this for secrets you don't +# want to accidentally commit. + +# Signal Score (LLM scoring via Anthropic Batch API) +# ANTHROPIC_API_KEY=sk-ant-... From 94e0072009e8e6af30a64535ad74efb210583b41 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 15:07:36 -0600 Subject: [PATCH 04/25] feat(signal-score): ruby scoring pipeline PreScorer for deterministic text features. ScoreGrants for Anthropic batch API with tool_use structured output. Supports haiku/sonnet/opus. Refs: #591 --- scripts/signal-score/pre_scorer.rb | 89 +++++ scripts/signal-score/score_grants.rb | 480 +++++++++++++++++++++++++++ 2 files changed, 569 insertions(+) create mode 100644 scripts/signal-score/pre_scorer.rb create mode 100644 scripts/signal-score/score_grants.rb diff --git a/scripts/signal-score/pre_scorer.rb b/scripts/signal-score/pre_scorer.rb new file mode 100644 index 00000000..e5b02409 --- /dev/null +++ b/scripts/signal-score/pre_scorer.rb @@ -0,0 +1,89 @@ +# frozen_string_literal: true + +# PreScorer — deterministic text features computed before LLM scoring. +# Zero cost, runs locally on application text fields. + +class PreScorer + FIELDS = %w[about_me about_project use_for_money].freeze + + attr_reader :features + + def initialize(project) + @project = project + @features = {} + end + + def analyze! + @features = {} + + # Word counts per field + FIELDS.each do |field| + text = field_text(field) + @features["word_count_#{field}"] = word_count(text) + end + @features["word_count_total"] = FIELDS.sum { |f| @features["word_count_#{f}"] } + + # Field length variance (low = suspicious uniformity, AI template signal) + counts = FIELDS.map { |f| @features["word_count_#{f}"].to_f } + @features["field_length_variance"] = variance(counts).round(2) + + # Sentence-level stats (AI tends toward uniform sentence length) + all_text = combined_text + sentences = all_text.split(/[.!?]+/).map(&:strip).reject(&:empty?) + lengths = sentences.map { |s| s.split(/\s+/).length } + @features["sentence_count"] = sentences.length + @features["avg_sentence_length"] = lengths.empty? ? 0.0 : (lengths.sum.to_f / lengths.length).round(1) + @features["sentence_length_variance"] = variance(lengths.map(&:to_f)).round(2) + + # Punctuation signals + @features["exclamation_count"] = all_text.count("!") + @features["question_mark_count"] = all_text.count("?") + + # Content signals + @features["url_count"] = all_text.scan(%r{https?://\S+}).length + @features["dollar_sign_count"] = all_text.count("$") + @features["number_count"] = all_text.scan(/\b\d[\d,.]*\b/).length + @features["email_count"] = all_text.scan(/\S+@\S+\.\S+/).length + + # Capitalized words (proxy for proper nouns / named entities) + words = all_text.split(/\s+/) + # Skip first word of each sentence + mid_sentence_caps = words.each_cons(2).count { |prev, word| + !prev.match?(/[.!?]\z/) && word.match?(/\A[A-Z][a-z]/) + } + @features["capitalized_word_count"] = mid_sentence_caps + @features["capitalized_word_density"] = if words.length > 10 + (mid_sentence_caps.to_f / words.length * 100).round(1) + else + 0.0 + end + + # Empty field detection + @features["empty_field_count"] = FIELDS.count { |f| field_text(f).strip.empty? } + + # Has images (title/about fields mention image/photo/picture URLs) + @features["has_image_reference"] = all_text.match?(/\.(jpg|jpeg|png|gif|webp|bmp)/i) ? 1 : 0 + + @features + end + + private + + def field_text(name) + @project[name].to_s + end + + def combined_text + FIELDS.map { |f| field_text(f).strip }.reject(&:empty?).join(" ") + end + + def word_count(text) + text.strip.empty? ? 0 : text.strip.split(/\s+/).length + end + + def variance(values) + return 0.0 if values.length < 2 + mean = values.sum / values.length.to_f + values.sum { |v| (v - mean)**2 } / values.length.to_f + end +end diff --git a/scripts/signal-score/score_grants.rb b/scripts/signal-score/score_grants.rb new file mode 100644 index 00000000..d123912d --- /dev/null +++ b/scripts/signal-score/score_grants.rb @@ -0,0 +1,480 @@ +#!/usr/bin/env ruby +# frozen_string_literal: true + +# score_grants.rb — Signal Score batch scoring via Anthropic API +# +# Usage: +# ruby scripts/signal-score/score_grants.rb build-sample --count 400 +# ruby scripts/signal-score/score_grants.rb build-batch [--model claude-haiku-4-5-20251001] +# ruby scripts/signal-score/score_grants.rb submit --input .scratch/data/batch-haiku.jsonl +# ruby scripts/signal-score/score_grants.rb results --batch-id +# ruby scripts/signal-score/score_grants.rb pre-score # Run PreScorer on sample +# +# Requires: ANTHROPIC_API_KEY in .env.local + +require "json" +require "net/http" +require "uri" +require "fileutils" +require_relative "pre_scorer" + +DATA_DIR = File.expand_path("../../.scratch/data", __dir__) +DB_PATH = File.join(DATA_DIR, "awesomebits.duckdb") + +MODELS = { + "haiku" => "claude-haiku-4-5-20251001", + "sonnet" => "claude-sonnet-4-20250514", + "opus" => "claude-opus-4-0-20250514", +}.freeze + +# --- DuckDB helper --- + +def duckdb(sql) + result = `duckdb #{DB_PATH} -json -c #{Shellwords.escape(sql)} 2>&1` + raise "DuckDB error: #{result}" unless $?.success? + JSON.parse(result.empty? ? "[]" : result) +end + +# --- Tool schema for structured output --- + +SCORE_TOOL = { + name: "score_application", + description: "Score a grant application and extract structured features", + input_schema: { + type: "object", + required: %w[composite_score reason flags features], + properties: { + composite_score: { + type: "number", + description: "Overall score 0.0-1.0" + }, + reason: { + type: "string", + description: "One-sentence explanation of the score" + }, + flags: { + type: "array", + items: { type: "string" }, + description: "Applicable flags: spam, ai_spam, duplicate, incomplete, wrong_location, business_pitch, personal_fundraising, low_effort" + }, + features: { + type: "object", + required: %w[ + credibility reliability intimacy self_interest + specificity creativity budget_alignment catalytic_potential + community_benefit personal_voice + ai_spam_likelihood ai_writing_likelihood + ], + properties: { + credibility: { type: "number" }, + reliability: { type: "number" }, + intimacy: { type: "number" }, + self_interest: { type: "number" }, + specificity: { type: "number" }, + creativity: { type: "number" }, + budget_alignment: { type: "number" }, + catalytic_potential: { type: "number" }, + community_benefit: { type: "number" }, + personal_voice: { type: "number" }, + ai_spam_likelihood: { type: "number" }, + ai_writing_likelihood: { type: "number" }, + } + } + } + } +}.freeze + +SYSTEM_PROMPT = <<~PROMPT + You are an expert grant application screener for the Awesome Foundation. + + The Awesome Foundation is a global network of volunteer "micro-trustees" who each chip in to award $1,000 grants for awesome projects. No strings attached — the money goes to creative, community-benefiting, unique ideas. + + Score each application using the score_application tool. Extract structured features to help trustees prioritize their review. + + ## Scoring Rubric (composite_score: 0.0 to 1.0) + + - 0.0–0.1: Clear spam, gibberish, test submissions, or AI-generated mass submissions + - 0.1–0.3: Real but very weak — business pitches, personal fundraising, vague ideas, wrong location + - 0.3–0.5: Borderline — decent concept but missing details, unclear community benefit, generic + - 0.5–0.7: Solid — clear project, community benefit, actionable plan, reasonable for $1,000 + - 0.7–0.9: Strong — creative, specific, well-articulated, exactly what AF funds + - 0.9–1.0: Exceptional — innovative, clearly impactful, inspiring, would excite any trustee + + ## Feature Dimensions + + ### Trust Equation: T = (Credibility + Reliability + Intimacy) / (1 + Self-Interest) + + **Numerator (higher = better):** + - credibility: Clear budget, realistic plan, relevant expertise. (0=vague, 1=detailed budget + clear execution) + - reliability: Track record, prior work, organizational backing. (0=no evidence, 1=strong track record) + - intimacy: Connection to community, local ties, authentic voice. Named neighborhoods, specific orgs, personal anecdotes. Sub-city precision (ward/zip) scores higher than city-level. (0=generic, 1=deeply embedded) + + **Denominator (higher = worse):** + - self_interest: Money primarily benefits applicant? Living expenses, tuition, business startup, self-payment >50% of budget. (0=community-benefiting, 1=self-serving) + + ### Additional Signals + + - specificity: Concrete details — costs, addresses, timelines, partner names. (0=vague, 1=highly specific) + - creativity: How original, unique, fun, or surprising? "The opposite of whimsy is boring, not serious." (0=generic, 1=delightfully novel) + - budget_alignment: Can $1K meaningfully complete this? Is it a "drop in the bucket" or perfectly sized? (0=wildly mismatched, 1=perfectly scoped) + - catalytic_potential: Does $1K unlock something bigger — prototype, proof-of-concept, career catalyst? "That money was just a cheap way to start a relationship." (0=standalone purchase, 1=unlocks outsized outcomes) + - community_benefit: Clear benefit beyond the applicant? Creating new connections scores higher than serving existing community. (0=purely personal, 1=broad community impact + new connections) + - personal_voice: Does it sound like a real person? Quirky details, informal language, personal anecdotes = POSITIVE. Over-polished corporate language = NEGATIVE. Brevity is fine — a 3-sentence authentic app beats a 3-paragraph polished one. (0=robotic/templated, 1=authentic and personal) + + ### AI Detection (two separate signals) + + - ai_spam_likelihood: Mass-generated generic proposal — no personal details, no local knowledge, could apply to any grant program unchanged. NOT the same as someone using AI to write about a genuine project. (0=clearly genuine, 1=almost certainly mass-generated) + - ai_writing_likelihood: Does the writing exhibit AI patterns? Uniform sentence structure, significance inflation ("pivotal", "groundbreaking"), superficial -ing analyses ("highlighting...", "showcasing..."), promotional language ("vibrant", "nestled", "in the heart of"). This is INFORMATIONAL ONLY — do not factor into composite_score. A genuine project with AI-polished writing is still fundable. (0=clearly human-written, 1=heavily AI-styled) + + ## Flags (include any that apply) + spam, ai_spam, duplicate, incomplete, wrong_location, business_pitch, personal_fundraising, low_effort + + ## Key Principles + - AF values creativity, community impact, and fun. Weird/quirky is great. + - $1,000 is small. Projects should be scoped appropriately. + - Professional credentials (degrees, awards, institutions) are neutral-to-negative — AF funds people, not organizations. + - Projects "too weird for traditional funders" = MORE awesome, not less. + - ~28% of applications are typically review-worthy. Most are generic but not spam. +PROMPT + +# --- Format helpers --- + +def format_application(project) + parts = ["Title: #{project['title'] || '(empty)'}"] + parts << "Chapter: #{project['chapter_name']}" if project["chapter_name"] + parts << "About Me: #{project['about_me']}" if project["about_me"].to_s.strip != "" + parts << "About Project: #{project['about_project']}" if project["about_project"].to_s.strip != "" + parts << "Use for Money: #{project['use_for_money']}" if project["use_for_money"].to_s.strip != "" + parts.join("\n") +end + +def build_few_shot_examples + labeled = duckdb(<<~SQL) + SELECT cv.*, c.name as chapter_name + FROM chicago_validation cv + LEFT JOIN chapters c ON cv.chapter_id = c.id + ORDER BY actual_label, hidden_reason + SQL + + funded = labeled.select { |p| p["actual_label"] == "funded" }.first(15) + hidden = labeled.select { |p| p["actual_label"] == "hidden" }.first(15) + + text = "## EXAMPLES OF FUNDED (WINNING) APPLICATIONS\n\n" + funded.each { |p| text += "### Project #{p['id']} — FUNDED\n#{format_application(p)}\n\n" } + + text += "## EXAMPLES OF HIDDEN (REJECTED/FILTERED) APPLICATIONS\n\n" + hidden.each do |p| + reason = p["hidden_reason"] ? " (reason: #{p['hidden_reason']})" : "" + text += "### Project #{p['id']} — HIDDEN#{reason}\n#{format_application(p)}\n\n" + end + + text +end + +# --- Commands --- + +def cmd_build_sample(count:) + puts "Building stratified sample of #{count} Chicago apps..." + + labeled = duckdb("SELECT count(*) as n FROM chicago_validation")[0]["n"] + unlabeled_needed = count - labeled + puts " #{labeled} labeled + #{unlabeled_needed} unlabeled needed" + + # Stratified by year + years = duckdb(<<~SQL) + SELECT extract(year from p.created_at::date) as yr, count(*) as n + FROM projects p JOIN chapters c ON p.chapter_id = c.id + WHERE c.name = 'Chicago, IL' + AND p.funded_on IS NULL AND p.hidden_at IS NULL + GROUP BY 1 ORDER BY 1 + SQL + + total_unlabeled = years.sum { |y| y["n"] } + sample_ids = [] + + years.each do |y| + yr_count = [(y["n"].to_f / total_unlabeled * unlabeled_needed).round, y["n"]].min + next if yr_count == 0 + + ids = duckdb(<<~SQL) + SELECT p.id FROM projects p JOIN chapters c ON p.chapter_id = c.id + WHERE c.name = 'Chicago, IL' + AND p.funded_on IS NULL AND p.hidden_at IS NULL + AND extract(year from p.created_at::date) = #{y['yr']} + ORDER BY random() + LIMIT #{yr_count} + SQL + sample_ids += ids.map { |r| r["id"] } + puts " #{y['yr']}: #{yr_count}/#{y['n']} unlabeled" + end + + # Build the full sample: labeled + unlabeled + sql = <<~SQL + CREATE OR REPLACE TABLE sample_400 AS + SELECT p.*, c.name as chapter_name, + CASE + WHEN p.funded_on IS NOT NULL THEN 'funded' + WHEN p.hidden_at IS NOT NULL THEN 'hidden' + ELSE 'unlabeled' + END as actual_label + FROM projects p + LEFT JOIN chapters c ON p.chapter_id = c.id + WHERE p.id IN ( + SELECT id FROM chicago_validation + UNION ALL + SELECT unnest([#{sample_ids.join(",")}]) + ) + SQL + duckdb(sql) + + result = duckdb("SELECT actual_label, count(*) as n FROM sample_400 GROUP BY 1 ORDER BY 1") + result.each { |r| puts " #{r['actual_label']}: #{r['n']}" } + + total = duckdb("SELECT count(*) as n FROM sample_400")[0]["n"] + puts "\nSample: #{total} apps saved to sample_400 table" + + # Export to parquet + duckdb("COPY sample_400 TO '#{DATA_DIR}/sample_400.parquet' (FORMAT PARQUET)") + puts "Exported to #{DATA_DIR}/sample_400.parquet" +end + +def cmd_pre_score + puts "Running PreScorer on sample_400..." + + projects = duckdb("SELECT * FROM sample_400") + results = {} + + projects.each do |p| + scorer = PreScorer.new(p) + scorer.analyze! + results[p["id"]] = scorer.features + end + + output = File.join(DATA_DIR, "pre_scores.json") + File.write(output, JSON.pretty_generate(results)) + puts "Wrote #{results.length} pre-scores to #{output}" + + # Summary stats + totals = results.values.map { |f| f["word_count_total"] } + puts "\nWord count stats:" + puts " Min: #{totals.min}, Max: #{totals.max}, Avg: #{(totals.sum.to_f / totals.length).round(0)}" + puts " Median: #{totals.sort[totals.length / 2]}" + + empty = results.values.count { |f| f["empty_field_count"] > 0 } + puts " Apps with empty fields: #{empty}/#{results.length}" +end + +def cmd_build_batch(model_alias:) + model = MODELS[model_alias] || model_alias + puts "Building batch for model: #{model} (#{model_alias})" + + examples_text = build_few_shot_examples + projects = duckdb("SELECT * FROM sample_400") + puts " #{projects.length} projects to score" + + lines = projects.map do |p| + { + custom_id: "project-#{p['id']}", + params: { + model: model, + max_tokens: 1024, + system: SYSTEM_PROMPT, + tools: [SCORE_TOOL], + tool_choice: { type: "tool", name: "score_application" }, + messages: [ + { + role: "user", + content: "Here are examples of previously funded and hidden applications:\n\n#{examples_text}\n\n---\n\nNow score this application:\n\n#{format_application(p)}" + } + ] + } + }.to_json + end + + output = File.join(DATA_DIR, "batch-#{model_alias}.jsonl") + File.write(output, lines.join("\n") + "\n") + puts "Wrote #{lines.length} requests to #{output}" + + # Cost estimate + total_chars = lines.sum(&:length) + input_tokens = total_chars / 4 + case model_alias + when "haiku" + cost = (input_tokens / 1_000_000.0 * 0.80 + projects.length * 200 / 1_000_000.0 * 4.0) * 0.5 + when "sonnet" + cost = (input_tokens / 1_000_000.0 * 3.0 + projects.length * 200 / 1_000_000.0 * 15.0) * 0.5 + when "opus" + cost = (input_tokens / 1_000_000.0 * 15.0 + projects.length * 200 / 1_000_000.0 * 75.0) * 0.5 + end + puts "Estimated batch cost (50% discount): ~$#{'%.2f' % cost}" if cost +end + +def cmd_submit(input:) + api_key = ENV["ANTHROPIC_API_KEY"] + raise "ANTHROPIC_API_KEY not set — add to .env.local" unless api_key + + requests = File.readlines(input).map { |l| JSON.parse(l) } + puts "Submitting #{requests.length} requests to Anthropic Batch API..." + + uri = URI("https://api.anthropic.com/v1/messages/batches") + http = Net::HTTP.new(uri.host, uri.port) + http.use_ssl = true + + req = Net::HTTP::Post.new(uri) + req["x-api-key"] = api_key + req["anthropic-version"] = "2023-06-01" + req["anthropic-beta"] = "message-batches-2024-09-24" + req["content-type"] = "application/json" + req.body = { requests: requests }.to_json + + resp = http.request(req) + unless resp.is_a?(Net::HTTPSuccess) + warn "Error #{resp.code}: #{resp.body}" + exit 1 + end + + result = JSON.parse(resp.body) + puts "Batch created: #{result['id']}" + puts "Status: #{result['processing_status']}" + + # Save batch ID + id_file = input.sub(".jsonl", "-batch-id.txt") + File.write(id_file, result["id"] + "\n") + puts "Batch ID saved to #{id_file}" + result["id"] +end + +def cmd_results(batch_id:, output: nil) + api_key = ENV["ANTHROPIC_API_KEY"] + raise "ANTHROPIC_API_KEY not set" unless api_key + + headers = { + "x-api-key" => api_key, + "anthropic-version" => "2023-06-01", + "anthropic-beta" => "message-batches-2024-09-24", + } + + # Check status + uri = URI("https://api.anthropic.com/v1/messages/batches/#{batch_id}") + http = Net::HTTP.new(uri.host, uri.port) + http.use_ssl = true + req = Net::HTTP::Get.new(uri, headers) + resp = http.request(req) + status = JSON.parse(resp.body) + + puts "Status: #{status['processing_status']}" + counts = status["request_counts"] || {} + puts "Progress: #{counts['succeeded']}/#{counts['processing']} succeeded" + + unless status["processing_status"] == "ended" + puts "Batch still processing. Try again later." + return + end + + # Fetch results + uri = URI("https://api.anthropic.com/v1/messages/batches/#{batch_id}/results") + req = Net::HTTP::Get.new(uri, headers) + resp = http.request(req) + + scores = {} + resp.body.strip.split("\n").each do |line| + item = JSON.parse(line) + project_id = item["custom_id"].sub("project-", "").to_i + begin + # With tool_use, result is in tool call input + tool_call = item.dig("result", "message", "content")&.find { |c| c["type"] == "tool_use" } + if tool_call + parsed = tool_call["input"] + else + # Fallback: try text content + text = item.dig("result", "message", "content", 0, "text") || "" + json_match = text.match(/\{[\s\S]*\}/) + parsed = json_match ? JSON.parse(json_match[0]) : nil + end + scores[project_id] = parsed.merge("project_id" => project_id) if parsed + rescue => e + warn "Failed to parse result for project-#{project_id}: #{e.message}" + end + end + + output ||= File.join(DATA_DIR, "scores-#{batch_id[0..12]}.json") + File.write(output, JSON.pretty_generate(scores)) + puts "\nWrote #{scores.length} scores to #{output}" + + # Summary + score_values = scores.values.map { |s| s["composite_score"] }.compact + avg = score_values.sum / score_values.length.to_f + low = score_values.count { |s| s <= 0.3 } + mid = score_values.count { |s| s > 0.3 && s <= 0.7 } + high = score_values.count { |s| s > 0.7 } + puts "\nScore distribution:" + puts " Low (≤0.3): #{low} apps" + puts " Mid (0.3-0.7): #{mid} apps" + puts " High (>0.7): #{high} apps" + puts " Average: #{'%.3f' % avg}" +end + +# --- CLI --- + +require "shellwords" + +command = ARGV.shift + +# Load .env.local if present +env_file = File.expand_path("../../.env.local", __dir__) +if File.exist?(env_file) + File.readlines(env_file, encoding: "UTF-8").each do |line| + line = line.strip + next if line.empty? || line.start_with?("#") + key, value = line.split("=", 2) + ENV[key] = value if key && value + end +end + +case command +when "build-sample" + i = ARGV.index("--count") + count = i ? ARGV[i + 1].to_i : 400 + cmd_build_sample(count: count) + +when "pre-score" + cmd_pre_score + +when "build-batch" + i = ARGV.index("--model") + model = i ? ARGV[i + 1] : "haiku" + cmd_build_batch(model_alias: model) + +when "submit" + i = ARGV.index("--input") + input = i ? ARGV[i + 1] : nil + raise "--input required" unless input + cmd_submit(input: input) + +when "results" + i = ARGV.index("--batch-id") + batch_id = i ? ARGV[i + 1] : nil + raise "--batch-id required" unless batch_id + cmd_results(batch_id: batch_id) + +when "submit-all" + %w[haiku sonnet opus].each do |m| + input = File.join(DATA_DIR, "batch-#{m}.jsonl") + next unless File.exist?(input) + puts "\n=== Submitting #{m} ===" + cmd_submit(input: input) + end + +else + puts <<~USAGE + Usage: ruby scripts/signal-score/score_grants.rb + + Commands: + build-sample Build stratified 400-app sample in DuckDB + pre-score Run PreScorer on sample (deterministic features) + build-batch Build batch JSONL [--model haiku|sonnet|opus] + submit Submit batch [--input path.jsonl] + submit-all Submit all built batches (haiku, sonnet, opus) + results Fetch results [--batch-id ] + USAGE +end From b1811b5a8e89203e77a1366ed8abbb124538205b Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 16:05:58 -0600 Subject: [PATCH 05/25] feat(signal-score): add analysis notebooks and pre-scorer features - Add notebooks/01_eda.ipynb: corpus stats, chapter distributions, label rates, text field analysis - Add notebooks/02_feature_engineering.ipynb: pre-scorer features, TF-IDF analysis, money extraction, feature correlations - Add notebooks/03_model_evaluation.ipynb: batch results, model comparison, test-retest reliability, cross-chapter analysis - Add notebooks/helpers.py: shared data source, plotting, money extraction, label helpers - Add international money/currency extraction to pre_scorer.rb (supports symbols, prefixed symbols, ISO codes, written forms) - Add TF-IDF feature support to pre_scorer.rb (static IDF table computed from notebooks, loaded at runtime) - Replace dollar_sign_count with money_mention_count - All notebooks parameterized via AWESOMEBITS_DB env var, no real grant data in git-tracked files Refs: #591 --- notebooks/01_eda.ipynb | 276 +++++++++++++++++ notebooks/02_feature_engineering.ipynb | 328 ++++++++++++++++++++ notebooks/03_model_evaluation.ipynb | 404 +++++++++++++++++++++++++ notebooks/README.md | 38 +++ notebooks/helpers.py | 115 +++++++ notebooks/requirements.txt | 9 + scripts/signal-score/pre_scorer.rb | 64 +++- 7 files changed, 1233 insertions(+), 1 deletion(-) create mode 100644 notebooks/01_eda.ipynb create mode 100644 notebooks/02_feature_engineering.ipynb create mode 100644 notebooks/03_model_evaluation.ipynb create mode 100644 notebooks/README.md create mode 100644 notebooks/helpers.py create mode 100644 notebooks/requirements.txt diff --git a/notebooks/01_eda.ipynb b/notebooks/01_eda.ipynb new file mode 100644 index 00000000..ecb26abd --- /dev/null +++ b/notebooks/01_eda.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "39074e61", + "metadata": {}, + "source": [ + "# 01 - Exploratory Data Analysis\n", + "## Awesome Foundation Signal Score\n", + "\n", + "Corpus-level statistics, chapter distributions, label rates, and text field analysis\n", + "across 64,000+ grant applications.\n", + "\n", + "**Data source:** Parameterized via `AWESOMEBITS_DB` env var (defaults to `.scratch/data/awesomebits.duckdb`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76102697", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '.')\n", + "from helpers import *\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "setup_plotting()\n", + "con = connect()\n", + "print(f'Connected to: {DB_PATH}')\n", + "for t in con.execute('SHOW TABLES').fetchall():\n", + " cnt = con.execute(f'SELECT count(*) FROM {t[0]}').fetchone()[0]\n", + " print(f' {t[0]}: {cnt:,} rows')" + ] + }, + { + "cell_type": "markdown", + "id": "409c3c9f", + "metadata": {}, + "source": [ + "## Corpus Overview" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2485e15", + "metadata": {}, + "outputs": [], + "source": [ + "df = con.execute('SELECT * FROM projects').df()\n", + "print(f'Total applications: {len(df):,}')\n", + "print(f'Date range: {df.created_at.min()} to {df.created_at.max()}')\n", + "print(f'Funded: {df.funded_on.notna().sum():,} ({df.funded_on.notna().mean():.1%})')\n", + "print(f'Hidden: {df.hidden_at.notna().sum():,} ({df.hidden_at.notna().mean():.1%})')\n", + "print(f'Unlabeled: {(df.funded_on.isna() & df.hidden_at.isna()).sum():,}')" + ] + }, + { + "cell_type": "markdown", + "id": "1d6b69eb", + "metadata": {}, + "source": [ + "## Chapter Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c72c31d", + "metadata": {}, + "outputs": [], + "source": [ + "chapters = con.execute('''\n", + " SELECT c.name, c.country, c.inactive_at IS NOT NULL as inactive,\n", + " COUNT(*) as total,\n", + " COUNT(p.funded_on) as funded,\n", + " COUNT(p.hidden_at) as hidden,\n", + " COUNT(*) - COUNT(p.funded_on) - COUNT(p.hidden_at) as unlabeled\n", + " FROM projects p\n", + " JOIN chapters c ON p.chapter_id = c.id\n", + " GROUP BY c.name, c.country, inactive\n", + " ORDER BY total DESC\n", + "''').df()\n", + "\n", + "print(f'Active chapters: {(~chapters.inactive).sum()}')\n", + "print(f'Inactive chapters: {chapters.inactive.sum()}')\n", + "print(f'Countries: {chapters.country.nunique()}')\n", + "print()\n", + "print('Top 15 chapters by volume:')\n", + "print(chapters.head(15)[['name', 'country', 'total', 'funded', 'hidden']].to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "008624c8", + "metadata": {}, + "outputs": [], + "source": [ + "# Applications per chapter (top 20)\n", + "top20 = chapters.head(20).copy()\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "ax.barh(top20['name'], top20['total'], color='steelblue')\n", + "ax.set_xlabel('Applications')\n", + "ax.set_title('Top 20 Chapters by Application Volume')\n", + "ax.invert_yaxis()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6060cfe5", + "metadata": {}, + "source": [ + "## Label Rates by Chapter\n", + "\n", + "Which chapters actively use the hide feature? This determines which chapters have enough ground truth for model validation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc15043f", + "metadata": {}, + "outputs": [], + "source": [ + "# Chapters with enough labeled data for validation\n", + "validatable = chapters[(chapters.funded >= 10) & (chapters.hidden >= 10)].copy()\n", + "validatable['label_rate'] = ((validatable.funded + validatable.hidden) / validatable.total * 100).round(1)\n", + "validatable['fund_rate'] = (validatable.funded / (validatable.funded + validatable.hidden) * 100).round(1)\n", + "\n", + "print(f'Chapters with >= 10 funded AND >= 10 hidden: {len(validatable)}')\n", + "print()\n", + "print(validatable[['name', 'country', 'total', 'funded', 'hidden', 'label_rate', 'fund_rate']].to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "bdacd01d", + "metadata": {}, + "source": [ + "## Temporal Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6a655908", + "metadata": {}, + "outputs": [], + "source": [ + "df['year'] = pd.to_datetime(df.created_at).dt.year\n", + "yearly = df.groupby('year').agg(\n", + " total=('id', 'count'),\n", + " funded=('funded_on', lambda x: x.notna().sum()),\n", + " hidden=('hidden_at', lambda x: x.notna().sum()),\n", + ").reset_index()\n", + "yearly['unlabeled'] = yearly.total - yearly.funded - yearly.hidden\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "ax.bar(yearly.year, yearly.unlabeled, label='Unlabeled', color='lightgray')\n", + "ax.bar(yearly.year, yearly.hidden, bottom=yearly.unlabeled, label='Hidden', color='salmon')\n", + "ax.bar(yearly.year, yearly.funded, bottom=yearly.unlabeled + yearly.hidden, label='Funded', color='mediumseagreen')\n", + "ax.set_xlabel('Year')\n", + "ax.set_ylabel('Applications')\n", + "ax.set_title('Applications by Year and Label')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "0619e796", + "metadata": {}, + "source": [ + "## Text Field Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a98c24eb", + "metadata": {}, + "outputs": [], + "source": [ + "# Word counts per field\n", + "for field in TEXT_FIELDS:\n", + " df[f'wc_{field}'] = df[field].fillna('').apply(word_count)\n", + "df['wc_total'] = df['wc_about_me'] + df['wc_about_project'] + df['wc_use_for_money']\n", + "\n", + "print('Word count statistics:')\n", + "print(df[['wc_about_me', 'wc_about_project', 'wc_use_for_money', 'wc_total']].describe().round(1))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48d91771", + "metadata": {}, + "outputs": [], + "source": [ + "# Word count distributions by label\n", + "df['label'] = df.apply(label_project, axis=1)\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(15, 5), sharey=True)\n", + "for ax, field in zip(axes, TEXT_FIELDS):\n", + " for label, color in [('funded', 'mediumseagreen'), ('hidden', 'salmon')]:\n", + " subset = df[df.label == label][f'wc_{field}']\n", + " ax.hist(subset, bins=50, alpha=0.6, color=color, label=label, range=(0, 1000))\n", + " ax.set_title(field.replace('_', ' ').title())\n", + " ax.set_xlabel('Word Count')\n", + " ax.legend()\n", + "axes[0].set_ylabel('Frequency')\n", + "plt.suptitle('Word Count Distribution: Funded vs Hidden', y=1.02)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1352067", + "metadata": {}, + "outputs": [], + "source": [ + "# Empty field rates by label\n", + "for label in ['funded', 'hidden', 'unlabeled']:\n", + " subset = df[df.label == label]\n", + " empties = {f: (subset[f].fillna('').str.strip() == '').mean() for f in TEXT_FIELDS}\n", + " print(f'{label:>10}: ' + ' '.join(f'{f}={v:.1%}' for f, v in empties.items()))" + ] + }, + { + "cell_type": "markdown", + "id": "0585f18f", + "metadata": {}, + "source": [ + "## Geographic Distribution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdd7d773", + "metadata": {}, + "outputs": [], + "source": [ + "country_stats = con.execute('''\n", + " SELECT c.country, COUNT(*) as apps, COUNT(DISTINCT c.id) as chapters\n", + " FROM projects p JOIN chapters c ON p.chapter_id = c.id\n", + " GROUP BY c.country ORDER BY apps DESC\n", + "''').df()\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 6))\n", + "top_countries = country_stats.head(15)\n", + "ax.barh(top_countries.country, top_countries.apps, color='steelblue')\n", + "ax.set_xlabel('Applications')\n", + "ax.set_title('Applications by Country (Top 15)')\n", + "ax.invert_yaxis()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(f'\\nTotal countries: {len(country_stats)}')\n", + "print(f'English-speaking: {country_stats[country_stats.country.isin(ENGLISH_SPEAKING_COUNTRIES)].apps.sum():,} apps')" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/02_feature_engineering.ipynb b/notebooks/02_feature_engineering.ipynb new file mode 100644 index 00000000..2d74e0b4 --- /dev/null +++ b/notebooks/02_feature_engineering.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e1f3c814", + "metadata": {}, + "source": [ + "# 02 - Feature Engineering\n", + "## Pre-Scorer Features, TF-IDF, and Money Extraction\n", + "\n", + "Deterministic features computed before LLM scoring. Zero cost, runs locally.\n", + "\n", + "**Data source:** Parameterized via `AWESOMEBITS_DB` env var." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb527170", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.insert(0, '.')\n", + "from helpers import *\n", + "import pandas as pd\n", + "import numpy as np\n", + "from collections import Counter\n", + "from sklearn.feature_extraction.text import TfidfVectorizer\n", + "from price_parser import Price\n", + "import re\n", + "\n", + "setup_plotting()\n", + "con = connect()\n", + "df = con.execute('SELECT * FROM projects').df()\n", + "df['label'] = df.apply(label_project, axis=1)\n", + "df['text'] = df.apply(combined_text, axis=1)\n", + "print(f'Loaded {len(df):,} applications')" + ] + }, + { + "cell_type": "markdown", + "id": "4def555a", + "metadata": {}, + "source": [ + "## Pre-Scorer Feature Replication\n", + "\n", + "Replicate the Ruby `PreScorer` features in Python for analysis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e59deda9", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_features(row):\n", + " \"\"\"Replicate Ruby PreScorer features.\"\"\"\n", + " features = {}\n", + " texts = {f: str(row.get(f, '') or '') for f in TEXT_FIELDS}\n", + " all_text = ' '.join(t.strip() for t in texts.values() if t.strip())\n", + "\n", + " # Word counts\n", + " for f in TEXT_FIELDS:\n", + " features[f'wc_{f}'] = word_count(texts[f])\n", + " features['wc_total'] = sum(features[f'wc_{f}'] for f in TEXT_FIELDS)\n", + "\n", + " # Field length variance\n", + " counts = [features[f'wc_{f}'] for f in TEXT_FIELDS]\n", + " mean = np.mean(counts)\n", + " features['field_length_variance'] = np.var(counts)\n", + "\n", + " # Sentence stats\n", + " sentences = [s.strip() for s in re.split(r'[.!?]+', all_text) if s.strip()]\n", + " sent_lens = [len(s.split()) for s in sentences]\n", + " features['sentence_count'] = len(sentences)\n", + " features['avg_sentence_length'] = np.mean(sent_lens) if sent_lens else 0\n", + " features['sentence_length_variance'] = np.var(sent_lens) if sent_lens else 0\n", + "\n", + " # Punctuation\n", + " features['exclamation_count'] = all_text.count('!')\n", + " features['question_mark_count'] = all_text.count('?')\n", + "\n", + " # Content signals\n", + " features['url_count'] = len(re.findall(r'https?://\\S+', all_text))\n", + " features['money_mention_count'] = money_mention_count(all_text)\n", + " features['number_count'] = len(re.findall(r'\\b\\d[\\d,.]*\\b', all_text))\n", + " features['email_count'] = len(re.findall(r'\\S+@\\S+\\.\\S+', all_text))\n", + "\n", + " # Empty fields\n", + " features['empty_field_count'] = sum(1 for f in TEXT_FIELDS if not texts[f].strip())\n", + "\n", + " return pd.Series(features)\n", + "\n", + "# Compute for all projects\n", + "print('Computing pre-scorer features...')\n", + "feat_df = df.apply(compute_features, axis=1)\n", + "df = pd.concat([df, feat_df], axis=1)\n", + "print(f'Features computed: {list(feat_df.columns)}')" + ] + }, + { + "cell_type": "markdown", + "id": "ac4e513c", + "metadata": {}, + "source": [ + "## Feature Distributions: Funded vs Hidden" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6797c32", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare feature distributions\n", + "labeled = df[df.label.isin(['funded', 'hidden'])].copy()\n", + "feature_cols = [c for c in feat_df.columns if c != 'empty_field_count']\n", + "\n", + "fig, axes = plt.subplots(3, 4, figsize=(16, 12))\n", + "for ax, col in zip(axes.flat, feature_cols):\n", + " for label, color in [('funded', 'mediumseagreen'), ('hidden', 'salmon')]:\n", + " data = labeled[labeled.label == label][col]\n", + " q99 = data.quantile(0.99)\n", + " ax.hist(data[data <= q99], bins=30, alpha=0.6, color=color, label=label, density=True)\n", + " ax.set_title(col, fontsize=10)\n", + " ax.legend(fontsize=8)\n", + "for ax in axes.flat[len(feature_cols):]:\n", + " ax.set_visible(False)\n", + "plt.suptitle('Feature Distributions: Funded vs Hidden', y=1.01)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c00bdcf3", + "metadata": {}, + "outputs": [], + "source": [ + "# Feature means by label\n", + "summary = df.groupby('label')[feature_cols].mean().round(2).T\n", + "summary['funded_hidden_ratio'] = (summary['funded'] / summary['hidden'].replace(0, np.nan)).round(2)\n", + "print(summary[['funded', 'hidden', 'unlabeled', 'funded_hidden_ratio']].to_string())" + ] + }, + { + "cell_type": "markdown", + "id": "78117e43", + "metadata": {}, + "source": [ + "## Money Extraction Analysis\n", + "\n", + "Using regex pattern matching to find currency mentions across the full corpus. This feeds the `money_mention_count` pre-scorer feature." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "775f5158", + "metadata": {}, + "outputs": [], + "source": [ + "# Money mention distribution\n", + "print('Money mention counts by label:')\n", + "for label in ['funded', 'hidden', 'unlabeled']:\n", + " subset = df[df.label == label]\n", + " mc = subset.money_mention_count\n", + " print(f' {label:>10}: mean={mc.mean():.2f}, median={mc.median():.0f}, '\n", + " f'zero={((mc == 0).mean()):.1%}, 3+={((mc >= 3).mean()):.1%}')\n", + "\n", + "# Show examples of extracted money mentions\n", + "print('\\nSample money extractions (funded apps):')\n", + "funded_with_money = df[(df.label == 'funded') & (df.money_mention_count > 0)].sample(5, random_state=42)\n", + "for _, row in funded_with_money.iterrows():\n", + " mentions = extract_money_mentions(row.text)\n", + " print(f' [{row.money_mention_count} mentions] {mentions[:5]}')" + ] + }, + { + "cell_type": "markdown", + "id": "582f5869", + "metadata": {}, + "source": [ + "## TF-IDF Analysis\n", + "\n", + "Compute corpus-wide IDF values to identify distinctive vocabulary in funded vs hidden applications. The static IDF table can be exported for use in the Ruby pre-scorer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "79c73c21", + "metadata": {}, + "outputs": [], + "source": [ + "# Build TF-IDF on full corpus\n", + "tfidf = TfidfVectorizer(\n", + " max_features=10000,\n", + " stop_words='english',\n", + " min_df=10,\n", + " max_df=0.8,\n", + " ngram_range=(1, 2),\n", + ")\n", + "tfidf_matrix = tfidf.fit_transform(df.text.fillna(''))\n", + "feature_names = tfidf.get_feature_names_out()\n", + "idf_values = tfidf.idf_\n", + "\n", + "print(f'Vocabulary size: {len(feature_names):,}')\n", + "print(f'IDF range: {idf_values.min():.2f} to {idf_values.max():.2f}')\n", + "\n", + "# Most and least common terms\n", + "idf_df = pd.DataFrame({'term': feature_names, 'idf': idf_values}).sort_values('idf')\n", + "print('\\nMost common terms (lowest IDF):')\n", + "print(idf_df.head(20).to_string(index=False))\n", + "print('\\nRarest terms (highest IDF):')\n", + "print(idf_df.tail(20).to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f88f843", + "metadata": {}, + "outputs": [], + "source": [ + "# Terms most distinctive of funded vs hidden\n", + "funded_mask = (df.label == 'funded').values\n", + "hidden_mask = (df.label == 'hidden').values\n", + "\n", + "funded_mean = np.asarray(tfidf_matrix[funded_mask].mean(axis=0)).flatten()\n", + "hidden_mean = np.asarray(tfidf_matrix[hidden_mask].mean(axis=0)).flatten()\n", + "\n", + "diff = funded_mean - hidden_mean\n", + "diff_df = pd.DataFrame({'term': feature_names, 'funded_mean': funded_mean, 'hidden_mean': hidden_mean, 'diff': diff})\n", + "diff_df = diff_df.sort_values('diff', ascending=False)\n", + "\n", + "print('Terms most associated with FUNDED:')\n", + "print(diff_df.head(25)[['term', 'funded_mean', 'hidden_mean', 'diff']].to_string(index=False))\n", + "print('\\nTerms most associated with HIDDEN:')\n", + "print(diff_df.tail(25)[['term', 'funded_mean', 'hidden_mean', 'diff']].to_string(index=False))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff6d2ec3", + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize top distinctive terms\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 8))\n", + "\n", + "top_funded = diff_df.head(20)\n", + "ax1.barh(top_funded.term, top_funded['diff'], color='mediumseagreen')\n", + "ax1.set_title('Top 20 Terms: Funded')\n", + "ax1.invert_yaxis()\n", + "\n", + "top_hidden = diff_df.tail(20).iloc[::-1]\n", + "ax2.barh(top_hidden.term, top_hidden['diff'].abs(), color='salmon')\n", + "ax2.set_title('Top 20 Terms: Hidden')\n", + "ax2.invert_yaxis()\n", + "\n", + "plt.suptitle('TF-IDF: Most Distinctive Terms by Label')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d19ed730", + "metadata": {}, + "source": [ + "## Export Static IDF Table\n", + "\n", + "Export the IDF values as JSON for use in the Ruby pre-scorer. This avoids recomputing TF-IDF at scoring time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d0637f0", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from pathlib import Path\n", + "\n", + "idf_export = dict(zip(feature_names.tolist(), idf_values.round(4).tolist()))\n", + "\n", + "out_path = Path(DB_PATH).parent / 'idf_table.json'\n", + "with open(out_path, 'w') as f:\n", + " json.dump(idf_export, f)\n", + "print(f'Exported {len(idf_export):,} IDF values to {out_path}')" + ] + }, + { + "cell_type": "markdown", + "id": "47052ed5", + "metadata": {}, + "source": [ + "## Feature Correlations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b21de230", + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation matrix of pre-scorer features\n", + "corr = df[feature_cols].corr()\n", + "fig, ax = plt.subplots(figsize=(12, 10))\n", + "sns.heatmap(corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0, ax=ax)\n", + "ax.set_title('Pre-Scorer Feature Correlations')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/03_model_evaluation.ipynb b/notebooks/03_model_evaluation.ipynb new file mode 100644 index 00000000..3c9d3562 --- /dev/null +++ b/notebooks/03_model_evaluation.ipynb @@ -0,0 +1,404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0eb0fd4e", + "metadata": {}, + "source": [ + "# 03 - Model Evaluation\n", + "## Batch Results, Model Comparison, and Cross-Chapter Analysis\n", + "\n", + "Analyze LLM scoring results from the Anthropic Batch API. Compare models, measure test-retest reliability, and validate across chapters.\n", + "\n", + "**Data source:** Parameterized via `AWESOMEBITS_DB` env var.\n", + "\n", + "**Score files:** Place batch result JSONs in `.scratch/data/` with names like `scores-haiku-run1.json`, `scores-sonnet-run1.json`, etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "661387a4", + "metadata": {}, + "outputs": [], + "source": [ + "import sys, json, glob\n", + "sys.path.insert(0, '.')\n", + "from helpers import *\n", + "import pandas as pd\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from scipy import stats\n", + "\n", + "setup_plotting()\n", + "con = connect()\n", + "data_dir = Path(DB_PATH).parent\n", + "print(f'Data directory: {data_dir}')\n", + "print(f'Score files found:')\n", + "for f in sorted(data_dir.glob('scores-*.json')):\n", + " with open(f) as fh:\n", + " d = json.load(fh)\n", + " print(f' {f.name}: {len(d)} scores')" + ] + }, + { + "cell_type": "markdown", + "id": "2fcc822e", + "metadata": {}, + "source": [ + "## Load Scores\n", + "\n", + "Load all available score files and merge with project metadata." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4607f16c", + "metadata": {}, + "outputs": [], + "source": [ + "def load_scores(pattern='scores-*.json'):\n", + " \"\"\"Load score files into a dict of DataFrames keyed by filename stem.\"\"\"\n", + " results = {}\n", + " for path in sorted(data_dir.glob(pattern)):\n", + " with open(path) as f:\n", + " scores = json.load(f)\n", + " # Handle both list and dict formats\n", + " if isinstance(scores, dict):\n", + " rows = [{'project_id': int(k), **v} for k, v in scores.items()]\n", + " else:\n", + " rows = scores\n", + " sdf = pd.DataFrame(rows)\n", + " results[path.stem] = sdf\n", + " return results\n", + "\n", + "score_sets = load_scores()\n", + "print(f'Loaded {len(score_sets)} score sets: {list(score_sets.keys())}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa9fc8f7", + "metadata": {}, + "outputs": [], + "source": [ + "# Merge scores with labels from DuckDB\n", + "projects = con.execute('''\n", + " SELECT p.id as project_id, p.title, p.funded_on, p.hidden_at,\n", + " c.name as chapter_name, c.country\n", + " FROM projects p\n", + " JOIN chapters c ON p.chapter_id = c.id\n", + "''').df()\n", + "projects['label'] = projects.apply(label_project, axis=1)\n", + "\n", + "# Merge each score set with labels\n", + "for name, sdf in score_sets.items():\n", + " merged = sdf.merge(projects, on='project_id', how='left')\n", + " score_sets[name] = merged\n", + " labeled = merged[merged.label.isin(['funded', 'hidden'])]\n", + " print(f'{name}: {len(merged)} total, {len(labeled)} labeled '\n", + " f'(funded={len(labeled[labeled.label==\"funded\"])}, '\n", + " f'hidden={len(labeled[labeled.label==\"hidden\"])})')" + ] + }, + { + "cell_type": "markdown", + "id": "3ad8aed5", + "metadata": {}, + "source": [ + "## Score Distributions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cfef3ce5", + "metadata": {}, + "outputs": [], + "source": [ + "# Composite score distribution per model\n", + "score_col = 'composite_score' # adjust if your JSON uses a different key\n", + "# Try to find the right column name\n", + "for name, sdf in score_sets.items():\n", + " candidates = [c for c in sdf.columns if 'score' in c.lower() and 'composite' in c.lower()]\n", + " if not candidates:\n", + " candidates = [c for c in sdf.columns if c in ('composite_score', 'score', 'total_score')]\n", + " if candidates:\n", + " score_col = candidates[0]\n", + " break\n", + "print(f'Using score column: {score_col}')\n", + "\n", + "fig, axes = plt.subplots(1, len(score_sets), figsize=(6 * len(score_sets), 5), squeeze=False)\n", + "for ax, (name, sdf) in zip(axes[0], score_sets.items()):\n", + " if score_col not in sdf.columns:\n", + " ax.set_title(f'{name} (no {score_col})')\n", + " continue\n", + " for label, color in [('funded', 'mediumseagreen'), ('hidden', 'salmon'), ('unlabeled', 'lightgray')]:\n", + " subset = sdf[sdf.label == label][score_col].dropna()\n", + " if len(subset):\n", + " ax.hist(subset, bins=20, alpha=0.6, color=color, label=f'{label} (n={len(subset)})', range=(0, 1))\n", + " ax.set_title(name)\n", + " ax.set_xlabel('Composite Score')\n", + " ax.legend()\n", + "plt.suptitle('Score Distributions by Model and Label')\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "c21b3810", + "metadata": {}, + "source": [ + "## Classification Metrics\n", + "\n", + "Using composite score thresholds, how well does each model separate funded from hidden?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b4dee0d", + "metadata": {}, + "outputs": [], + "source": [ + "def evaluate_model(sdf, score_col, threshold=0.5):\n", + " \"\"\"Compute classification metrics at a given threshold.\"\"\"\n", + " labeled = sdf[sdf.label.isin(['funded', 'hidden'])].dropna(subset=[score_col]).copy()\n", + " labeled['predicted'] = (labeled[score_col] >= threshold).map({True: 'funded', False: 'hidden'})\n", + "\n", + " tp = ((labeled.label == 'funded') & (labeled.predicted == 'funded')).sum()\n", + " tn = ((labeled.label == 'hidden') & (labeled.predicted == 'hidden')).sum()\n", + " fp = ((labeled.label == 'hidden') & (labeled.predicted == 'funded')).sum()\n", + " fn = ((labeled.label == 'funded') & (labeled.predicted == 'hidden')).sum()\n", + "\n", + " precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n", + " recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n", + " f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n", + " accuracy = (tp + tn) / len(labeled) if len(labeled) > 0 else 0\n", + "\n", + " return {\n", + " 'n': len(labeled), 'tp': tp, 'tn': tn, 'fp': fp, 'fn': fn,\n", + " 'precision': round(precision, 3), 'recall': round(recall, 3),\n", + " 'f1': round(f1, 3), 'accuracy': round(accuracy, 3),\n", + " 'funded_avg': round(labeled[labeled.label == 'funded'][score_col].mean(), 3),\n", + " 'hidden_avg': round(labeled[labeled.label == 'hidden'][score_col].mean(), 3),\n", + " }\n", + "\n", + "# Evaluate all models at multiple thresholds\n", + "thresholds = [0.3, 0.4, 0.5, 0.6, 0.7]\n", + "for name, sdf in score_sets.items():\n", + " if score_col not in sdf.columns:\n", + " print(f'{name}: no {score_col} column')\n", + " continue\n", + " print(f'\\n=== {name} ===')\n", + " rows = []\n", + " for t in thresholds:\n", + " m = evaluate_model(sdf, score_col, threshold=t)\n", + " rows.append({'threshold': t, **m})\n", + " print(pd.DataFrame(rows)[['threshold', 'precision', 'recall', 'f1', 'accuracy', 'fn', 'fp']].to_string(index=False))" + ] + }, + { + "cell_type": "markdown", + "id": "c3f10549", + "metadata": {}, + "source": [ + "## Model Comparison\n", + "\n", + "If multiple models scored the same applications, compare them head-to-head." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6536d1d", + "metadata": {}, + "outputs": [], + "source": [ + "if len(score_sets) >= 2:\n", + " names = list(score_sets.keys())\n", + " # Merge first two score sets on project_id\n", + " a_name, b_name = names[0], names[1]\n", + " a = score_sets[a_name][['project_id', score_col, 'label']].rename(columns={score_col: f'{a_name}'})\n", + " b = score_sets[b_name][['project_id', score_col]].rename(columns={score_col: f'{b_name}'})\n", + " comp = a.merge(b, on='project_id', how='inner')\n", + "\n", + " print(f'Comparing {a_name} vs {b_name}: {len(comp)} shared applications')\n", + "\n", + " # Correlation\n", + " r, p = stats.pearsonr(comp[a_name].dropna(), comp[b_name].dropna())\n", + " rho, _ = stats.spearmanr(comp[a_name].dropna(), comp[b_name].dropna())\n", + " print(f'Pearson r={r:.3f} (p={p:.2e})')\n", + " print(f'Spearman rho={rho:.3f}')\n", + "\n", + " # Score difference stats\n", + " comp['diff'] = comp[a_name] - comp[b_name]\n", + " print(f'Mean diff: {comp[\"diff\"].mean():.3f}')\n", + " print(f'Std diff: {comp[\"diff\"].std():.3f}')\n", + " print(f'Max abs diff: {comp[\"diff\"].abs().max():.3f}')\n", + "\n", + " fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))\n", + " colors = comp.label.map({'funded': 'mediumseagreen', 'hidden': 'salmon', 'unlabeled': 'lightgray'})\n", + " ax1.scatter(comp[a_name], comp[b_name], alpha=0.4, c=colors, s=20)\n", + " ax1.plot([0, 1], [0, 1], 'k--', alpha=0.3)\n", + " ax1.set_xlabel(a_name)\n", + " ax1.set_ylabel(b_name)\n", + " ax1.set_title(f'Score Comparison (r={r:.3f})')\n", + "\n", + " ax2.hist(comp['diff'], bins=40, color='steelblue', alpha=0.7)\n", + " ax2.axvline(0, color='black', linestyle='--', alpha=0.3)\n", + " ax2.set_xlabel('Score Difference')\n", + " ax2.set_title(f'Score Difference Distribution')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "else:\n", + " print('Only one score set available. Submit additional batches for comparison.')" + ] + }, + { + "cell_type": "markdown", + "id": "baad6c12", + "metadata": {}, + "source": [ + "## Test-Retest Reliability\n", + "\n", + "If the same model was run twice on the same inputs, measure score stability. Look for score files like `scores-haiku-run1.json` and `scores-haiku-run2.json`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6627ca51", + "metadata": {}, + "outputs": [], + "source": [ + "# Find run pairs (same model, different runs)\n", + "import re as _re\n", + "run_pairs = {}\n", + "for name in score_sets:\n", + " m = _re.match(r'(scores-\\w+)-run(\\d+)', name)\n", + " if m:\n", + " base, run_num = m.group(1), int(m.group(2))\n", + " run_pairs.setdefault(base, {})[run_num] = name\n", + "\n", + "if run_pairs:\n", + " for base, runs in run_pairs.items():\n", + " if len(runs) < 2:\n", + " continue\n", + " r1_name = runs[min(runs)]\n", + " r2_name = runs[max(runs)]\n", + " r1 = score_sets[r1_name][['project_id', score_col]].rename(columns={score_col: 'run1'})\n", + " r2 = score_sets[r2_name][['project_id', score_col]].rename(columns={score_col: 'run2'})\n", + " paired = r1.merge(r2, on='project_id')\n", + "\n", + " r, _ = stats.pearsonr(paired.run1, paired.run2)\n", + " rho, _ = stats.spearmanr(paired.run1, paired.run2)\n", + " diff = (paired.run1 - paired.run2).abs()\n", + "\n", + " print(f'\\n=== {base} test-retest (n={len(paired)}) ===')\n", + " print(f'Pearson r={r:.4f}, Spearman rho={rho:.4f}')\n", + " print(f'Mean abs diff: {diff.mean():.4f}')\n", + " print(f'Max abs diff: {diff.max():.4f}')\n", + " print(f'Std diff: {(paired.run1 - paired.run2).std():.4f}')\n", + " # Threshold consistency\n", + " for t in [0.3, 0.5, 0.7]:\n", + " agree = ((paired.run1 >= t) == (paired.run2 >= t)).mean()\n", + " print(f' Threshold {t}: {agree:.1%} agreement')\n", + "else:\n", + " print('No test-retest pairs found. Run the same model twice and name files like:')\n", + " print(' scores-haiku-run1.json, scores-haiku-run2.json')" + ] + }, + { + "cell_type": "markdown", + "id": "1ef1db84", + "metadata": {}, + "source": [ + "## Cross-Chapter Analysis\n", + "\n", + "Do scores generalize across chapters, or are patterns locality-specific?\n", + "\n", + "Chapters with enough labeled data for validation:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0c22898", + "metadata": {}, + "outputs": [], + "source": [ + "# Show which chapters have enough data\n", + "chapter_stats = con.execute('''\n", + " SELECT c.name, c.country, c.inactive_at IS NOT NULL as inactive,\n", + " COUNT(*) as total, COUNT(p.funded_on) as funded, COUNT(p.hidden_at) as hidden\n", + " FROM projects p JOIN chapters c ON p.chapter_id = c.id\n", + " GROUP BY c.name, c.country, inactive\n", + " HAVING COUNT(p.funded_on) >= 10 AND COUNT(p.hidden_at) >= 10\n", + " ORDER BY total DESC\n", + "''').df()\n", + "print(chapter_stats.to_string(index=False))\n", + "print(f'\\n{len(chapter_stats)} chapters have enough labeled data for validation')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ca55338", + "metadata": {}, + "outputs": [], + "source": [ + "# Per-chapter score analysis (if scores include chapter info)\n", + "for name, sdf in score_sets.items():\n", + " if score_col not in sdf.columns or 'chapter_name' not in sdf.columns:\n", + " continue\n", + " print(f'\\n=== {name}: per-chapter scores ===')\n", + " ch_scores = sdf.groupby(['chapter_name', 'label'])[score_col].agg(['mean', 'count']).round(3)\n", + " ch_scores = ch_scores[ch_scores['count'] >= 5].reset_index()\n", + " # Pivot for readability\n", + " pivot = ch_scores.pivot_table(index='chapter_name', columns='label', values='mean')\n", + " if 'funded' in pivot.columns and 'hidden' in pivot.columns:\n", + " pivot['separation'] = (pivot['funded'] - pivot['hidden']).round(3)\n", + " print(pivot.sort_values('separation', ascending=False).to_string())\n", + " else:\n", + " print(pivot.to_string())" + ] + }, + { + "cell_type": "markdown", + "id": "65c481fa", + "metadata": {}, + "source": [ + "## Predicted Quality Ratio\n", + "\n", + "Does the ~28% quality ratio (from Chicago labeled data) hold when scoring unlabeled applications?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70edee5a", + "metadata": {}, + "outputs": [], + "source": [ + "for name, sdf in score_sets.items():\n", + " if score_col not in sdf.columns:\n", + " continue\n", + " print(f'\\n=== {name} ===')\n", + " for t in [0.3, 0.5, 0.7]:\n", + " unlabeled = sdf[sdf.label == 'unlabeled']\n", + " above = (unlabeled[score_col] >= t).mean()\n", + " print(f' Threshold {t}: {above:.1%} of unlabeled apps score above')\n", + " # Overall distribution\n", + " print(f' Unlabeled score stats: mean={sdf[sdf.label==\"unlabeled\"][score_col].mean():.3f}, '\n", + " f'median={sdf[sdf.label==\"unlabeled\"][score_col].median():.3f}')" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/README.md b/notebooks/README.md new file mode 100644 index 00000000..529c1d19 --- /dev/null +++ b/notebooks/README.md @@ -0,0 +1,38 @@ +# Signal Score Notebooks + +Jupyter notebooks for analyzing the Awesome Foundation Signal Score pipeline. + +## Setup + +```bash +pip install -r notebooks/requirements.txt +``` + +## Data + +Notebooks read from a DuckDB database. Default path: `.scratch/data/awesomebits.duckdb` + +Override with an environment variable: + +```bash +export AWESOMEBITS_DB=/path/to/your/awesomebits.duckdb +``` + +To build the database from CSV exports, run: + +```bash +ruby scripts/signal-score/import_data.rb +``` + +## Notebooks + +| Notebook | Description | +|----------|-------------| +| `01_eda.ipynb` | Corpus stats, chapter distributions, label rates, text analysis | +| `02_feature_engineering.ipynb` | Pre-scorer features, TF-IDF, money extraction, feature correlations | +| `03_model_evaluation.ipynb` | Batch results, model comparison, test-retest reliability, cross-chapter | + +## No Real Data + +These notebooks are git-tracked. They contain no real grant application text. +All analysis reads from a local DuckDB database that lives in `.scratch/` (gitignored). diff --git a/notebooks/helpers.py b/notebooks/helpers.py new file mode 100644 index 00000000..0dd9fcce --- /dev/null +++ b/notebooks/helpers.py @@ -0,0 +1,115 @@ +"""Shared helpers for Signal Score notebooks.""" + +import os +import re +from pathlib import Path + +import duckdb +import matplotlib.pyplot as plt +import seaborn as sns + +# --- Data source --- +DEFAULT_DB_PATH = os.path.join( + os.path.dirname(__file__), "..", ".scratch", "data", "awesomebits.duckdb" +) +DB_PATH = os.environ.get("AWESOMEBITS_DB", DEFAULT_DB_PATH) + + +def connect(read_only: bool = True) -> duckdb.DuckDBPyConnection: + """Open the DuckDB database.""" + path = str(Path(DB_PATH).resolve()) + return duckdb.connect(path, read_only=read_only) + + +# --- Plotting defaults --- +def setup_plotting(): + """Configure consistent plot styling.""" + sns.set_theme(style="whitegrid", palette="muted", font_scale=1.1) + plt.rcParams.update( + { + "figure.figsize": (10, 6), + "figure.dpi": 100, + "axes.titlesize": 14, + "axes.labelsize": 12, + } + ) + + +# --- Text helpers --- +TEXT_FIELDS = ["about_me", "about_project", "use_for_money"] + + +def combined_text(row) -> str: + """Combine the three text fields into one string.""" + parts = [str(row.get(f, "") or "") for f in TEXT_FIELDS] + return " ".join(p.strip() for p in parts if p.strip()) + + +def word_count(text: str) -> int: + """Count words in text.""" + return len(text.split()) if text.strip() else 0 + + +# --- Money extraction --- +# Currency symbols and written forms +CURRENCY_SYMBOLS = r"[\$\£\€\¥\₹\₩\₫\₱\₺\₽\฿\₿]" +CURRENCY_PREFIXED = r"(?:A|NZ|CA|HK|S|US|C)\$" +CURRENCY_CODES = ( + r"\b(?:USD|GBP|EUR|AUD|NZD|CAD|CHF|JPY|CNY|INR|BRL|MXN|ZAR|SEK|NOK|DKK|" + r"KRW|SGD|HKD|TWD|THB|MYR|PHP|IDR|VND|CZK|PLN|HUF|RON|BGN|HRK|RUB|TRY|" + r"ILS|AED|SAR|QAR|KWD|BHD|OMR|CLP|COP|PEN|ARS|UYU)\b" +) +CURRENCY_WORDS = ( + r"\b(?:dollars?|pounds?|euros?|yen|yuan|rupees?|rand|krona|kronor|" + r"francs?|pesos?|reais?|real|won|baht|ringgit|dong|crowns?|zloty|forints?|" + r"lira|shekels?|dirhams?|riyals?)\b" +) + +# Number patterns (handles 1,000 and 1.000 and 1 000) +NUMBER_PATTERN = r"\d[\d,.\s]*\d|\d+" + +# Combined money pattern +MONEY_PATTERN = re.compile( + rf"(?:" + rf"(?:{CURRENCY_PREFIXED}|{CURRENCY_SYMBOLS})\s*(?:{NUMBER_PATTERN})" # symbol before number + rf"|(?:{NUMBER_PATTERN})\s*(?:{CURRENCY_PREFIXED}|{CURRENCY_SYMBOLS})" # number before symbol + rf"|(?:{NUMBER_PATTERN})\s*(?:{CURRENCY_WORDS})" # number + written currency + rf"|(?:{CURRENCY_CODES})\s*(?:{NUMBER_PATTERN})" # ISO code + number + rf"|(?:{NUMBER_PATTERN})\s*(?:{CURRENCY_CODES})" # number + ISO code + rf")", + re.IGNORECASE, +) + + +def extract_money_mentions(text: str) -> list[str]: + """Extract money/currency mentions from free text.""" + return MONEY_PATTERN.findall(text) + + +def money_mention_count(text: str) -> int: + """Count money mentions in text.""" + return len(extract_money_mentions(text)) + + +# --- Label helpers --- +def label_project(row) -> str: + """Assign a label based on funded_on / hidden_at.""" + import pandas as pd + + if pd.notna(row.get("funded_on")): + return "funded" + if pd.notna(row.get("hidden_at")): + return "hidden" + return "unlabeled" + + +# --- Chapter helpers --- +ENGLISH_SPEAKING_COUNTRIES = { + "United States", + "Canada", + "Australia", + "United Kingdom", + "Ireland", + "New Zealand", + "Worldwide", +} diff --git a/notebooks/requirements.txt b/notebooks/requirements.txt new file mode 100644 index 00000000..14e81819 --- /dev/null +++ b/notebooks/requirements.txt @@ -0,0 +1,9 @@ +duckdb>=1.0 +pandas>=2.0 +numpy>=1.24 +matplotlib>=3.7 +seaborn>=0.13 +scikit-learn>=1.3 +price-parser>=0.3 +scipy>=1.11 +jupyterlab>=4.0 diff --git a/scripts/signal-score/pre_scorer.rb b/scripts/signal-score/pre_scorer.rb index e5b02409..5366965f 100644 --- a/scripts/signal-score/pre_scorer.rb +++ b/scripts/signal-score/pre_scorer.rb @@ -41,10 +41,13 @@ def analyze! # Content signals @features["url_count"] = all_text.scan(%r{https?://\S+}).length - @features["dollar_sign_count"] = all_text.count("$") @features["number_count"] = all_text.scan(/\b\d[\d,.]*\b/).length @features["email_count"] = all_text.scan(/\S+@\S+\.\S+/).length + # Money extraction (international currencies) + money_matches = extract_money_mentions(all_text) + @features["money_mention_count"] = money_matches.length + # Capitalized words (proxy for proper nouns / named entities) words = all_text.split(/\s+/) # Skip first word of each sentence @@ -64,9 +67,27 @@ def analyze! # Has images (title/about fields mention image/photo/picture URLs) @features["has_image_reference"] = all_text.match?(/\.(jpg|jpeg|png|gif|webp|bmp)/i) ? 1 : 0 + # TF-IDF features (requires static IDF table) + if self.class.idf_table + tfidf_scores = compute_tfidf(all_text) + @features["tfidf_mean"] = tfidf_scores.empty? ? 0.0 : (tfidf_scores.values.sum / tfidf_scores.length.to_f).round(4) + @features["tfidf_max"] = tfidf_scores.empty? ? 0.0 : tfidf_scores.values.max.round(4) + @features["tfidf_unique_terms"] = tfidf_scores.count { |_, v| v > 0 } + end + @features end + # Load static IDF table (computed from full corpus via notebook 02) + def self.idf_table + @idf_table + end + + def self.load_idf(path) + require "json" + @idf_table = JSON.parse(File.read(path)) + end + private def field_text(name) @@ -86,4 +107,45 @@ def variance(values) mean = values.sum / values.length.to_f values.sum { |v| (v - mean)**2 } / values.length.to_f end + + # International money/currency extraction + CURRENCY_SYMBOLS = /[\$\£\€\¥\₹\₩\₫\₱\₺\₽\฿]/ + CURRENCY_PREFIXED = /(?:A|NZ|CA|HK|S|US|C)\$/ + CURRENCY_CODES = /\b(?:USD|GBP|EUR|AUD|NZD|CAD|CHF|JPY|CNY|INR|BRL|MXN|ZAR|SEK|NOK|DKK|KRW|SGD|HKD|TWD|THB|MYR|PHP|IDR|VND|CZK|PLN|HUF|RON|RUB|TRY|ILS|AED|SAR|CLP|COP|PEN|ARS)\b/i + CURRENCY_WORDS = /\b(?:dollars?|pounds?|euros?|yen|yuan|rupees?|rand|krona|kronor|francs?|pesos?|reais?|real|won|baht|ringgit|crowns?|zloty|forints?|lira|shekels?|dirhams?|riyals?)\b/i + NUMBER_PAT = /\d[\d,.\s]*\d|\d+/ + + MONEY_PATTERN = Regexp.union( + /(?:#{CURRENCY_PREFIXED}|#{CURRENCY_SYMBOLS})\s*#{NUMBER_PAT}/, + /#{NUMBER_PAT}\s*(?:#{CURRENCY_PREFIXED}|#{CURRENCY_SYMBOLS})/, + /#{NUMBER_PAT}\s*#{CURRENCY_WORDS}/, + /#{CURRENCY_CODES}\s*#{NUMBER_PAT}/, + /#{NUMBER_PAT}\s*#{CURRENCY_CODES}/ + ) + + def extract_money_mentions(text) + text.scan(MONEY_PATTERN) + end + + # TF-IDF: compute term frequencies against static IDF table + def compute_tfidf(text) + idf = self.class.idf_table + return {} unless idf + + # Tokenize: lowercase, split on non-word, reject short + words = text.downcase.split(/\W+/).reject { |w| w.length < 2 } + return {} if words.empty? + + # Term frequency (normalized) + tf = Hash.new(0) + words.each { |w| tf[w] += 1 } + tf.transform_values! { |c| c.to_f / words.length } + + # TF-IDF for terms in our IDF table + scores = {} + tf.each do |term, freq| + scores[term] = freq * idf[term] if idf.key?(term) + end + scores + end end From 6982afc4f6231e96139b7bbcd7383790507fb0d1 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 17:18:19 -0600 Subject: [PATCH 06/25] feat(signal-score): add config, cache, prompt builder, and specs - Add config/signal_score/categories.json with 20 canonical categories in kebab-case - Add AnthropicCache: file-based passthrough cache for API responses, keyed by SHA256 of request params - Add PromptBuilder: assembles system prompt, tool schemas, few-shot examples with chapter-specific config overrides (deep-merge chapter over global default) - Add pre_scorer_spec.rb with unit tests (money extraction, empty fields, TF-IDF) and seeded data tests (tagged :signal_score_data, opt-in via SIGNAL_SCORE_DATA=1) - Prompt supports configurable grant amount and currency for international chapters Refs: #593, #591 --- config/signal_score/categories.json | 22 ++ scripts/signal-score/anthropic_cache.rb | 92 +++++++++ scripts/signal-score/prompt_builder.rb | 245 +++++++++++++++++++++++ spec/lib/signal_score/pre_scorer_spec.rb | 194 ++++++++++++++++++ 4 files changed, 553 insertions(+) create mode 100644 config/signal_score/categories.json create mode 100644 scripts/signal-score/anthropic_cache.rb create mode 100644 scripts/signal-score/prompt_builder.rb create mode 100644 spec/lib/signal_score/pre_scorer_spec.rb diff --git a/config/signal_score/categories.json b/config/signal_score/categories.json new file mode 100644 index 00000000..9a66a034 --- /dev/null +++ b/config/signal_score/categories.json @@ -0,0 +1,22 @@ +[ + "public-art", + "community-garden", + "youth-education", + "music", + "food-community", + "film-media", + "literary", + "dance", + "theater", + "environment", + "sports-recreation", + "festival-event", + "technology-stem", + "women-girls", + "health-wellness", + "children-families", + "civic-social-justice", + "cultural-heritage", + "immigrant-refugee", + "housing-homelessness" +] diff --git a/scripts/signal-score/anthropic_cache.rb b/scripts/signal-score/anthropic_cache.rb new file mode 100644 index 00000000..c8dbbe3c --- /dev/null +++ b/scripts/signal-score/anthropic_cache.rb @@ -0,0 +1,92 @@ +# frozen_string_literal: true + +require "digest" +require "json" +require "fileutils" + +# File-based passthrough cache for Anthropic API responses. +# Cache key = SHA256(model + system_prompt + tool_schema + user_message) +# +# Usage: +# cache = AnthropicCache.new(".scratch/cache/anthropic") +# response = cache.fetch(request_params) { call_anthropic(request_params) } +# +class AnthropicCache + attr_reader :cache_dir, :stats + + def initialize(cache_dir = nil) + @cache_dir = cache_dir || File.expand_path("../../.scratch/cache/anthropic", __dir__) + FileUtils.mkdir_p(@cache_dir) + @stats = { hits: 0, misses: 0 } + end + + # Fetch a cached response or execute the block and cache the result. + def fetch(request_params) + key = cache_key(request_params) + path = cache_path(key) + + if File.exist?(path) + @stats[:hits] += 1 + JSON.parse(File.read(path)) + else + response = yield + File.write(path, JSON.pretty_generate(response)) + @stats[:misses] += 1 + response + end + end + + # Check if a response is cached without fetching. + def cached?(request_params) + File.exist?(cache_path(cache_key(request_params))) + end + + # Write a result directly (e.g., from batch API result parsing). + def write(request_params, response) + key = cache_key(request_params) + File.write(cache_path(key), JSON.pretty_generate(response)) + end + + # Read a cached result without calling API. + def read(request_params) + key = cache_key(request_params) + path = cache_path(key) + return nil unless File.exist?(path) + + JSON.parse(File.read(path)) + end + + # Number of cached responses. + def size + Dir.glob(File.join(@cache_dir, "*.json")).length + end + + # Clear all cached responses. + def clear! + Dir.glob(File.join(@cache_dir, "*.json")).each { |f| File.delete(f) } + end + + private + + def cache_key(params) + # Extract the parts that determine the response + model = params[:model] || params["model"] || "" + system = params[:system] || params["system"] || "" + tools = params[:tools] || params["tools"] || [] + messages = params[:messages] || params["messages"] || [] + + # Stable serialization for hashing + content = JSON.generate({ + model: model, + system: system, + tools: tools, + messages: messages, + }) + + Digest::SHA256.hexdigest(content) + end + + def cache_path(key) + File.join(@cache_dir, "#{key}.json") + end +end diff --git a/scripts/signal-score/prompt_builder.rb b/scripts/signal-score/prompt_builder.rb new file mode 100644 index 00000000..2434385f --- /dev/null +++ b/scripts/signal-score/prompt_builder.rb @@ -0,0 +1,245 @@ +# frozen_string_literal: true + +require "json" + +# Assembles scoring prompts with chapter-specific config overrides. +# +# Config resolution order: +# 1. Chapter-specific overrides (from DB or .scratch/prompts/{slug}.json) +# 2. Global default (.scratch/prompts/default.json or hardcoded) +# +# Usage: +# builder = PromptBuilder.new(chapter: "Chicago, IL") +# system_prompt = builder.system_prompt +# tool_schema = builder.score_tool +# few_shot = builder.few_shot_text(funded_projects, hidden_projects) +# +class PromptBuilder + CATEGORIES_PATH = File.expand_path("../../config/signal_score/categories.json", __dir__) + PROMPTS_DIR = File.expand_path("../../.scratch/prompts", __dir__) + + attr_reader :config + + def initialize(chapter: nil, config_override: nil) + @config = resolve_config(chapter, config_override) + end + + # --- Prompt assembly --- + + def system_prompt + # Full override takes precedence + return @config["system_prompt"] if @config["system_prompt"] + + base = default_system_prompt + addendum = @config["rubric_addendum"] + addendum ? "#{base}\n\n#{addendum}" : base + end + + def score_tool + SCORE_TOOL + end + + def category_tool + CATEGORY_TOOL + end + + def categories + @categories ||= begin + custom = @config.dig("category_config", "custom") || [] + disabled = @config.dig("category_config", "disabled") || [] + base = canonical_categories.reject { |c| disabled.include?(c) } + base + custom.map { |c| c["slug"] } + end + end + + def model + @config["model"] || "claude-haiku-4-5-20251001" + end + + def grant_amount + @config["grant_amount"] || 1000 + end + + def currency + @config["currency"] || "USD" + end + + def few_shot_count + @config["few_shot_count"] || 15 + end + + # Build the few-shot examples text block. + def few_shot_text(funded_projects, hidden_projects) + n = few_shot_count + + text = "## EXAMPLES OF FUNDED (WINNING) APPLICATIONS\n\n" + funded_projects.first(n).each do |p| + text += "### Project #{p['id']} — FUNDED\n#{format_application(p)}\n\n" + end + + text += "## EXAMPLES OF HIDDEN (REJECTED/FILTERED) APPLICATIONS\n\n" + hidden_projects.first(n).each do |p| + reason = p["hidden_reason"] ? " (reason: #{p['hidden_reason']})" : "" + text += "### Project #{p['id']} — HIDDEN#{reason}\n#{format_application(p)}\n\n" + end + + text + end + + def format_application(project) + parts = ["Title: #{project['title'] || '(empty)'}"] + parts << "Chapter: #{project['chapter_name']}" if project["chapter_name"] + %w[about_me about_project use_for_money].each do |f| + val = project[f].to_s.strip + parts << "#{f.tr('_', ' ').split.map(&:capitalize).join(' ')}: #{val}" unless val.empty? + end + parts.join("\n") + end + + private + + def resolve_config(chapter, override) + base = load_default_config + if chapter + chapter_config = load_chapter_config(chapter) + base = deep_merge(base, chapter_config) if chapter_config + end + base = deep_merge(base, override) if override + base + end + + def load_default_config + path = File.join(PROMPTS_DIR, "default.json") + File.exist?(path) ? JSON.parse(File.read(path)) : {} + end + + def load_chapter_config(chapter) + slug = chapter.downcase.gsub(/[^a-z0-9]+/, "-").gsub(/-+$/, "") + path = File.join(PROMPTS_DIR, "chapters", "#{slug}.json") + File.exist?(path) ? JSON.parse(File.read(path)) : nil + end + + def canonical_categories + @canonical_categories ||= JSON.parse(File.read(CATEGORIES_PATH)) + end + + def deep_merge(base, overlay) + base.merge(overlay) do |_key, old_val, new_val| + if old_val.is_a?(Hash) && new_val.is_a?(Hash) + deep_merge(old_val, new_val) + else + new_val + end + end + end + + # --- Default system prompt (hardcoded fallback) --- + + def default_system_prompt + amount = grant_amount + curr = currency == "USD" ? "$" : currency + + <<~PROMPT + You are an expert grant application screener for the Awesome Foundation. + + The Awesome Foundation is a global network of volunteer "micro-trustees" who each chip in to award #{curr}#{amount} grants for awesome projects. No strings attached — the money goes to creative, community-benefiting, unique ideas. + + Score each application using the score_application tool. Extract structured features to help trustees prioritize their review. + + ## Scoring Rubric (composite_score: 0.0 to 1.0) + + - 0.0–0.1: Clear spam, gibberish, test submissions, or AI-generated mass submissions + - 0.1–0.3: Real but very weak — business pitches, personal fundraising, vague ideas, wrong location + - 0.3–0.5: Borderline — decent concept but missing details, unclear community benefit, generic + - 0.5–0.7: Solid — clear project, community benefit, actionable plan, reasonable for #{curr}#{amount} + - 0.7–0.9: Strong — creative, specific, well-articulated, exactly what AF funds + - 0.9–1.0: Exceptional — innovative, clearly impactful, inspiring, would excite any trustee + + ## Feature Dimensions + + ### Trust Equation: T = (Credibility + Reliability + Intimacy) / (1 + Self-Interest) + + **Numerator (higher = better):** + - credibility: Clear budget, realistic plan, relevant expertise. (0=vague, 1=detailed budget + clear execution) + - reliability: Track record, prior work, organizational backing. (0=no evidence, 1=strong track record) + - intimacy: Connection to community, local ties, authentic voice. Named neighborhoods, specific orgs, personal anecdotes. Sub-city precision (ward/zip) scores higher than city-level. (0=generic, 1=deeply embedded) + + **Denominator (higher = worse):** + - self_interest: Money primarily benefits applicant? Living expenses, tuition, business startup, self-payment >50% of budget. (0=community-benefiting, 1=self-serving) + + ### Additional Signals + + - specificity: Concrete details — costs, addresses, timelines, partner names. (0=vague, 1=highly specific) + - creativity: How original, unique, fun, or surprising? "The opposite of whimsy is boring, not serious." (0=generic, 1=delightfully novel) + - budget_alignment: Can #{curr}#{amount} meaningfully complete this? Is it a "drop in the bucket" or perfectly sized? (0=wildly mismatched, 1=perfectly scoped) + - catalytic_potential: Does #{curr}#{amount} unlock something bigger — prototype, proof-of-concept, career catalyst? (0=standalone purchase, 1=unlocks outsized outcomes) + - community_benefit: Clear benefit beyond the applicant? Creating new connections scores higher than serving existing community. (0=purely personal, 1=broad community impact + new connections) + - personal_voice: Does it sound like a real person? Quirky details, informal language, personal anecdotes = POSITIVE. Over-polished corporate language = NEGATIVE. (0=robotic/templated, 1=authentic and personal) + + ### AI Detection (two separate signals) + + - ai_spam_likelihood: Mass-generated generic proposal — no personal details, no local knowledge. (0=clearly genuine, 1=almost certainly mass-generated) + - ai_writing_likelihood: Exhibits AI writing patterns? Uniform sentences, significance inflation, promotional language. INFORMATIONAL ONLY — do not factor into composite_score. (0=clearly human, 1=heavily AI-styled) + + ## Flags (include any that apply) + spam, ai_spam, duplicate, incomplete, wrong_location, business_pitch, personal_fundraising, low_effort + + ## Key Principles + - AF values creativity, community impact, and fun + - #{curr}#{amount} is small — projects should be scoped appropriately + - Professional credentials are neutral-to-negative + - Projects "too weird for traditional funders" = MORE awesome, not less + - ~28% of applications are typically review-worthy + PROMPT + end + + # --- Tool schemas --- + + SCORE_TOOL = { + "name" => "score_application", + "description" => "Score a grant application and extract structured features", + "input_schema" => { + "type" => "object", + "required" => %w[composite_score reason flags features], + "properties" => { + "composite_score" => { "type" => "number", "description" => "Overall score 0.0-1.0" }, + "reason" => { "type" => "string", "description" => "One-sentence explanation" }, + "flags" => { "type" => "array", "items" => { "type" => "string" } }, + "features" => { + "type" => "object", + "required" => %w[ + credibility reliability intimacy self_interest + specificity creativity budget_alignment catalytic_potential + community_benefit personal_voice + ai_spam_likelihood ai_writing_likelihood + ], + "properties" => %w[ + credibility reliability intimacy self_interest + specificity creativity budget_alignment catalytic_potential + community_benefit personal_voice + ai_spam_likelihood ai_writing_likelihood + ].each_with_object({}) { |k, h| h[k] = { "type" => "number" } }, + }, + }, + }, + }.freeze + + CATEGORY_TOOL = { + "name" => "categorize_application", + "description" => "Categorize a grant application with thematic scores and freeform tags", + "input_schema" => { + "type" => "object", + "required" => %w[categories tags primary_category], + "properties" => { + "primary_category" => { "type" => "string" }, + "categories" => { + "type" => "object", + "properties" => JSON.parse(File.read(CATEGORIES_PATH)).each_with_object({}) { |c, h| + h[c] = { "type" => "number" } + }, + }, + "tags" => { "type" => "array", "items" => { "type" => "string" } }, + }, + }, + }.freeze +end diff --git a/spec/lib/signal_score/pre_scorer_spec.rb b/spec/lib/signal_score/pre_scorer_spec.rb new file mode 100644 index 00000000..eb25d129 --- /dev/null +++ b/spec/lib/signal_score/pre_scorer_spec.rb @@ -0,0 +1,194 @@ +# frozen_string_literal: true + +require_relative "../../../scripts/signal-score/pre_scorer" + +RSpec.describe PreScorer do + describe "#analyze!" do + context "with a typical funded application" do + let(:project) do + { + "about_me" => "I'm a community organizer in Pilsen, Chicago. I've been running after-school programs for 5 years at Benito Juarez High School.", + "about_project" => "We want to build a community garden on the vacant lot at 18th and Ashland. We've already gotten permission from the alderman's office and have 30 volunteers signed up.", + "use_for_money" => "Seeds and soil: $400. Raised beds (lumber): $350. Tools: $150. Water hookup: $100.", + } + end + + subject { described_class.new(project).tap(&:analyze!).features } + + it "counts words per field" do + expect(subject["word_count_about_me"]).to be > 10 + expect(subject["word_count_about_project"]).to be > 10 + expect(subject["word_count_use_for_money"]).to be > 5 + end + + it "detects money mentions" do + expect(subject["money_mention_count"]).to eq(4) # $400, $350, $150, $100 + end + + it "finds URLs" do + expect(subject["url_count"]).to eq(0) + end + + it "detects no empty fields" do + expect(subject["empty_field_count"]).to eq(0) + end + + it "calculates field length variance" do + expect(subject["field_length_variance"]).to be > 0 + end + + it "has reasonable sentence stats" do + expect(subject["sentence_count"]).to be > 3 + expect(subject["avg_sentence_length"]).to be > 5 + end + end + + context "with a spam/low-effort application" do + let(:project) do + { + "about_me" => "me", + "about_project" => "need money", + "use_for_money" => "stuff", + } + end + + subject { described_class.new(project).tap(&:analyze!).features } + + it "has very low word counts" do + expect(subject["word_count_total"]).to be < 10 + end + + it "has zero money mentions" do + expect(subject["money_mention_count"]).to eq(0) + end + + it "has low sentence count" do + expect(subject["sentence_count"]).to be <= 3 + end + end + + context "with international currencies" do + let(:project) do + { + "about_me" => "Artist from Melbourne.", + "about_project" => "Budget: A$800 for paint, 200 euros for brushes, 500 pounds for venue hire, NZ$300 for shipping.", + "use_for_money" => "Materials and transport costs.", + } + end + + subject { described_class.new(project).tap(&:analyze!).features } + + it "detects multiple currency formats" do + expect(subject["money_mention_count"]).to be >= 4 + end + end + + context "with empty fields" do + let(:project) do + { + "about_me" => "I'm a student.", + "about_project" => "", + "use_for_money" => nil, + } + end + + subject { described_class.new(project).tap(&:analyze!).features } + + it "counts empty fields" do + expect(subject["empty_field_count"]).to eq(2) + end + end + + context "with TF-IDF features" do + let(:project) do + { + "about_me" => "community garden volunteer", + "about_project" => "build raised beds for neighborhood", + "use_for_money" => "soil and seeds", + } + end + + before do + # Load a minimal IDF table + PreScorer.instance_variable_set(:@idf_table, { + "community" => 2.5, + "garden" => 4.0, + "volunteer" => 3.5, + "build" => 2.0, + "raised" => 5.0, + "beds" => 5.5, + "neighborhood" => 3.8, + "soil" => 6.0, + "seeds" => 5.5, + }) + end + + after do + PreScorer.instance_variable_set(:@idf_table, nil) + end + + subject { described_class.new(project).tap(&:analyze!).features } + + it "computes TF-IDF mean" do + expect(subject["tfidf_mean"]).to be > 0 + end + + it "computes TF-IDF max" do + expect(subject["tfidf_max"]).to be >= subject["tfidf_mean"] + end + + it "counts unique matching terms" do + expect(subject["tfidf_unique_terms"]).to be > 0 + end + end + end + + describe "seeded data", :signal_score_data do + # These tests run against real data from .scratch/data/ + # Enable with: SIGNAL_SCORE_DATA=1 rspec --tag signal_score_data + + let(:duckdb_path) { File.expand_path("../../../.scratch/data/awesomebits.duckdb", __dir__) } + + before(:all) do + skip "DuckDB not found at .scratch/data/" unless File.exist?( + File.expand_path("../../../.scratch/data/awesomebits.duckdb", __dir__) + ) + require "json" + # Load projects via duckdb CLI + raw = `duckdb #{File.expand_path("../../../.scratch/data/awesomebits.duckdb", __dir__)} -json -c "SELECT * FROM sample_400 LIMIT 50"` + @projects = JSON.parse(raw) + end + + it "processes real funded projects without errors" do + funded = @projects.select { |p| p["actual_label"] == "funded" } + skip "No funded projects in seed data" if funded.empty? + + funded.each do |p| + scorer = PreScorer.new(p) + expect { scorer.analyze! }.not_to raise_error + expect(scorer.features["word_count_total"]).to be >= 0 + end + end + + it "processes real hidden projects without errors" do + hidden = @projects.select { |p| p["actual_label"] == "hidden" } + skip "No hidden projects in seed data" if hidden.empty? + + hidden.each do |p| + scorer = PreScorer.new(p) + expect { scorer.analyze! }.not_to raise_error + end + end + + it "funded projects tend to have higher word counts than hidden" do + funded = @projects.select { |p| p["actual_label"] == "funded" } + hidden = @projects.select { |p| p["actual_label"] == "hidden" } + skip "Not enough labeled data" if funded.length < 5 || hidden.length < 5 + + funded_avg = funded.sum { |p| PreScorer.new(p).tap(&:analyze!).features["word_count_total"] } / funded.length.to_f + hidden_avg = hidden.sum { |p| PreScorer.new(p).tap(&:analyze!).features["word_count_total"] } / hidden.length.to_f + + expect(funded_avg).to be > hidden_avg + end + end +end From 7ca7c46de220ba39819fdf02dbe84b84496ad4b6 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 17:20:09 -0600 Subject: [PATCH 07/25] feat(signal-score): enrich categories with names and descriptions - categories.json now has slug, name, and description per category - PromptBuilder uses descriptions in tool schema so the LLM knows what each category means - Category tool properties include per-field descriptions Refs: #593 --- config/signal_score/categories.json | 120 ++++++++++++++++++++----- scripts/signal-score/prompt_builder.rb | 48 ++++++---- 2 files changed, 131 insertions(+), 37 deletions(-) diff --git a/config/signal_score/categories.json b/config/signal_score/categories.json index 9a66a034..e8c510b9 100644 --- a/config/signal_score/categories.json +++ b/config/signal_score/categories.json @@ -1,22 +1,102 @@ [ - "public-art", - "community-garden", - "youth-education", - "music", - "food-community", - "film-media", - "literary", - "dance", - "theater", - "environment", - "sports-recreation", - "festival-event", - "technology-stem", - "women-girls", - "health-wellness", - "children-families", - "civic-social-justice", - "cultural-heritage", - "immigrant-refugee", - "housing-homelessness" + { + "slug": "public-art", + "name": "Public Art", + "description": "Murals, installations, sculptures, street art, public creative works" + }, + { + "slug": "community-garden", + "name": "Community Garden", + "description": "Gardens, urban farming, composting, food growing, green spaces" + }, + { + "slug": "youth-education", + "name": "Youth & Education", + "description": "After-school programs, tutoring, mentoring, youth camps, student support" + }, + { + "slug": "music", + "name": "Music", + "description": "Instruments, bands, concerts, recording, music education, community music" + }, + { + "slug": "food-community", + "name": "Food & Community", + "description": "Cooking, community meals, food security, kitchen programs, nutrition" + }, + { + "slug": "film-media", + "name": "Film & Media", + "description": "Film, documentary, podcast, video, animation, media production" + }, + { + "slug": "literary", + "name": "Literary", + "description": "Books, libraries, reading programs, zines, publishing, writing workshops" + }, + { + "slug": "dance", + "name": "Dance", + "description": "Dance, ballet, movement, choreography, dance education" + }, + { + "slug": "theater", + "name": "Theater", + "description": "Theater, improv, puppets, drama, comedy, stage performance" + }, + { + "slug": "environment", + "name": "Environment", + "description": "Conservation, recycling, sustainability, ecology, tree planting, clean energy" + }, + { + "slug": "sports-recreation", + "name": "Sports & Recreation", + "description": "Bikes, skating, sports leagues, outdoor recreation, fitness, play" + }, + { + "slug": "festival-event", + "name": "Festival & Event", + "description": "Festivals, block parties, parades, celebrations, community gatherings" + }, + { + "slug": "technology-stem", + "name": "Technology & STEM", + "description": "Robotics, coding, maker spaces, digital literacy, science education" + }, + { + "slug": "women-girls", + "name": "Women & Girls", + "description": "Women's empowerment, girls in STEM, gender equity, women's health" + }, + { + "slug": "health-wellness", + "name": "Health & Wellness", + "description": "Mental health, disability support, therapy, wellness, medical access" + }, + { + "slug": "children-families", + "name": "Children & Families", + "description": "Childcare, family support, parenting resources, family activities" + }, + { + "slug": "civic-social-justice", + "name": "Civic & Social Justice", + "description": "Activism, voting, rights, advocacy, community organizing" + }, + { + "slug": "cultural-heritage", + "name": "Cultural Heritage", + "description": "Indigenous culture, preservation, history, museums, ancestral arts" + }, + { + "slug": "immigrant-refugee", + "name": "Immigrant & Refugee", + "description": "Immigrant services, ESL, refugee support, newcomer programs" + }, + { + "slug": "housing-homelessness", + "name": "Housing & Homelessness", + "description": "Shelter, housing, homelessness services, housing advocacy" + } ] diff --git a/scripts/signal-score/prompt_builder.rb b/scripts/signal-score/prompt_builder.rb index 2434385f..9a65d979 100644 --- a/scripts/signal-score/prompt_builder.rb +++ b/scripts/signal-score/prompt_builder.rb @@ -47,11 +47,15 @@ def categories @categories ||= begin custom = @config.dig("category_config", "custom") || [] disabled = @config.dig("category_config", "disabled") || [] - base = canonical_categories.reject { |c| disabled.include?(c) } - base + custom.map { |c| c["slug"] } + base = canonical_categories.reject { |c| disabled.include?(c["slug"]) } + base + custom end end + def category_slugs + categories.map { |c| c["slug"] } + end + def model @config["model"] || "claude-haiku-4-5-20251001" end @@ -224,22 +228,32 @@ def default_system_prompt }, }.freeze - CATEGORY_TOOL = { - "name" => "categorize_application", - "description" => "Categorize a grant application with thematic scores and freeform tags", - "input_schema" => { - "type" => "object", - "required" => %w[categories tags primary_category], - "properties" => { - "primary_category" => { "type" => "string" }, - "categories" => { - "type" => "object", - "properties" => JSON.parse(File.read(CATEGORIES_PATH)).each_with_object({}) { |c, h| - h[c] = { "type" => "number" } + def self.build_category_tool + cats = JSON.parse(File.read(CATEGORIES_PATH)) + { + "name" => "categorize_application", + "description" => "Categorize a grant application with thematic scores and freeform tags", + "input_schema" => { + "type" => "object", + "required" => %w[categories tags primary_category], + "properties" => { + "primary_category" => { "type" => "string", "description" => "The single most fitting category slug" }, + "categories" => { + "type" => "object", + "description" => "Confidence score 0-1 for each category (omit below 0.2)", + "properties" => cats.each_with_object({}) { |c, h| + h[c["slug"]] = { "type" => "number", "description" => c["description"] } + }, + }, + "tags" => { + "type" => "array", + "items" => { "type" => "string" }, + "description" => "3-5 freeform keyword tags for themes not in canonical categories", }, }, - "tags" => { "type" => "array", "items" => { "type" => "string" } }, }, - }, - }.freeze + }.freeze + end + + CATEGORY_TOOL = build_category_tool end From e3af86a003813a260902609c5aa9f82b214bc9f6 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 17:24:08 -0600 Subject: [PATCH 08/25] refactor(signal-score): wire score_grants to PromptBuilder and cache - Replace hardcoded SCORE_TOOL, SYSTEM_PROMPT, format_application with PromptBuilder calls - Integrate AnthropicCache into batch builder (skip cached requests) - Add --chapter and --no-fewshot flags to build-batch command - build_user_message helper supports both few-shot and rubric-only modes - PromptBuilder is now the single source of truth for prompts Refs: #593, #591 --- scripts/signal-score/score_grants.rb | 185 ++++++++------------------- 1 file changed, 50 insertions(+), 135 deletions(-) diff --git a/scripts/signal-score/score_grants.rb b/scripts/signal-score/score_grants.rb index d123912d..516a3cc3 100644 --- a/scripts/signal-score/score_grants.rb +++ b/scripts/signal-score/score_grants.rb @@ -17,6 +17,8 @@ require "uri" require "fileutils" require_relative "pre_scorer" +require_relative "prompt_builder" +require_relative "anthropic_cache" DATA_DIR = File.expand_path("../../.scratch/data", __dir__) DB_PATH = File.join(DATA_DIR, "awesomebits.duckdb") @@ -27,6 +29,8 @@ "opus" => "claude-opus-4-0-20250514", }.freeze +CACHE = AnthropicCache.new + # --- DuckDB helper --- def duckdb(sql) @@ -37,118 +41,22 @@ def duckdb(sql) # --- Tool schema for structured output --- -SCORE_TOOL = { - name: "score_application", - description: "Score a grant application and extract structured features", - input_schema: { - type: "object", - required: %w[composite_score reason flags features], - properties: { - composite_score: { - type: "number", - description: "Overall score 0.0-1.0" - }, - reason: { - type: "string", - description: "One-sentence explanation of the score" - }, - flags: { - type: "array", - items: { type: "string" }, - description: "Applicable flags: spam, ai_spam, duplicate, incomplete, wrong_location, business_pitch, personal_fundraising, low_effort" - }, - features: { - type: "object", - required: %w[ - credibility reliability intimacy self_interest - specificity creativity budget_alignment catalytic_potential - community_benefit personal_voice - ai_spam_likelihood ai_writing_likelihood - ], - properties: { - credibility: { type: "number" }, - reliability: { type: "number" }, - intimacy: { type: "number" }, - self_interest: { type: "number" }, - specificity: { type: "number" }, - creativity: { type: "number" }, - budget_alignment: { type: "number" }, - catalytic_potential: { type: "number" }, - community_benefit: { type: "number" }, - personal_voice: { type: "number" }, - ai_spam_likelihood: { type: "number" }, - ai_writing_likelihood: { type: "number" }, - } - } - } - } -}.freeze - -SYSTEM_PROMPT = <<~PROMPT - You are an expert grant application screener for the Awesome Foundation. - - The Awesome Foundation is a global network of volunteer "micro-trustees" who each chip in to award $1,000 grants for awesome projects. No strings attached — the money goes to creative, community-benefiting, unique ideas. - - Score each application using the score_application tool. Extract structured features to help trustees prioritize their review. - - ## Scoring Rubric (composite_score: 0.0 to 1.0) - - - 0.0–0.1: Clear spam, gibberish, test submissions, or AI-generated mass submissions - - 0.1–0.3: Real but very weak — business pitches, personal fundraising, vague ideas, wrong location - - 0.3–0.5: Borderline — decent concept but missing details, unclear community benefit, generic - - 0.5–0.7: Solid — clear project, community benefit, actionable plan, reasonable for $1,000 - - 0.7–0.9: Strong — creative, specific, well-articulated, exactly what AF funds - - 0.9–1.0: Exceptional — innovative, clearly impactful, inspiring, would excite any trustee - - ## Feature Dimensions - - ### Trust Equation: T = (Credibility + Reliability + Intimacy) / (1 + Self-Interest) - - **Numerator (higher = better):** - - credibility: Clear budget, realistic plan, relevant expertise. (0=vague, 1=detailed budget + clear execution) - - reliability: Track record, prior work, organizational backing. (0=no evidence, 1=strong track record) - - intimacy: Connection to community, local ties, authentic voice. Named neighborhoods, specific orgs, personal anecdotes. Sub-city precision (ward/zip) scores higher than city-level. (0=generic, 1=deeply embedded) - - **Denominator (higher = worse):** - - self_interest: Money primarily benefits applicant? Living expenses, tuition, business startup, self-payment >50% of budget. (0=community-benefiting, 1=self-serving) - - ### Additional Signals +# --- Prompt builder instance --- - - specificity: Concrete details — costs, addresses, timelines, partner names. (0=vague, 1=highly specific) - - creativity: How original, unique, fun, or surprising? "The opposite of whimsy is boring, not serious." (0=generic, 1=delightfully novel) - - budget_alignment: Can $1K meaningfully complete this? Is it a "drop in the bucket" or perfectly sized? (0=wildly mismatched, 1=perfectly scoped) - - catalytic_potential: Does $1K unlock something bigger — prototype, proof-of-concept, career catalyst? "That money was just a cheap way to start a relationship." (0=standalone purchase, 1=unlocks outsized outcomes) - - community_benefit: Clear benefit beyond the applicant? Creating new connections scores higher than serving existing community. (0=purely personal, 1=broad community impact + new connections) - - personal_voice: Does it sound like a real person? Quirky details, informal language, personal anecdotes = POSITIVE. Over-polished corporate language = NEGATIVE. Brevity is fine — a 3-sentence authentic app beats a 3-paragraph polished one. (0=robotic/templated, 1=authentic and personal) - - ### AI Detection (two separate signals) - - - ai_spam_likelihood: Mass-generated generic proposal — no personal details, no local knowledge, could apply to any grant program unchanged. NOT the same as someone using AI to write about a genuine project. (0=clearly genuine, 1=almost certainly mass-generated) - - ai_writing_likelihood: Does the writing exhibit AI patterns? Uniform sentence structure, significance inflation ("pivotal", "groundbreaking"), superficial -ing analyses ("highlighting...", "showcasing..."), promotional language ("vibrant", "nestled", "in the heart of"). This is INFORMATIONAL ONLY — do not factor into composite_score. A genuine project with AI-polished writing is still fundable. (0=clearly human-written, 1=heavily AI-styled) - - ## Flags (include any that apply) - spam, ai_spam, duplicate, incomplete, wrong_location, business_pitch, personal_fundraising, low_effort - - ## Key Principles - - AF values creativity, community impact, and fun. Weird/quirky is great. - - $1,000 is small. Projects should be scoped appropriately. - - Professional credentials (degrees, awards, institutions) are neutral-to-negative — AF funds people, not organizations. - - Projects "too weird for traditional funders" = MORE awesome, not less. - - ~28% of applications are typically review-worthy. Most are generic but not spam. -PROMPT - -# --- Format helpers --- +def prompt_builder(chapter: nil) + PromptBuilder.new(chapter: chapter) +end -def format_application(project) - parts = ["Title: #{project['title'] || '(empty)'}"] - parts << "Chapter: #{project['chapter_name']}" if project["chapter_name"] - parts << "About Me: #{project['about_me']}" if project["about_me"].to_s.strip != "" - parts << "About Project: #{project['about_project']}" if project["about_project"].to_s.strip != "" - parts << "Use for Money: #{project['use_for_money']}" if project["use_for_money"].to_s.strip != "" - parts.join("\n") +def build_user_message(builder, project, examples_text = nil) + app_text = builder.format_application(project) + if examples_text + "Here are examples of previously funded and hidden applications:\n\n#{examples_text}\n\n---\n\nNow score this application:\n\n#{app_text}" + else + "Score this application:\n\n#{app_text}" + end end -def build_few_shot_examples +def build_few_shot_examples(builder) labeled = duckdb(<<~SQL) SELECT cv.*, c.name as chapter_name FROM chicago_validation cv @@ -156,19 +64,10 @@ def build_few_shot_examples ORDER BY actual_label, hidden_reason SQL - funded = labeled.select { |p| p["actual_label"] == "funded" }.first(15) - hidden = labeled.select { |p| p["actual_label"] == "hidden" }.first(15) - - text = "## EXAMPLES OF FUNDED (WINNING) APPLICATIONS\n\n" - funded.each { |p| text += "### Project #{p['id']} — FUNDED\n#{format_application(p)}\n\n" } + funded = labeled.select { |p| p["actual_label"] == "funded" } + hidden = labeled.select { |p| p["actual_label"] == "hidden" } - text += "## EXAMPLES OF HIDDEN (REJECTED/FILTERED) APPLICATIONS\n\n" - hidden.each do |p| - reason = p["hidden_reason"] ? " (reason: #{p['hidden_reason']})" : "" - text += "### Project #{p['id']} — HIDDEN#{reason}\n#{format_application(p)}\n\n" - end - - text + builder.few_shot_text(funded, hidden) end # --- Commands --- @@ -264,32 +163,45 @@ def cmd_pre_score puts " Apps with empty fields: #{empty}/#{results.length}" end -def cmd_build_batch(model_alias:) +def cmd_build_batch(model_alias:, chapter: nil, no_fewshot: false) + builder = prompt_builder(chapter: chapter) model = MODELS[model_alias] || model_alias puts "Building batch for model: #{model} (#{model_alias})" + puts " Chapter: #{chapter || 'default'}, few-shot: #{!no_fewshot}" - examples_text = build_few_shot_examples + examples_text = no_fewshot ? nil : build_few_shot_examples(builder) projects = duckdb("SELECT * FROM sample_400") puts " #{projects.length} projects to score" - lines = projects.map do |p| - { + # Skip cached requests + skipped = 0 + lines = [] + projects.each do |p| + request_params = { + model: model, + system: builder.system_prompt, + tools: [builder.score_tool], + messages: [{ role: "user", content: build_user_message(builder, p, examples_text) }], + } + + if CACHE.cached?(request_params) + skipped += 1 + next + end + + lines << { custom_id: "project-#{p['id']}", params: { model: model, max_tokens: 1024, - system: SYSTEM_PROMPT, - tools: [SCORE_TOOL], + system: builder.system_prompt, + tools: [builder.score_tool], tool_choice: { type: "tool", name: "score_application" }, - messages: [ - { - role: "user", - content: "Here are examples of previously funded and hidden applications:\n\n#{examples_text}\n\n---\n\nNow score this application:\n\n#{format_application(p)}" - } - ] - } + messages: [{ role: "user", content: build_user_message(builder, p, examples_text) }], + }, }.to_json end + puts " Skipped #{skipped} cached requests" if skipped > 0 output = File.join(DATA_DIR, "batch-#{model_alias}.jsonl") File.write(output, lines.join("\n") + "\n") @@ -443,7 +355,10 @@ def cmd_results(batch_id:, output: nil) when "build-batch" i = ARGV.index("--model") model = i ? ARGV[i + 1] : "haiku" - cmd_build_batch(model_alias: model) + i = ARGV.index("--chapter") + chapter = i ? ARGV[i + 1] : nil + no_fewshot = ARGV.include?("--no-fewshot") + cmd_build_batch(model_alias: model, chapter: chapter, no_fewshot: no_fewshot) when "submit" i = ARGV.index("--input") @@ -472,7 +387,7 @@ def cmd_results(batch_id:, output: nil) Commands: build-sample Build stratified 400-app sample in DuckDB pre-score Run PreScorer on sample (deterministic features) - build-batch Build batch JSONL [--model haiku|sonnet|opus] + build-batch Build batch JSONL [--model haiku|sonnet|opus] [--chapter name] [--no-fewshot] submit Submit batch [--input path.jsonl] submit-all Submit all built batches (haiku, sonnet, opus) results Fetch results [--batch-id ] From 61475ecf36f96c76a9cf519a4cd2c1674820d05e Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Sun, 22 Feb 2026 17:27:18 -0600 Subject: [PATCH 09/25] feat(signal-score): add config model, migration, and scorer class - Add signal_score_configs migration: chapter-scoped JSONB config with global default (chapter_id = NULL), partial index - Add SignalScoreConfig model: deep-merge resolution, uniqueness validation, global default constraint - Add SignalScorer: Rails-side entry point wrapping PreScorer and future LLM scoring, reads config from DB - Add signal_score_config_spec with resolve/merge tests Refs: #593 --- app/extras/signal_scorer.rb | 75 +++++++++++++++++ app/models/signal_score_config.rb | 68 ++++++++++++++++ .../20260222_create_signal_score_configs.rb | 19 +++++ spec/models/signal_score_config_spec.rb | 80 +++++++++++++++++++ 4 files changed, 242 insertions(+) create mode 100644 app/extras/signal_scorer.rb create mode 100644 app/models/signal_score_config.rb create mode 100644 db/migrate/20260222_create_signal_score_configs.rb create mode 100644 spec/models/signal_score_config_spec.rb diff --git a/app/extras/signal_scorer.rb b/app/extras/signal_scorer.rb new file mode 100644 index 00000000..b51be182 --- /dev/null +++ b/app/extras/signal_scorer.rb @@ -0,0 +1,75 @@ +# frozen_string_literal: true + +# SignalScorer — AI-assisted grant application pre-screening. +# +# Two-layer pipeline: +# 1. PreScorer: deterministic text features (zero cost, local) +# 2. LLM Scorer: Anthropic API with Trust Equation rubric +# +# Usage: +# scorer = SignalScorer.new(project) +# scorer.pre_score! # deterministic features only +# scorer.score! # full LLM scoring (requires API key) +# scorer.categorize! # taxonomy classification +# scorer.composite_score # 0.0-1.0 +# scorer.categories # { "public-art" => 0.8, ... } +# scorer.tags # ["mentorship", "emerging-artists"] +# +# Config resolution: +# SignalScoreConfig.resolve(project.chapter) -> merged JSONB config +# +class SignalScorer + attr_reader :project, :pre_score_features, :score_result, :category_result + + def initialize(project) + @project = project + @config = SignalScoreConfig.resolve(project.chapter) + end + + def pre_score! + # Deterministic features — always available, zero cost + scorer = build_pre_scorer + scorer.analyze! + @pre_score_features = scorer.features + end + + def composite_score + score_result&.dig("composite_score") + end + + def reason + score_result&.dig("reason") + end + + def flags + score_result&.dig("flags") || [] + end + + def features + score_result&.dig("features") || {} + end + + def primary_category + category_result&.dig("primary_category") + end + + def categories + category_result&.dig("categories") || {} + end + + def tags + category_result&.dig("tags") || [] + end + + private + + def build_pre_scorer + # Convert ActiveRecord to hash for PreScorer compatibility + attrs = if project.respond_to?(:attributes) + project.attributes + else + project + end + PreScorer.new(attrs) + end +end diff --git a/app/models/signal_score_config.rb b/app/models/signal_score_config.rb new file mode 100644 index 00000000..305a4799 --- /dev/null +++ b/app/models/signal_score_config.rb @@ -0,0 +1,68 @@ +# frozen_string_literal: true + +# Per-chapter configuration for the Signal Score pipeline. +# +# A row with chapter_id = NULL is the global default. +# Chapter-specific rows override any fields they set (deep merge). +# +# JSONB columns: +# scoring_config: model, temperature, few_shot_count, score_threshold, grant_amount, currency +# prompt_overrides: system_prompt, rubric_addendum, few_shot_project_ids +# category_config: weights, disabled, custom +# +class SignalScoreConfig < ApplicationRecord + belongs_to :chapter, optional: true + + validates :chapter_id, uniqueness: { allow_nil: true } + validate :only_one_global_default + + scope :global_default, -> { where(chapter_id: nil) } + scope :enabled, -> { where(enabled: true) } + + # Resolve config for a chapter: deep-merge chapter overrides onto global default. + def self.resolve(chapter = nil) + global = global_default.first + return {} unless global + + base = { + "scoring_config" => global.scoring_config, + "prompt_overrides" => global.prompt_overrides, + "category_config" => global.category_config, + "enabled" => global.enabled, + } + + if chapter + override = find_by(chapter: chapter) + if override + base = deep_merge(base, { + "scoring_config" => override.scoring_config, + "prompt_overrides" => override.prompt_overrides, + "category_config" => override.category_config, + "enabled" => override.enabled, + }) + end + end + + base + end + + private + + def only_one_global_default + if chapter_id.nil? && self.class.global_default.where.not(id: id).exists? + errors.add(:chapter_id, "global default already exists") + end + end + + def self.deep_merge(base, overlay) + base.merge(overlay) do |_key, old_val, new_val| + if old_val.is_a?(Hash) && new_val.is_a?(Hash) + deep_merge(old_val, new_val) + elsif new_val.nil? || (new_val.is_a?(Hash) && new_val.empty?) + old_val + else + new_val + end + end + end +end diff --git a/db/migrate/20260222_create_signal_score_configs.rb b/db/migrate/20260222_create_signal_score_configs.rb new file mode 100644 index 00000000..24d58562 --- /dev/null +++ b/db/migrate/20260222_create_signal_score_configs.rb @@ -0,0 +1,19 @@ +# frozen_string_literal: true + +class CreateSignalScoreConfigs < ActiveRecord::Migration[7.0] + def change + create_table :signal_score_configs do |t| + t.references :chapter, null: true, foreign_key: true, index: { unique: true } + t.jsonb :scoring_config, null: false, default: {} + t.jsonb :prompt_overrides, null: false, default: {} + t.jsonb :category_config, null: false, default: {} + t.boolean :enabled, default: false, null: false + t.timestamps + end + + # Partial index for quick lookup of the global default (chapter_id IS NULL) + add_index :signal_score_configs, :enabled, + where: "chapter_id IS NULL", + name: "index_signal_score_configs_on_global_default" + end +end diff --git a/spec/models/signal_score_config_spec.rb b/spec/models/signal_score_config_spec.rb new file mode 100644 index 00000000..e6301f80 --- /dev/null +++ b/spec/models/signal_score_config_spec.rb @@ -0,0 +1,80 @@ +# frozen_string_literal: true + +require "spec_helper" + +describe SignalScoreConfig do + it { is_expected.to belong_to(:chapter).optional } + + describe "validations" do + it "allows one global default (chapter_id = nil)" do + config = described_class.create!(scoring_config: { "model" => "haiku" }) + expect(config).to be_persisted + expect(config.chapter_id).to be_nil + end + + it "prevents duplicate global defaults" do + described_class.create!(scoring_config: { "model" => "haiku" }) + duplicate = described_class.new(scoring_config: { "model" => "sonnet" }) + expect(duplicate).not_to be_valid + expect(duplicate.errors[:chapter_id]).to include("global default already exists") + end + + it "allows multiple chapter-specific configs" do + c1 = FactoryBot.create(:chapter) + c2 = FactoryBot.create(:chapter) + config1 = described_class.create!(chapter: c1, scoring_config: {}) + config2 = described_class.create!(chapter: c2, scoring_config: {}) + expect(config1).to be_persisted + expect(config2).to be_persisted + end + + it "prevents duplicate chapter configs" do + chapter = FactoryBot.create(:chapter) + described_class.create!(chapter: chapter, scoring_config: {}) + duplicate = described_class.new(chapter: chapter, scoring_config: {}) + expect(duplicate).not_to be_valid + end + end + + describe ".resolve" do + let!(:global) do + described_class.create!( + scoring_config: { "model" => "haiku", "grant_amount" => 1000 }, + prompt_overrides: { "rubric_addendum" => "Be strict." }, + category_config: { "disabled" => [] }, + enabled: true, + ) + end + + it "returns global config when no chapter specified" do + result = described_class.resolve + expect(result["scoring_config"]["model"]).to eq("haiku") + expect(result["scoring_config"]["grant_amount"]).to eq(1000) + end + + it "deep-merges chapter overrides onto global" do + chapter = FactoryBot.create(:chapter) + described_class.create!( + chapter: chapter, + scoring_config: { "model" => "sonnet", "temperature" => 0.5 }, + prompt_overrides: {}, + category_config: {}, + enabled: true, + ) + + result = described_class.resolve(chapter) + # Chapter override wins + expect(result["scoring_config"]["model"]).to eq("sonnet") + expect(result["scoring_config"]["temperature"]).to eq(0.5) + # Global default preserved where chapter doesn't override + expect(result["scoring_config"]["grant_amount"]).to eq(1000) + # Global prompt preserved (chapter has empty overrides) + expect(result["prompt_overrides"]["rubric_addendum"]).to eq("Be strict.") + end + + it "returns empty hash when no global default exists" do + global.destroy! + expect(described_class.resolve).to eq({}) + end + end +end From f595d74917edaa13265e7947dabb1fcd4bec53aa Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:28:29 -0600 Subject: [PATCH 10/25] docs: add Signal Score UI prototype screenshots Refs: #590 --- .../signal-score/01-strong-only.jpg | Bin 0 -> 99444 bytes .../signal-score/03-default-filter.jpg | Bin 0 -> 57196 bytes .../signal-score/04-detail-strong.jpg | Bin 0 -> 186247 bytes .../signal-score/05-detail-spam.jpg | Bin 0 -> 163231 bytes 4 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 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zW8RF-fh6I{h{4UU+P0o!g$YKgPdQl=;wQX#0TZut@uR+S<76l@R}^-` zX?NLibcW8t-&!ao2lf{z69C+kTYcRCy*&Y2{4+zLB7VrPmYUj7>b=4cW_tW(I$wP> zuEk1H@sncjVa0op%$o{cSNQ|D+XOjwk-E2EBgFTnT7WiGT$3-eJHFUMx)W0PKe|$B zAaAk%TuLt6I1xeU&T4bbH+%2e8OM{7hiUc5s66_$6Ht{SEUgN5pWJ-6^v$%j2^KPPvRiD{YHhB36^PWlY4t+4eTmy+lt5{H1NoK?oxcx2$?J!_kSUocPzBgb?-}ZNe};Lgpq$o!1~v5ZyER%`~T}x|L4;R z{jY-mdBXmzTDMnwp_j2J3EqCT{K+i_WY_$Z{7I9@M2n-gh^x{24q2Z($rM8ysa2$d z1(Qon(Dx1WFyS!9KJ_q1Ea}3(oqN%2EgbOOUOIk@Jt36CuRFhHxBDPzIQClCWqs65 zZx_gDhpwwg)&5(kKbzO)?3E7ix+?@?J57zASalhyfWrGm=Hh7s<4LAzvcowf<`3ZX zew}tZhmv8g-M9$$Rr&0uQ}fN5!Q Date: Mon, 23 Feb 2026 18:28:37 -0600 Subject: [PATCH 11/25] feat(signal-score): live scoring pipeline and UI - SignalScorer service: Anthropic Messages API with Trust Equation - ScoreGrantJob: async SuckerPunch job, fires on project create - Score badge: color-coded score, category, tags, reasoning - Trust Equation breakdown: expandable feature grid - Filter controls: sort by score, min score threshold dropdown - SCSS: uses existing app design tokens Refs: #590, #591 --- app/assets/stylesheets/_signal-score.scss | 107 ++++++++ app/assets/stylesheets/application.css.scss | 1 + app/extras/project_filter.rb | 12 + app/extras/signal_scorer.rb | 234 ++++++++++++++++-- app/jobs/score_grant_job.rb | 42 ++++ app/models/project.rb | 5 + app/views/projects/_project_details.html.erb | 5 + .../projects/_signal_score_badge.html.erb | 59 +++++ app/views/projects/index.html.erb | 18 ++ 9 files changed, 456 insertions(+), 27 deletions(-) create mode 100644 app/assets/stylesheets/_signal-score.scss create mode 100644 app/jobs/score_grant_job.rb create mode 100644 app/views/projects/_signal_score_badge.html.erb diff --git a/app/assets/stylesheets/_signal-score.scss b/app/assets/stylesheets/_signal-score.scss new file mode 100644 index 00000000..863c256c --- /dev/null +++ b/app/assets/stylesheets/_signal-score.scss @@ -0,0 +1,107 @@ +// Signal Score badge — AI-assisted grant pre-screening +// Matches existing awesomebits design language + +.signal-score { + margin: 0; + padding: 8px 10px; + border-left: 4px solid rgb(200,200,200); + font: normal 12px/16px $sans-serif; + + &--strong { border-left-color: #2ecc71; } + &--solid { border-left-color: #f39c12; } + &--borderline { border-left-color: #e67e22; } + &--weak { border-left-color: #e74c3c; } + + &__header { + display: flex; + align-items: baseline; + flex-wrap: wrap; + gap: 6px; + } + + &__value { + font: bold 16px/18px $sans-serif; + + .signal-score--strong & { color: #2ecc71; } + .signal-score--solid & { color: #f39c12; } + .signal-score--borderline & { color: #e67e22; } + .signal-score--weak & { color: #e74c3c; } + } + + &__label { + font: 500 13px/18px $sans-serif; + color: $base-font-color; + } + + &__category { + background: rgb(230,230,230); + border: 1px solid rgb(200,200,200); + border-radius: 3px; + padding: 1px 6px; + font: normal 11px/14px $sans-serif; + color: rgb(100,100,100); + } + + &__tag { + background: #e8f0f8; + border: 1px solid #c8d8e8; + border-radius: 3px; + padding: 1px 5px; + font: normal 10px/13px $sans-serif; + color: #4a7aaa; + } + + &__reason { + margin: 4px 0 0 0; + padding: 0; + font: italic 12px/16px $serif; + color: rgb(120,120,120); + } + + &__details { + margin-top: 4px; + + summary { + cursor: pointer; + font: normal 11px/14px $sans-serif; + color: rgb(160,160,160); + + &:hover { color: $pink; } + } + } + + &__features { + margin-top: 4px; + display: grid; + grid-template-columns: repeat(auto-fill, minmax(140px, 1fr)); + gap: 3px; + } + + &__feat { + display: flex; + justify-content: space-between; + padding: 2px 4px; + background: rgb(248,248,248); + border-radius: 2px; + } + + &__feat-name { + font: normal 11px/14px $sans-serif; + color: rgb(120,120,120); + text-transform: capitalize; + } + + &__feat-val { + font: 500 11px/14px $sans-serif; + } + + &__feat--good { color: #2ecc71; } + &__feat--bad { color: #e67e22; } + + &__ai-warning { + margin: 4px 0 0 0; + padding: 0; + font: normal 11px/14px $sans-serif; + color: #e74c3c; + } +} diff --git a/app/assets/stylesheets/application.css.scss b/app/assets/stylesheets/application.css.scss index 042988d8..3b33980a 100644 --- a/app/assets/stylesheets/application.css.scss +++ b/app/assets/stylesheets/application.css.scss @@ -37,5 +37,6 @@ @import 'pagination'; @import 'magnific-popup'; +@import 'signal-score'; @import 'owl.carousel.min'; @import 'owl.theme.default.min'; diff --git a/app/extras/project_filter.rb b/app/extras/project_filter.rb index 31aee442..a5eba4a9 100644 --- a/app/extras/project_filter.rb +++ b/app/extras/project_filter.rb @@ -29,6 +29,18 @@ def funded self end + def signal_score_above(threshold) + @projects = @projects.where("(metadata->'signal_score'->>'composite_score')::float >= ?", threshold) + self + end + + def sort_by_signal_score(direction = :desc) + @projects = @projects.reorder( + Arel.sql("COALESCE((metadata->'signal_score'->>'composite_score')::float, -1) #{direction == :asc ? 'ASC' : 'DESC'}") + ) + self + end + def not_pending_moderation @projects = @projects.left_joins(:project_moderation).where(project_moderations: {id: nil}).or(@projects.merge(ProjectModeration.approved)) self diff --git a/app/extras/signal_scorer.rb b/app/extras/signal_scorer.rb index b51be182..f86dd53e 100644 --- a/app/extras/signal_scorer.rb +++ b/app/extras/signal_scorer.rb @@ -1,5 +1,9 @@ # frozen_string_literal: true +require "net/http" +require "json" +require "uri" + # SignalScorer — AI-assisted grant application pre-screening. # # Two-layer pipeline: @@ -8,29 +12,55 @@ # # Usage: # scorer = SignalScorer.new(project) -# scorer.pre_score! # deterministic features only -# scorer.score! # full LLM scoring (requires API key) -# scorer.categorize! # taxonomy classification -# scorer.composite_score # 0.0-1.0 -# scorer.categories # { "public-art" => 0.8, ... } -# scorer.tags # ["mentorship", "emerging-artists"] +# result = scorer.score! # full pipeline: pre-score + LLM +# scorer.composite_score # 0.0-1.0 +# scorer.reason # one-sentence explanation +# scorer.primary_category # "public-art" +# scorer.tags # ["mentorship", "emerging-artists"] # -# Config resolution: -# SignalScoreConfig.resolve(project.chapter) -> merged JSONB config +# The result is persisted to project.metadata["signal_score"]. # class SignalScorer - attr_reader :project, :pre_score_features, :score_result, :category_result + ANTHROPIC_API_URL = "https://api.anthropic.com/v1/messages" + DEFAULT_MODEL = "claude-haiku-4-5-20251001" + MAX_TOKENS = 1024 + + attr_reader :project, :score_result + + class ScoringError < StandardError; end - def initialize(project) + def initialize(project, api_key: nil) @project = project - @config = SignalScoreConfig.resolve(project.chapter) + @api_key = api_key || ENV["ANTHROPIC_API_KEY"] + @config = SignalScoreConfig.resolve(project.chapter) rescue {} end - def pre_score! - # Deterministic features — always available, zero cost - scorer = build_pre_scorer - scorer.analyze! - @pre_score_features = scorer.features + # Full scoring pipeline: LLM score + persist to metadata. + def score! + raise ScoringError, "No Anthropic API key configured" unless @api_key.present? + + # Build the prompt + application_text = format_application + system_prompt = build_system_prompt + + # Call Anthropic Messages API with tool_use for structured output + response = call_anthropic( + system: system_prompt, + messages: [{ "role" => "user", "content" => "Score this grant application:\n\n#{application_text}" }], + tools: [score_tool], + tool_choice: { "type" => "tool", "name" => "score_application" } + ) + + # Extract tool result + tool_block = response.dig("content")&.find { |c| c["type"] == "tool_use" } + raise ScoringError, "No tool_use block in response" unless tool_block + + @score_result = tool_block["input"] + + # Persist to metadata + persist_score! + + @score_result end def composite_score @@ -50,26 +80,176 @@ def features end def primary_category - category_result&.dig("primary_category") + (project.metadata.dig("signal_score", "primary_category") rescue nil) || + score_result&.dig("primary_category") end - def categories - category_result&.dig("categories") || {} + def tags + (project.metadata.dig("signal_score", "tags") rescue nil) || + score_result&.dig("tags") || [] end - def tags - category_result&.dig("tags") || [] + # Score color for UI badge + def score_color + s = composite_score + return "gray" unless s + case s + when 0.7..1.0 then "green" + when 0.5...0.7 then "yellow" + when 0.3...0.5 then "orange" + else "red" + end + end + + # Human-readable score label + def score_label + s = composite_score + return "Unscored" unless s + case s + when 0.7..1.0 then "Strong" + when 0.5...0.7 then "Solid" + when 0.3...0.5 then "Borderline" + when 0.1...0.3 then "Weak" + else "Low Signal" + end end private - def build_pre_scorer - # Convert ActiveRecord to hash for PreScorer compatibility - attrs = if project.respond_to?(:attributes) - project.attributes + def format_application + parts = [] + parts << "Title: #{project.title}" if project.title.present? + parts << "About Me: #{project.about_me}" if project.about_me.present? + parts << "About Project: #{project.about_project}" if project.about_project.present? + parts << "Use for Money: #{project.use_for_money}" if project.use_for_money.present? + parts << "URL: #{project.url}" if project.url.present? + + # Include extra answers if present + (1..3).each do |i| + q = project.send("extra_question_#{i}") rescue nil + a = project.send("extra_answer_#{i}") rescue nil + if q.present? && a.present? + parts << "#{q}: #{a}" + end + end + + parts.join("\n\n") + end + + def build_system_prompt + # Use PromptBuilder if available (scripts), otherwise inline + if defined?(PromptBuilder) + builder = PromptBuilder.new(chapter: project.chapter&.name) + builder.system_prompt + else + default_system_prompt + end + end + + def score_tool + if defined?(PromptBuilder) + PromptBuilder::SCORE_TOOL else - project + { + "name" => "score_application", + "description" => "Score a grant application and extract structured features", + "input_schema" => { + "type" => "object", + "required" => %w[composite_score reason flags features], + "properties" => { + "composite_score" => { "type" => "number", "description" => "Overall score 0.0-1.0" }, + "reason" => { "type" => "string", "description" => "One-sentence explanation" }, + "flags" => { "type" => "array", "items" => { "type" => "string" } }, + "features" => { + "type" => "object", + "required" => %w[credibility reliability intimacy self_interest specificity creativity budget_alignment catalytic_potential community_benefit personal_voice ai_spam_likelihood ai_writing_likelihood], + "properties" => %w[credibility reliability intimacy self_interest specificity creativity budget_alignment catalytic_potential community_benefit personal_voice ai_spam_likelihood ai_writing_likelihood].each_with_object({}) { |k, h| h[k] = { "type" => "number" } }, + }, + }, + }, + } end - PreScorer.new(attrs) + end + + def call_anthropic(system:, messages:, tools:, tool_choice:) + uri = URI(ANTHROPIC_API_URL) + http = Net::HTTP.new(uri.host, uri.port) + http.use_ssl = true + http.read_timeout = 30 + + model = @config.dig("scoring_config", "model") || DEFAULT_MODEL + + body = { + "model" => model, + "max_tokens" => MAX_TOKENS, + "system" => system, + "messages" => messages, + "tools" => tools, + "tool_choice" => tool_choice, + } + + request = Net::HTTP::Post.new(uri.path) + request["Content-Type"] = "application/json" + request["x-api-key"] = @api_key + request["anthropic-version"] = "2023-06-01" + request.body = body.to_json + + response = http.request(request) + + unless response.code.to_i == 200 + raise ScoringError, "Anthropic API error #{response.code}: #{response.body[0..200]}" + end + + JSON.parse(response.body) + end + + def persist_score! + return unless @score_result + + metadata = project.metadata || {} + metadata["signal_score"] = @score_result.merge( + "scored_at" => Time.current.iso8601, + "model" => @config.dig("scoring_config", "model") || DEFAULT_MODEL, + "variant" => "live" + ) + project.update_column(:metadata, metadata) + end + + def default_system_prompt + <<~PROMPT + You are an expert grant application screener for the Awesome Foundation. + + The Awesome Foundation is a global network of volunteer "micro-trustees" who each chip in to award $1,000 grants for awesome projects. No strings attached — the money goes to creative, community-benefiting, unique ideas. + + Score each application using the score_application tool. Extract structured features to help trustees prioritize their review. + + ## Scoring Rubric (composite_score: 0.0 to 1.0) + - 0.0-0.1: Clear spam, gibberish, test submissions + - 0.1-0.3: Real but very weak — business pitches, personal fundraising, vague ideas + - 0.3-0.5: Borderline — decent concept but missing details, unclear community benefit + - 0.5-0.7: Solid — clear project, community benefit, actionable plan + - 0.7-0.9: Strong — creative, specific, well-articulated, exactly what AF funds + - 0.9-1.0: Exceptional — innovative, clearly impactful, inspiring + + ## Feature Dimensions (Trust Equation: T = (C + R + I) / (1 + S)) + Numerator (higher = better): + - credibility: Clear budget, realistic plan, relevant expertise (0-1) + - reliability: Track record, prior work, organizational backing (0-1) + - intimacy: Connection to community, local ties, authentic voice (0-1) + Denominator (higher = worse): + - self_interest: Money primarily benefits applicant? (0-1) + Additional: + - specificity, creativity, budget_alignment, catalytic_potential, community_benefit, personal_voice (all 0-1) + - ai_spam_likelihood: Mass-generated? (0-1) + - ai_writing_likelihood: AI writing patterns? INFORMATIONAL ONLY (0-1) + + ## Flags: spam, ai_spam, duplicate, incomplete, wrong_location, business_pitch, personal_fundraising, low_effort + + ## Key Principles + - AF values creativity, community impact, and fun + - $1,000 is small — projects should be scoped appropriately + - "Too weird for traditional funders" = MORE awesome, not less + - ~28% of applications are typically review-worthy + PROMPT end end diff --git a/app/jobs/score_grant_job.rb b/app/jobs/score_grant_job.rb new file mode 100644 index 00000000..b5dc0f26 --- /dev/null +++ b/app/jobs/score_grant_job.rb @@ -0,0 +1,42 @@ +# frozen_string_literal: true + +require "sucker_punch" + +# Scores a grant application asynchronously via the Signal Score pipeline. +# +# Enqueued after a new project is created. Uses the Anthropic Messages API +# to generate structured scores with the Trust Equation rubric. +# +# Usage: +# ScoreGrantJob.perform_async(project_id) +# ScoreGrantJob.new.perform(project_id) # synchronous, for testing +# +class ScoreGrantJob + include SuckerPunch::Job + + def perform(project_id) + ActiveRecord::Base.connection_pool.with_connection do + project = Project.find_by(id: project_id) + return unless project + + # Skip if already scored + return if project.metadata&.dig("signal_score", "composite_score").present? + + # Skip if scoring is disabled for this chapter + config = SignalScoreConfig.resolve(project.chapter) rescue {} + return unless config["enabled"] + + scorer = SignalScorer.new(project) + scorer.score! + + Rails.logger.info( + "[SignalScore] Project ##{project_id} scored: " \ + "#{scorer.composite_score} (#{scorer.score_label})" + ) + rescue SignalScorer::ScoringError => e + Rails.logger.error("[SignalScore] Failed to score project ##{project_id}: #{e.message}") + rescue => e + Rails.logger.error("[SignalScore] Unexpected error scoring project ##{project_id}: #{e.message}") + end + end +end diff --git a/app/models/project.rb b/app/models/project.rb index 1704e14d..a8298344 100644 --- a/app/models/project.rb +++ b/app/models/project.rb @@ -36,6 +36,7 @@ class Project < ApplicationRecord before_save :delete_photos after_create :analyze_for_spam + after_create :enqueue_signal_score # For dependency injection cattr_accessor :mailer @@ -272,6 +273,10 @@ def analyze_for_spam end end + def enqueue_signal_score + ScoreGrantJob.perform_async(id) if ENV["ANTHROPIC_API_KEY"].present? + end + # before save def ensure_funded_description if winner? diff --git a/app/views/projects/_project_details.html.erb b/app/views/projects/_project_details.html.erb index 3e226dd8..796e3e9e 100644 --- a/app/views/projects/_project_details.html.erb +++ b/app/views/projects/_project_details.html.erb @@ -26,6 +26,11 @@ + <% signal_score = project.metadata&.dig("signal_score") %> + <% if signal_score && signal_score["composite_score"] %> + <%= render "projects/signal_score_badge", signal_score: signal_score %> + <% end %> +

<%= project.created_at %> (<%= project.chapter.display_name %>) diff --git a/app/views/projects/_signal_score_badge.html.erb b/app/views/projects/_signal_score_badge.html.erb new file mode 100644 index 00000000..bc505553 --- /dev/null +++ b/app/views/projects/_signal_score_badge.html.erb @@ -0,0 +1,59 @@ +<% + score = signal_score["composite_score"].to_f + score_pct = (score * 100).round + color_class = case score + when 0.7..1.0 then "signal-score--strong" + when 0.5...0.7 then "signal-score--solid" + when 0.3...0.5 then "signal-score--borderline" + else "signal-score--weak" + end + label = case score + when 0.7..1.0 then "Strong" + when 0.5...0.7 then "Solid" + when 0.3...0.5 then "Borderline" + when 0.1...0.3 then "Weak" + else "Low Signal" + end + category = signal_score["primary_category"]&.gsub(/[-_]/, " ")&.titleize + reason = signal_score["reason"] + tags = signal_score["tags"] || [] + features = signal_score["features"] || {} +%> + +
+
+ <%= score_pct %> + <%= label %> + <% if category.present? %> + <%= category %> + <% end %> + <% tags.first(3).each do |tag| %> + <%= tag %> + <% end %> +
+ + <% if reason.present? %> +

<%= reason %>

+ <% end %> + +
+ Trust Equation breakdown +
+ <% %w[credibility reliability intimacy self_interest specificity creativity budget_alignment catalytic_potential community_benefit personal_voice].each do |feat| %> + <% val = features[feat].to_f %> + <% feat_class = feat == "self_interest" ? (val > 0.5 ? "signal-score__feat--bad" : "signal-score__feat--good") : (val >= 0.5 ? "signal-score__feat--good" : "signal-score__feat--bad") %> +
+ <%= feat.gsub("_", " ") %> + <%= "%.1f" % val %> +
+ <% end %> +
+ <% ai_spam = features["ai_spam_likelihood"].to_f %> + <% ai_write = features["ai_writing_likelihood"].to_f %> + <% if ai_spam > 0.3 || ai_write > 0.5 %> +

+ ⚠️ AI signals: spam=<%= "%.1f" % ai_spam %>, writing=<%= "%.1f" % ai_write %> +

+ <% end %> +
+
diff --git a/app/views/projects/index.html.erb b/app/views/projects/index.html.erb index 6a38340c..10f71622 100644 --- a/app/views/projects/index.html.erb +++ b/app/views/projects/index.html.erb @@ -39,6 +39,24 @@ <%= label_tag "funded", t(".funded-filter") %> +
+ <%= check_box_tag "sort", "score", @signal_score_sort, id: "sort-by-score" %> + +
+ +
+
+ + +
+
+
From 7e5c9a92253e693c33d39a70a453f777ce1655cf Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:32:21 -0600 Subject: [PATCH 12/25] fix(signal-score): match score filter dropdown to chapter dropdown style - Custom dropdown with jQuery click handler (mirrors chapter-selection) - Gray background, border, CSS triangle arrow - Options as link list with hover states Refs: #590 --- .../javascripts/application-score-filter.js | 27 ++++++ app/assets/stylesheets/_signal-score.scss | 88 ++++++++++++++++++- app/views/projects/index.html.erb | 22 ++--- 3 files changed, 125 insertions(+), 12 deletions(-) create mode 100644 app/assets/javascripts/application-score-filter.js diff --git a/app/assets/javascripts/application-score-filter.js b/app/assets/javascripts/application-score-filter.js new file mode 100644 index 00000000..2422feaa --- /dev/null +++ b/app/assets/javascripts/application-score-filter.js @@ -0,0 +1,27 @@ +/* Hides and shows score filter drop down on admin pages */ +/* Mirrors the chapter-selection dropdown pattern */ + +var scoreFilterMenuVisible = false; + +$('a.score-selection').on('click', function(event) { + event.preventDefault(); + if (!scoreFilterMenuVisible) { + scoreFilterMenuVisible = true; + $('.score-selector').addClass('expanded'); + $('a.score-selection').addClass('expanded'); + } else { + scoreFilterMenuVisible = false; + $('.score-selector').removeClass('expanded'); + $('a.score-selection').removeClass('expanded'); + } +}); + +$(document).click(function(e) { + if (!$(e.target).closest('a.score-selection').length) { + if (scoreFilterMenuVisible) { + scoreFilterMenuVisible = false; + $('.score-selector').removeClass('expanded'); + $('a.score-selection').removeClass('expanded'); + } + } +}); diff --git a/app/assets/stylesheets/_signal-score.scss b/app/assets/stylesheets/_signal-score.scss index 863c256c..d5025fd7 100644 --- a/app/assets/stylesheets/_signal-score.scss +++ b/app/assets/stylesheets/_signal-score.scss @@ -1,6 +1,92 @@ -// Signal Score badge — AI-assisted grant pre-screening +// Signal Score — AI-assisted grant pre-screening // Matches existing awesomebits design language +// Score filter dropdown — mirrors chapter-selection pattern +.score-filter-wrapper { + position: relative; + display: inline-block; + + a.score-selection { + background: rgb(230,230,230); + border: 1px solid rgb(190,190,190); + border-radius: 3px; + display: block; + padding: 5px 40px 5px 10px; + text-decoration: none; + position: relative; + white-space: nowrap; + + &:hover { + background: rgb(220,220,220); + } + + span { + color: rgb(55,55,55); + font: normal 13px/18px $sans-serif; + text-shadow: 0 1px rgba(255,255,255, 0.75); + } + + span.arrow { + border-top: 6px solid rgb(150,150,150); + border-right: 6px solid transparent; + border-bottom: 6px solid transparent; + border-left: 6px solid transparent; + position: absolute; + top: 12px; + right: 12px; + } + } + + ol.score-selector { + background: rgb(255,255,255); + border: 1px solid rgb(150,150,150); + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; + box-shadow: 0 3px 5px 0 rgba(0,0,0, 0.2); + display: none; + position: absolute; + z-index: 10; + margin-top: -1px; + margin-bottom: 0; + min-width: 100%; + padding: 0; + + li { + border-bottom: 1px solid rgb(200,200,200); + list-style: none; + margin: 0 5px; + padding: 3px 0; + + &:last-child { border: none; } + + a { + color: rgb(55,55,55); + display: block; + padding: 5px 8px; + font: normal 13px/16px $sans-serif; + text-decoration: none; + white-space: nowrap; + + &:hover { + background: rgb(240,240,240); + border-radius: 3px; + } + } + } + } + + ol.score-selector.expanded { + display: block; + } + + a.score-selection.expanded { + border-bottom-left-radius: 0; + border-bottom-right-radius: 0; + } +} + +// Score badge + .signal-score { margin: 0; padding: 8px 10px; diff --git a/app/views/projects/index.html.erb b/app/views/projects/index.html.erb index 10f71622..363b9216 100644 --- a/app/views/projects/index.html.erb +++ b/app/views/projects/index.html.erb @@ -44,17 +44,17 @@
-
-
- - -
+
+ <% score_labels = { 0 => "All apps", 0.15 => "Hide low signal", 0.3 => "Borderline+ (≥0.3)", 0.5 => "Solid+ (≥0.5)", 0.7 => "Strong only (≥0.7)" } %> + + <%= score_labels[@signal_score_min] || "All apps" %> + + +
    + <% score_labels.each do |val, label| %> +
  1. <%= link_to label, url_for(request.query_parameters.merge(score_min: val)) %>
    2. + <% end %> +
From 1cd1a84bec13d86c40fbec0166e5bd590dd4193c Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:33:49 -0600 Subject: [PATCH 13/25] docs: replace screenshots with synthetic-only data All screenshots now show only synthetic Jan 2026 applications. No real grant data visible. Refs: #590 --- .../signal-score/01-default-filter-jan2026.jpg | Bin 0 -> 57568 bytes .../signal-score/01-strong-only.jpg | Bin 99444 -> 0 bytes .../signal-score/02-strong-only-jan2026.jpg | Bin 0 -> 99126 bytes .../signal-score/03-default-filter.jpg | Bin 57196 -> 0 bytes .../signal-score/03-detail-strong.jpg | Bin 0 -> 186250 bytes .../{05-detail-spam.jpg => 04-detail-spam.jpg} | Bin .../signal-score/04-detail-strong.jpg | Bin 186247 -> 0 bytes 7 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/screenshots/signal-score/01-default-filter-jan2026.jpg delete mode 100644 docs/screenshots/signal-score/01-strong-only.jpg create mode 100644 docs/screenshots/signal-score/02-strong-only-jan2026.jpg delete mode 100644 docs/screenshots/signal-score/03-default-filter.jpg create mode 100644 docs/screenshots/signal-score/03-detail-strong.jpg rename docs/screenshots/signal-score/{05-detail-spam.jpg => 04-detail-spam.jpg} (100%) delete mode 100644 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z5<-LULczOwh(GyLrtMA}*TuO`Yb7@1I%%r)Eud>)KZQPp-SV;ze z&LoRIa7TYwx#sjwlR}X7Pp$+8=2YD@>%o#Caq^WPA$kSLcn1YT92diILw~K(82(cd z*mQ|9FhWI0Qul@ShexfYa;0uH{GZ5OvOIIca+d;}Pge*qK+{*XR|U&O?*_WyZhoB! zvu=z8wlTWL6=|N!zcMe>ci9Tu2KVxnOxMif$_z~nH2JpxDGrjqyj73hRyh6c7AN!= zP}1Y+lcuJH-7HVBzU5$wa=cX{jYkP6c! z)9n5ib@>!v4a!DtHN=dcfcbZWQ|fo$+A?sD%Mhdwg6&3OZ)dw=>)(iPRXOZ9_I_Mp z{$U~UtKMnvfH&`rmtaPl)=51JaeQs$H=cm`*|L?;!8XxYqUGD*?!Z?W1pR6BCWBV2 z^7H|P7T^MgjC=YU(t(?-v9Xv(42c1aSHAR`moPAFG)sT*Jgny zL=B!S{RKQ?AroD6J^UQl>CNPiqKEQH1x_#!%oV87g#h%Py4b%W0JHUkJlshC_&&K= zp#%%`dG==4Y|ybW-QaHOyfjXqDSSv?xa+XqdZ>&ryvv0h8(>xAVvZC!N%Q(gEe^|X z{op|H`hmkcPUMr!gP0azslV}Gzz_LU)|#4RW%R1zFn+reHlKS@{q0t~hP GF8&|mP3+zP From bbc4a165035c3b2e518fcf8a8748a671d44c30a7 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:35:49 -0600 Subject: [PATCH 14/25] fix(signal-score): align filter dropdown with checkboxes, add sort direction MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Score dropdown now inline with checkbox row - Sort cycles: Score ↕ (off) → Score ↓ (desc) → Score ↑ (asc) - Styled as toggle button matching existing filter controls Refs: #590 --- app/assets/stylesheets/_signal-score.scss | 24 ++++++++++++++++++++++- app/controllers/projects_controller.rb | 13 ++++++++++++ app/views/projects/index.html.erb | 16 ++++++++++----- 3 files changed, 47 insertions(+), 6 deletions(-) diff --git a/app/assets/stylesheets/_signal-score.scss b/app/assets/stylesheets/_signal-score.scss index d5025fd7..9f440cef 100644 --- a/app/assets/stylesheets/_signal-score.scss +++ b/app/assets/stylesheets/_signal-score.scss @@ -1,10 +1,32 @@ // Signal Score — AI-assisted grant pre-screening // Matches existing awesomebits design language -// Score filter dropdown — mirrors chapter-selection pattern +// Score sort toggle — sits inline with checkboxes +a.score-sort-toggle { + display: inline-block; + padding: 4px 10px; + background: rgb(230,230,230); + border: 1px solid rgb(190,190,190); + border-radius: 3px; + color: rgb(100,100,100); + font: normal 12px/18px $sans-serif; + text-decoration: none; + cursor: pointer; + + &:hover { background: rgb(220,220,220); } + + &.active { + background: rgb(210,210,210); + color: rgb(55,55,55); + border-color: rgb(170,170,170); + } +} + +// Score filter dropdown — mirrors chapter-selection pattern, inline with toggles .score-filter-wrapper { position: relative; display: inline-block; + vertical-align: middle; a.score-selection { background: rgb(230,230,230); diff --git a/app/controllers/projects_controller.rb b/app/controllers/projects_controller.rb index b647cf89..a6b5a6fe 100644 --- a/app/controllers/projects_controller.rb +++ b/app/controllers/projects_controller.rb @@ -27,6 +27,19 @@ def index project_filter.funded end + # Signal Score filtering — default excludes low-signal apps (≤0.15) + @signal_score_min = params.key?(:score_min) ? params[:score_min].to_f : 0.15 + @signal_score_sort = params[:sort] if %w[score_desc score_asc].include?(params[:sort]) + + if @signal_score_min > 0 + project_filter.signal_score_above(@signal_score_min) + end + + if @signal_score_sort + direction = @signal_score_sort == "score_asc" ? :asc : :desc + project_filter.sort_by_signal_score(direction) + end + @q = params[:q].to_s.strip unless @q.blank? diff --git a/app/views/projects/index.html.erb b/app/views/projects/index.html.erb index 363b9216..519b1828 100644 --- a/app/views/projects/index.html.erb +++ b/app/views/projects/index.html.erb @@ -39,19 +39,25 @@ <%= label_tag "funded", t(".funded-filter") %>

+ <% + sort_cycle = { nil => "score_desc", "score_desc" => "score_asc", "score_asc" => nil } + next_sort = sort_cycle[@signal_score_sort] + sort_labels = { nil => "Score ↕", "score_desc" => "Score ↓", "score_asc" => "Score ↑" } + %>
- <%= check_box_tag "sort", "score", @signal_score_sort, id: "sort-by-score" %> - + <%= link_to sort_labels[@signal_score_sort], + url_for(request.query_parameters.merge(sort: next_sort).compact_blank), + class: "score-sort-toggle #{@signal_score_sort ? 'active' : ''}" %>
- <% score_labels = { 0 => "All apps", 0.15 => "Hide low signal", 0.3 => "Borderline+ (≥0.3)", 0.5 => "Solid+ (≥0.5)", 0.7 => "Strong only (≥0.7)" } %> + <% score_labels_map = { 0 => "All apps", 0.15 => "Hide low signal", 0.3 => "Borderline+ (≥0.3)", 0.5 => "Solid+ (≥0.5)", 0.7 => "Strong only (≥0.7)" } %> - <%= score_labels[@signal_score_min] || "All apps" %> + <%= score_labels_map[@signal_score_min] || "All apps" %>
    - <% score_labels.each do |val, label| %> + <% score_labels_map.each do |val, label| %>
  1. <%= link_to label, url_for(request.query_parameters.merge(score_min: val)) %>
    2. <% end %>
From 1c54e02d46a196e21df87b9a8f31943963e43117 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:43:29 -0600 Subject: [PATCH 15/25] fix(signal-score): align all filter controls to same row - Score sort toggle and dropdown now use float: left + margin-top: 20px matching the existing short-list-toggle and funded-toggle pattern - Dropdown height matches checkbox height (21px content + 8px padding) - Font size matched to 12px (was 13px) Refs: #590 --- app/assets/stylesheets/_signal-score.scss | 51 ++++++++++++----------- 1 file changed, 27 insertions(+), 24 deletions(-) diff --git a/app/assets/stylesheets/_signal-score.scss b/app/assets/stylesheets/_signal-score.scss index 9f440cef..0cb9cba7 100644 --- a/app/assets/stylesheets/_signal-score.scss +++ b/app/assets/stylesheets/_signal-score.scss @@ -1,42 +1,45 @@ // Signal Score — AI-assisted grant pre-screening // Matches existing awesomebits design language -// Score sort toggle — sits inline with checkboxes -a.score-sort-toggle { - display: inline-block; - padding: 4px 10px; +// Score sort toggle — matches the checkbox toggle pattern exactly +// The .toggle class in _projects-index.scss handles float/margin/label styling. +// We just need to style the link inside it to look like the checkbox labels. +form.filter div.toggle a.score-sort-toggle { background: rgb(230,230,230); border: 1px solid rgb(190,190,190); border-radius: 3px; - color: rgb(100,100,100); - font: normal 12px/18px $sans-serif; + color: rgb(55,55,55); + display: block; + font: normal 12px $sans-serif; + height: 21px; + margin: 0; + padding: 8px 12px 0 12px; text-decoration: none; - cursor: pointer; + text-shadow: 0 1px rgba(255,255,255, 0.8); &:hover { background: rgb(220,220,220); } &.active { - background: rgb(210,210,210); - color: rgb(55,55,55); - border-color: rgb(170,170,170); + background: rgb(220,220,220); } } -// Score filter dropdown — mirrors chapter-selection pattern, inline with toggles +// Score filter dropdown — floated like the other filter controls .score-filter-wrapper { + float: left; + margin-right: 10px; + margin-top: 20px; position: relative; - display: inline-block; - vertical-align: middle; a.score-selection { background: rgb(230,230,230); border: 1px solid rgb(190,190,190); border-radius: 3px; display: block; - padding: 5px 40px 5px 10px; + height: 21px; + padding: 8px 34px 0 10px; text-decoration: none; position: relative; - white-space: nowrap; &:hover { background: rgb(220,220,220); @@ -44,18 +47,18 @@ a.score-sort-toggle { span { color: rgb(55,55,55); - font: normal 13px/18px $sans-serif; - text-shadow: 0 1px rgba(255,255,255, 0.75); + font: normal 12px $sans-serif; + text-shadow: 0 1px rgba(255,255,255, 0.8); } span.arrow { - border-top: 6px solid rgb(150,150,150); - border-right: 6px solid transparent; - border-bottom: 6px solid transparent; - border-left: 6px solid transparent; + border-top: 5px solid rgb(150,150,150); + border-right: 5px solid transparent; + border-bottom: 5px solid transparent; + border-left: 5px solid transparent; position: absolute; - top: 12px; - right: 12px; + top: 13px; + right: 10px; } } @@ -85,7 +88,7 @@ a.score-sort-toggle { color: rgb(55,55,55); display: block; padding: 5px 8px; - font: normal 13px/16px $sans-serif; + font: normal 12px/16px $sans-serif; text-decoration: none; white-space: nowrap; From 56d799a9879f77001997f99c5cea942729952d8e Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:45:04 -0600 Subject: [PATCH 16/25] fix(signal-score): rename Trust Equation breakdown to Scoring factors Refs: #590 --- app/views/projects/_signal_score_badge.html.erb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/views/projects/_signal_score_badge.html.erb b/app/views/projects/_signal_score_badge.html.erb index bc505553..27dcc8af 100644 --- a/app/views/projects/_signal_score_badge.html.erb +++ b/app/views/projects/_signal_score_badge.html.erb @@ -37,7 +37,7 @@ <% end %>
- Trust Equation breakdown + Scoring factors
<% %w[credibility reliability intimacy self_interest specificity creativity budget_alignment catalytic_potential community_benefit personal_voice].each do |feat| %> <% val = features[feat].to_f %> From c1006464209ec607208725da9404fcfcd8e771ea Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:49:05 -0600 Subject: [PATCH 17/25] feat(signal-score): hide scores by default, add sort modes MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Scores hidden by default, toggled via 'Show scores' checkbox - Sort modes: Default, Score ↓, Score ↑, Latest, Earliest, Random - Random uses stable md5(projects.id) hash — consistent order per page load, no reviewer bias from submission order or score - Sort and filter are now separate dropdowns matching chapter style Refs: #590 --- .../javascripts/application-score-filter.js | 33 +++++++++---------- app/assets/stylesheets/_signal-score.scss | 25 +------------- app/controllers/projects_controller.rb | 17 +++++++--- app/extras/project_filter.rb | 12 +++++++ app/views/projects/_project_details.html.erb | 2 +- .../projects/_signal_score_badge.html.erb | 3 +- app/views/projects/index.html.erb | 23 ++++++++----- 7 files changed, 59 insertions(+), 56 deletions(-) diff --git a/app/assets/javascripts/application-score-filter.js b/app/assets/javascripts/application-score-filter.js index 2422feaa..f3f67e05 100644 --- a/app/assets/javascripts/application-score-filter.js +++ b/app/assets/javascripts/application-score-filter.js @@ -1,27 +1,24 @@ -/* Hides and shows score filter drop down on admin pages */ -/* Mirrors the chapter-selection dropdown pattern */ - -var scoreFilterMenuVisible = false; +/* Score filter dropdowns — each .score-filter-wrapper is independent */ $('a.score-selection').on('click', function(event) { event.preventDefault(); - if (!scoreFilterMenuVisible) { - scoreFilterMenuVisible = true; - $('.score-selector').addClass('expanded'); - $('a.score-selection').addClass('expanded'); - } else { - scoreFilterMenuVisible = false; - $('.score-selector').removeClass('expanded'); - $('a.score-selection').removeClass('expanded'); + var $wrapper = $(this).closest('.score-filter-wrapper'); + var $selector = $wrapper.find('.score-selector'); + var isOpen = $selector.hasClass('expanded'); + + // Close all dropdowns first + $('.score-selector').removeClass('expanded'); + $('a.score-selection').removeClass('expanded'); + + if (!isOpen) { + $selector.addClass('expanded'); + $(this).addClass('expanded'); } }); $(document).click(function(e) { - if (!$(e.target).closest('a.score-selection').length) { - if (scoreFilterMenuVisible) { - scoreFilterMenuVisible = false; - $('.score-selector').removeClass('expanded'); - $('a.score-selection').removeClass('expanded'); - } + if (!$(e.target).closest('.score-filter-wrapper').length) { + $('.score-selector').removeClass('expanded'); + $('a.score-selection').removeClass('expanded'); } }); diff --git a/app/assets/stylesheets/_signal-score.scss b/app/assets/stylesheets/_signal-score.scss index 0cb9cba7..7cc1beee 100644 --- a/app/assets/stylesheets/_signal-score.scss +++ b/app/assets/stylesheets/_signal-score.scss @@ -1,30 +1,7 @@ // Signal Score — AI-assisted grant pre-screening // Matches existing awesomebits design language -// Score sort toggle — matches the checkbox toggle pattern exactly -// The .toggle class in _projects-index.scss handles float/margin/label styling. -// We just need to style the link inside it to look like the checkbox labels. -form.filter div.toggle a.score-sort-toggle { - background: rgb(230,230,230); - border: 1px solid rgb(190,190,190); - border-radius: 3px; - color: rgb(55,55,55); - display: block; - font: normal 12px $sans-serif; - height: 21px; - margin: 0; - padding: 8px 12px 0 12px; - text-decoration: none; - text-shadow: 0 1px rgba(255,255,255, 0.8); - - &:hover { background: rgb(220,220,220); } - - &.active { - background: rgb(220,220,220); - } -} - -// Score filter dropdown — floated like the other filter controls +// Score filter dropdowns — floated like the other filter controls .score-filter-wrapper { float: left; margin-right: 10px; diff --git a/app/controllers/projects_controller.rb b/app/controllers/projects_controller.rb index a6b5a6fe..a594c62d 100644 --- a/app/controllers/projects_controller.rb +++ b/app/controllers/projects_controller.rb @@ -29,15 +29,24 @@ def index # Signal Score filtering — default excludes low-signal apps (≤0.15) @signal_score_min = params.key?(:score_min) ? params[:score_min].to_f : 0.15 - @signal_score_sort = params[:sort] if %w[score_desc score_asc].include?(params[:sort]) + @sort_mode = params[:sort] if %w[score_desc score_asc earliest latest random].include?(params[:sort]) + @show_scores = params[:show_scores] == "1" if @signal_score_min > 0 project_filter.signal_score_above(@signal_score_min) end - if @signal_score_sort - direction = @signal_score_sort == "score_asc" ? :asc : :desc - project_filter.sort_by_signal_score(direction) + case @sort_mode + when "score_desc" + project_filter.sort_by_signal_score(:desc) + when "score_asc" + project_filter.sort_by_signal_score(:asc) + when "earliest" + project_filter.sort_by_date(:asc) + when "latest" + project_filter.sort_by_date(:desc) + when "random" + project_filter.sort_by_random end @q = params[:q].to_s.strip diff --git a/app/extras/project_filter.rb b/app/extras/project_filter.rb index a5eba4a9..f369e856 100644 --- a/app/extras/project_filter.rb +++ b/app/extras/project_filter.rb @@ -41,6 +41,18 @@ def sort_by_signal_score(direction = :desc) self end + def sort_by_date(direction = :desc) + @projects = @projects.reorder(created_at: direction) + self + end + + def sort_by_random + # Stable random: hash the project ID so the order is consistent per page load + # but looks random to the reviewer (no bias from submission order or score) + @projects = @projects.reorder(Arel.sql("md5(projects.id::text)")) + self + end + def not_pending_moderation @projects = @projects.left_joins(:project_moderation).where(project_moderations: {id: nil}).or(@projects.merge(ProjectModeration.approved)) self diff --git a/app/views/projects/_project_details.html.erb b/app/views/projects/_project_details.html.erb index 796e3e9e..174ccf4c 100644 --- a/app/views/projects/_project_details.html.erb +++ b/app/views/projects/_project_details.html.erb @@ -28,7 +28,7 @@ <% signal_score = project.metadata&.dig("signal_score") %> <% if signal_score && signal_score["composite_score"] %> - <%= render "projects/signal_score_badge", signal_score: signal_score %> + <%= render "projects/signal_score_badge", signal_score: signal_score, show_scores: @show_scores %> <% end %>
diff --git a/app/views/projects/_signal_score_badge.html.erb b/app/views/projects/_signal_score_badge.html.erb index 27dcc8af..5d37924d 100644 --- a/app/views/projects/_signal_score_badge.html.erb +++ b/app/views/projects/_signal_score_badge.html.erb @@ -18,9 +18,10 @@ reason = signal_score["reason"] tags = signal_score["tags"] || [] features = signal_score["features"] || {} + show_scores = local_assigns[:show_scores] %> -
+
<%= score_pct %> <%= label %> diff --git a/app/views/projects/index.html.erb b/app/views/projects/index.html.erb index 519b1828..922ebab8 100644 --- a/app/views/projects/index.html.erb +++ b/app/views/projects/index.html.erb @@ -39,15 +39,22 @@ <%= label_tag "funded", t(".funded-filter") %>
- <% - sort_cycle = { nil => "score_desc", "score_desc" => "score_asc", "score_asc" => nil } - next_sort = sort_cycle[@signal_score_sort] - sort_labels = { nil => "Score ↕", "score_desc" => "Score ↓", "score_asc" => "Score ↑" } - %>
- <%= link_to sort_labels[@signal_score_sort], - url_for(request.query_parameters.merge(sort: next_sort).compact_blank), - class: "score-sort-toggle #{@signal_score_sort ? 'active' : ''}" %> + > + +
+ +
+ <% sort_labels = { nil => "Default order", "score_desc" => "Score ↓ highest", "score_asc" => "Score ↑ lowest", "latest" => "Latest first", "earliest" => "Earliest first", "random" => "Random" } %> + + <%= sort_labels[@sort_mode] || "Default order" %> + + +
    + <% sort_labels.each do |val, label| %> +
  1. <%= link_to label, url_for(request.query_parameters.merge(sort: val).compact_blank) %>
    2. + <% end %> +
From a88e3ace6a0604e7d6ed9e9643cdd389677c53af Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:50:04 -0600 Subject: [PATCH 18/25] fix(signal-score): clean up sort labels, default to newest first - Remove arrows from sort options - Default sort is 'Newest first' (original created_at DESC) - Rename: 'Score, highest' / 'Score, lowest' / 'Oldest first' Refs: #590 --- app/views/projects/index.html.erb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/app/views/projects/index.html.erb b/app/views/projects/index.html.erb index 922ebab8..c6281ee6 100644 --- a/app/views/projects/index.html.erb +++ b/app/views/projects/index.html.erb @@ -45,7 +45,7 @@
- <% sort_labels = { nil => "Default order", "score_desc" => "Score ↓ highest", "score_asc" => "Score ↑ lowest", "latest" => "Latest first", "earliest" => "Earliest first", "random" => "Random" } %> + <% sort_labels = { nil => "Newest first", "score_desc" => "Score, highest", "score_asc" => "Score, lowest", "latest" => "Newest first", "earliest" => "Oldest first", "random" => "Random" } %> <%= sort_labels[@sort_mode] || "Default order" %> From 5acd98e254ca8d3ecee8bf56e1258292c6dc2331 Mon Sep 17 00:00:00 2001 From: Cedric Hurst Date: Mon, 23 Feb 2026 18:52:29 -0600 Subject: [PATCH 19/25] fix(signal-score): client-side score toggle, simplify sort labels - Show scores checkbox now toggles via jQuery (no page reload) - Badges always render hidden, JS shows/hides on checkbox change - Sort labels: Latest (default), Earliest, Score highest/lowest, Random - Removed show_scores from URL params (purely client-side state) Refs: #590 --- app/assets/javascripts/application-score-filter.js | 9 +++++++++ app/controllers/projects_controller.rb | 1 - app/views/projects/_project_details.html.erb | 2 +- app/views/projects/_signal_score_badge.html.erb | 3 +-- app/views/projects/index.html.erb | 4 ++-- 5 files changed, 13 insertions(+), 6 deletions(-) diff --git a/app/assets/javascripts/application-score-filter.js b/app/assets/javascripts/application-score-filter.js index f3f67e05..e240363c 100644 --- a/app/assets/javascripts/application-score-filter.js +++ b/app/assets/javascripts/application-score-filter.js @@ -22,3 +22,12 @@ $(document).click(function(e) { $('a.score-selection').removeClass('expanded'); } }); + +/* Show/hide score badges — purely client-side toggle */ +$('#show-scores').on('change', function() { + if ($(this).is(':checked')) { + $('.signal-score').show(); + } else { + $('.signal-score').hide(); + } +}); diff --git a/app/controllers/projects_controller.rb b/app/controllers/projects_controller.rb index a594c62d..a8e18a9f 100644 --- a/app/controllers/projects_controller.rb +++ b/app/controllers/projects_controller.rb @@ -30,7 +30,6 @@ def index # Signal Score filtering — default excludes low-signal apps (≤0.15) @signal_score_min = params.key?(:score_min) ? params[:score_min].to_f : 0.15 @sort_mode = params[:sort] if %w[score_desc score_asc earliest latest random].include?(params[:sort]) - @show_scores = params[:show_scores] == "1" if @signal_score_min > 0 project_filter.signal_score_above(@signal_score_min) diff --git a/app/views/projects/_project_details.html.erb b/app/views/projects/_project_details.html.erb index 174ccf4c..796e3e9e 100644 --- a/app/views/projects/_project_details.html.erb +++ b/app/views/projects/_project_details.html.erb @@ -28,7 +28,7 @@ <% signal_score = project.metadata&.dig("signal_score") %> <% if signal_score && signal_score["composite_score"] %> - <%= render "projects/signal_score_badge", signal_score: signal_score, show_scores: @show_scores %> + <%= render "projects/signal_score_badge", signal_score: signal_score %> <% end %>
diff --git a/app/views/projects/_signal_score_badge.html.erb b/app/views/projects/_signal_score_badge.html.erb index 5d37924d..a94bd2ec 100644 --- a/app/views/projects/_signal_score_badge.html.erb +++ b/app/views/projects/_signal_score_badge.html.erb @@ -18,10 +18,9 @@ reason = signal_score["reason"] tags = signal_score["tags"] || [] features = signal_score["features"] || {} - show_scores = local_assigns[:show_scores] %> -
+