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post-bootstrap doc refinements: tagline, philosophy, install instructions, constitution wording, templates/ description, doc maintenance section
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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CLAUDE.md

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@@ -30,7 +30,7 @@ src/parallax/ # Main package
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cli/ # Typer CLI commands
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core/ # Hypothesis, state, templates, workflow logic
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db/ # SQLite models + queries
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templates/ # Templates Parallax generates for user projects
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templates/ # Source templates rendered by parallax init into user projects
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tests/ # pytest
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docs/ # VISION.md, ROADMAP.md, plans/
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.claude/skills/ # Claude Code skills for Parallax development
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- Never add backward-compat shims — just change the code
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- Never create docs/READMEs unless explicitly requested
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## Documentation Maintenance
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- At the end of every plan, verify README.md and other markdown docs reflect current state
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- If code changes affect documented behavior, update the relevant docs in the same PR
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- @README.md and other key docs should be reviewed before marking any plan complete
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## References
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- @CONSTITUTION.md — core scientific values (import into agent context)

CONSTITUTION.md

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## Principles
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1. **Robust science over fast science.** Accelerate as fast as possible but no faster. Speed is a means, not an end.
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1. **Robust science over fast science.** Accelerate as fast as is safe, quantifiable, and verifiable, but no faster.
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2. **NEVER weaken tests to pass.** Loud failure over silent success. If a test fails, the code is wrong or the test expectations need explicit human review — never quietly relax tolerances.
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2. **NEVER weaken tests to pass — unless a human user explicitly instructs it.** Loud failure over silent success. If a test fails, the code is wrong or the test expectations need explicit human review — never quietly relax tolerances.
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3. **Hypotheses before implementation.** Think before you code. Formalize what you expect and why before writing the first line.
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README.md

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Scientific augmentation tool encoding best practices for reproducible, hypothesis-driven science into agentic AI workflows.
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Two perspectives (human + AI) to get a stronger result.
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*True depth requires two lines of sight.*
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## Philosophy
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Accelerate excellent science — but no faster than that. AI supplements scientific work, never drives it. See [CONSTITUTION.md](CONSTITUTION.md) for core values.
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Accelerate as fast as is safe, quantifiable, and verifiable — but no faster. Parallax encodes scientific best practices into agentic AI workflows so that speed gains never come at the cost of rigor, reproducibility, or correctness. AI supplements scientific work; it never drives it.
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See [CONSTITUTION.md](CONSTITUTION.md) for core values.
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## Architecture
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```
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src/parallax/ # Main package (cli/, core/, db/)
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templates/ # Templates generated for user projects
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templates/ # Source templates (rendered by parallax init)
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tests/ # pytest
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docs/ # VISION.md, ROADMAP.md, plans/
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.claude/ # Skills and hooks for development
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```
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## Installation
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Requires [pixi](https://pixi.sh).
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Requires [pixi](https://pixi.sh). Install via:
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```bash
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# macOS / Linux
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curl -fsSL https://pixi.sh/install.sh | bash
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# macOS (Homebrew)
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brew install pixi
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# Windows
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powershell -c "irm https://pixi.sh/install.ps1 | iex"
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```
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Then:
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```bash
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pixi install

docs/VISION.md

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# Parallax Vision
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> Two perspectives (human + AI) to get a stronger result.
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> *True depth requires two lines of sight.*
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## Mission
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Parallax is a scientific augmentation tool that encodes best practices for reproducible, hypothesis-driven science into agentic AI workflows. It accelerates excellent science — but no faster than that.
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Parallax is a scientific augmentation tool that encodes best practices for reproducible, hypothesis-driven science into agentic AI workflows. It accelerates science as fast as is safe, quantifiable, and verifiable — but no faster.
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Personal science use case. Not enterprise/team tooling (yet).
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## Core Principles
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- AI supplements scientific work, never drives it
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- Robust science over fast science
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- Robust science over fast science — accelerate only as fast as is safe, quantifiable, and verifiable
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- Reproducibility is non-negotiable
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- Loud errors over silent failures
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- Negative results are scientifically valuable

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