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AgentForge

Python 3.11+ License: MIT No API Key Powered by Claude Code Claude Code Skill

What to Build in the Agent Era — AI 에이전트 시대의 선택장애 해결 프레임워크

No API key needed. AgentForge runs entirely through your locally installed Claude Code.

AgentForge helps Claude Code users and AI power-users overcome decision paralysis about what to build next. It combines personal ontology mapping, local environment scanning, GitHub trend analysis, and Claude-powered recommendations to produce a personalized, immediately actionable MVP — all using your existing Claude Code session.

The Problem

You use Claude Code every day. You see the potential. But when it comes to what to build, you freeze.

  • Too many ideas, no framework to evaluate them
  • You don't know what gaps exist in the ecosystem
  • You don't know which opportunities fit your specific skills
  • Even when you pick something, starting from scratch is slow

AgentForge solves all of this in one automated pipeline.

Pipeline Overview

                        AgentForge Pipeline
                        ===================

  [You]
    |
    v
+-------------------+
| 1. Ontology Survey|  20 questions about skills, domain,
|   (20 questions)  |  pain points, time, risk, motivation
+-------------------+
    |
    | Personal Ontology JSON
    v
+-------------------+
| 2. Env Scanner    |  pip packages, Claude skills, MCP servers,
|                   |  VS Code extensions, npm globals, git repos
+-------------------+
    |
    | ToolProfile
    v
+-------------------+
| 3. GitHub Search  |  Trending AI/agent repos across 5 categories
|                   |  (1000+ stars, active in last 90 days)
+-------------------+
    |
    | RepoInfo[]
    v
+-------------------+
| 4. Gap Analysis   |  Claude analyzes ecosystem for gaps,
|  (Claude Code)    |  pain themes, and opportunities
+-------------------+
    |
    | GapAnalysis
    v
+-------------------+
| 5. Recommender    |  Combines ontology + tools + gaps
|  (Claude Code)    |  -> Top 5 personalized recommendations
+-------------------+
    |
    | [User selects #N]
    v
+-------------------+
| 6. Data Collector |  Fetches GitHub issues, competitive analysis,
|                   |  integration opportunities, MVP scope
+-------------------+
    |
    | ProjectBrief
    v
+-------------------+
| 7. MVP Builder    |  Claude generates complete working codebase:
|  (Claude Code)    |  Python files, README, tests, Makefile
+-------------------+
    |
    v
  [Runnable MVP in your output directory]

Requirements

  • Python 3.11+
  • Claude Code installed and logged in
  • (Optional) GITHUB_TOKEN for higher GitHub API rate limits

No ANTHROPIC_API_KEY needed. AgentForge uses your Claude Code session directly.

Quick Install

git clone https://github.com/AlexAI-MCP/AgentForge
cd AgentForge
pip install -e .

Quick Start

# Run the full pipeline (no API key needed!)
agentforge run

# Answer 20 questions, get personalized recommendations, build your MVP

That's it. AgentForge handles everything else using your local Claude Code.

Live Demo Output

Real output from AlexLee's profile (A- · 91/100):

#1 ConstructIQ Korea   FIT:9.8  MARKET:8.5  Medium  8weeks
   건설 현장의 모든 지식을 AI로 연결하는 건설 특화 GraphRAG 플랫폼

#2 OntologyMe          FIT:9.2  MARKET:7.8  Medium  6weeks
   한국 최초 퍼스널 지식 그래프 SaaS

#3 MCPHub Korea        FIT:8.7  MARKET:8.2  Hard    10weeks
   한국어 MCP 스킬 마켓플레이스

#4 GraphRAG Enterprise FIT:9.0  MARKET:9.0  Hard    12weeks
   한국 기업 문서를 위한 엔터프라이즈 GraphRAG 레이어

#5 AI Community OS     FIT:9.5  MARKET:7.2  Easy    4weeks
   1200명 Discord 커뮤니티를 위한 지식 운영체제

Usage

Full Pipeline

agentforge run

Runs all 6 steps interactively. At step 5 you choose which recommendation to build. All intermediate results are saved so you can re-run individual steps.

Options:

--skip-scan       Skip the local environment scan
--skip-github     Skip GitHub search (use cached results)
--auto-build      Automatically build the top recommendation

Individual Commands

# Run just the 20-question survey and build your Personal Ontology
agentforge survey

# Scan your local development environment
agentforge scan

# Search GitHub for trending AI repos and run gap analysis
agentforge search
agentforge search --days 60 --limit 15  # custom parameters

# Generate recommendations (requires prior survey)
agentforge recommend

# Build MVP for a specific recommendation
agentforge build 1        # build recommendation #1
agentforge build          # interactive prompt to choose

How It Works

Step 1: Personal Ontology Survey

Twenty questions covering:

Category Questions
Technical Skills Primary language, years experience, AI tools used
Claude Depth How deeply you use Claude Code / Anthropic API
Domain Expertise Primary and secondary industry knowledge
Pain Points Biggest workflow frustration + dream tool
Availability Weekly hours, commitment level
Strategy Target user, monetization preference, risk tolerance
Context Team situation, geographic market, open-source stance
Motivation Why you build, past successes, fears, superpower

Claude then synthesizes these into a Personal Ontology — a structured JSON map of your strengths, gaps, opportunities, builder style, and ideal project traits.

Step 2: Environment Scanner

Non-destructive local scan that detects:

  • Python packages (pip list)
  • Anthropic, OpenAI, LangChain SDKs
  • Claude Code skills (~/.claude/skills/)
  • MCP server configurations
  • VS Code extensions
  • npm global packages
  • Nearby git repositories
  • Available CLI tools (git, docker, node)

Step 3: GitHub Search

Searches 5 categories across 4 queries each (20 total searches):

  • agent-frameworks — multi-agent orchestration
  • llm-tools — prompt engineering, RAG, embeddings
  • automation — workflow, RPA, computer use
  • developer-tools — code gen, AI assistants
  • data-pipelines — vector DBs, knowledge graphs

Returns up to 50 deduplicated repos sorted by stars, filtered to repos active in the last N days.

Step 4: Gap Analysis

Claude reads the full repo list and identifies:

  • Recurring pain themes in open issues
  • "Good but incomplete" frameworks
  • High-demand / low-quality-supply areas
  • Saturated areas to avoid
  • Ranked opportunities with potential scores

Step 5: Recommendation Engine

Combines all three inputs (ontology + tools + gaps) and Claude generates 5 personalized recommendations, each with:

  • Project name and concept
  • Why it fits you specifically (not generic advice)
  • Market opportunity score (1-10)
  • Technical difficulty score (1-10)
  • Estimated MVP timeline in weeks
  • Similar existing projects to differentiate from
  • Concrete first steps (immediately actionable)
  • Monetization path and risk factors

Step 6: Data Collector

Once you choose a recommendation:

  • Fetches open issues from similar/competing projects
  • Classifies issue themes (missing features, bugs, doc gaps)
  • Claude synthesizes a detailed ProjectBrief including:
    • Precise MVP scope (what to build first)
    • Pain-point evidence from real users
    • Competitive landscape with specific weaknesses
    • Integration opportunities
    • Success metrics
    • Risk/mitigation pairs

Step 7: MVP Builder

Claude generates a complete, runnable Python project:

  • Full package structure with working code
  • CLI entry point
  • README with setup instructions
  • pyproject.toml / requirements.txt
  • .env.example
  • Makefile
  • pytest test suite
  • All files written to a timestamped output directory

Architecture

agentforge/
├── __init__.py
├── cli.py              Click CLI with 5 commands
├── config.py           Pydantic config, env var loading
├── ontology/
│   ├── survey.py       20-question interactive survey
│   └── builder.py      Claude-powered ontology synthesis
├── scanner/
│   └── tools.py        Local environment scanner
├── github/
│   ├── searcher.py     Async GitHub API client
│   └── analyzer.py     Claude-powered gap analysis
├── recommender/
│   └── engine.py       Personalized recommendation engine
├── collector/
│   └── data.py         GitHub issue research + brief generation
└── mvp/
    └── builder.py      Claude-powered MVP code generation

All data flows as typed Pydantic/dataclass objects. Claude is called at 4 stages (ontology, gap analysis, recommendations, MVP generation). All intermediate outputs are persisted as JSON in ./agentforge_output/ so individual steps can be re-run without repeating expensive API calls.

Output Files

After a full run, ./agentforge_output/ contains:

agentforge_output/
├── survey.json              Your survey answers
├── ontology.json            Your Personal Ontology
├── tool_profile.json        Environment scan results
├── github_repos.json        Fetched GitHub repositories
├── gap_analysis.json        Ecosystem gap analysis
├── recommendations.json     Your 5 recommendations
├── <project>_brief.md       Project brief (Markdown)
├── <project>_brief.json     Project brief (JSON)
└── <project>_YYYYMMDD_HHMMSS/   Generated MVP codebase
    ├── README.md
    ├── pyproject.toml
    ├── requirements.txt
    ├── .env.example
    ├── Makefile
    ├── <package>/
    │   └── *.py
    └── tests/
        └── *.py

Configuration

All settings via environment variables or .env file:

Variable Required Default Description
ANTHROPIC_API_KEY Yes — Your Anthropic API key
GITHUB_TOKEN No — GitHub PAT (5000 req/hr vs 60)
AGENTFORGE_MODEL No claude-opus-4-6 Claude model to use
AGENTFORGE_OUTPUT_DIR No ./agentforge_output Output directory

Get your Anthropic API key at console.anthropic.com.

Get a GitHub token at github.com/settings/tokens (no permissions needed — public repo read is sufficient).

Requirements

  • Python 3.11+
  • anthropic>=0.40.0
  • click>=8.1.0
  • rich>=13.0.0
  • httpx>=0.27.0
  • pydantic>=2.0.0
  • python-dotenv>=1.0.0
  • jinja2>=3.1.0

For Korean Developers (한국 개발자를 위한 안내)

AgentForge는 Claude Code를 사용하는 한국 개발자들을 위해 특별히 설계되었습니다.

설문조사는 한국어로 진행됩니다 (영어 부제목 포함).

추천 시스템은 한국 시장의 특성을 고려합니다:

  • 한국어 우선 시장의 기회
  • 국내 개발자 커뮤니티의 미충족 수요
  • 글로벌 진출을 위한 영어 도구화 전략

AI 에이전트 시대에 무엇을 만들어야 할지 더 이상 고민하지 마세요. AgentForge가 당신의 강점, 도구, 시장 갭을 분석하여 가장 적합한 프로젝트를 추천합니다.

Contributing

Contributions are welcome. Please:

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/your-feature)
  3. Write tests for new functionality
  4. Ensure all tests pass (pytest)
  5. Run linting (ruff check . && black --check .)
  6. Submit a pull request

Development Setup

git clone https://github.com/agentforge/agentforge
cd agentforge
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows
pip install -e ".[dev]"
cp .env.example .env
# Add your API keys to .env
pytest

License

MIT License — see LICENSE file.

Acknowledgments

Built for Claude Code users who want to spend less time deciding and more time building. Powered by Anthropic Claude.

AgentForge — Stop deciding. Start building. 더 이상 고민하지 말고, 지금 바로 만드세요.

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