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DOCUMENT_PROJECT_STATE

Documents

DOCUMENT_PROJECT_STATE is an Agent Skill for documents published in the majiayu000/claude-skill-registry repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.

Generate documentation for an existing project. USE WHEN onboarded to a new codebase OR documenting legacy code. Capabilities include architectural mapping, tech stack inventory, and technical debt documentation.

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QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
Inspect the Agent Skill at https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/documents/document-project-state-kyteapp-growth-agents-and-sk-2/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/document-project-state, but wait for my approval before writing files or running scripts.

Copying this prompt does not install or run the skill. Review third-party files before use.

DOCUMENT PROJECT

Owner: Devon (Developer)

Goal

To produce accurate, reality-based documentation for an existing codebase. This is about documenting what is, not what should be.

Core Principles

  • Reality Over Theory: Document the actual patterns, even the ugly ones.
  • Lean: Focus on what an AI agent or new developer needs to know to work.
  • Automated Discovery: Use tools to read the code, don't just ask the user.

Workflow Instructions

1. Scope & Elicitation

  • Action: Determine what to document.
  • Prompt: "Are we documenting the whole system or just a specific module?"
  • Check: Is there a PRD? If so, focus on relevant areas.

2. Discovery

  • Action: Map the codebase.
  • Targets: package.json, README.md, src/ structure, build scripts.
  • Tool: Use context7 to identify frameworks and versions.

3. Deep Analysis

  • Action: Read key files.
  • Focus: Entry points, config, auth, data models, API routes.
  • Identify:
    • Patterns: How is code organized?
    • Debt: What looks hacky or legacy?
    • Integration: External APIs?

4. Drafting the Architecture Doc

  • Action: Assemble the insights.
  • Template: .gemini/templates/architecture-tmpl.yaml (conceptual reference).
  • Sections:
    • Overview: System purpose.
    • Tech Stack: Exact versions (verified).
    • Key Patterns: How to write code here.
    • Critical Files: The map.
    • Technical Debt: Warnings for the agent.

5. Review & Save

  • Action: Validate against reality.
  • Check: Do the file paths exist? Are the versions correct?
  • Write: docs/architecture/{{project_name}}-architecture.md.

Anti-Patterns

  • NEVER document theoretical best practices that aren't followed in the code.
  • NEVER omit version numbers for core dependencies.
  • NEVER generate generic "boilerplate" documentation.

Output Format

A comprehensive Markdown architecture document.