--- name: project-health description: | AI-agent readiness auditing for project documentation and workflows. Evaluates whether future Claude Code sessions can understand docs, execute workflows literally, and resume work effectively. Use when onboarding AI agents to a project or ensuring context continuity. Includes three specialized agents: context-auditor (AI-readability), workflow-validator (process executability), handoff-checker (session continuity). Use PROACTIVELY before handing off projects to other AI sessions or team members. --- # Project Health: AI-Agent Readiness Auditing **Status**: Active **Updated**: 2026-01-30 **Focus**: Ensuring documentation and workflows are executable by AI agents ## Overview This skill evaluates project health from an **AI-agent perspective** - not just whether docs are well-written for humans, but whether future Claude Code sessions can: 1. **Understand** the documentation without ambiguity 2. **Execute** workflows by following instructions literally 3. **Resume** work effectively with proper context handoff ## When to Use - Before handing off a project to another AI session - When onboarding AI agents to contribute to a codebase - After major refactors to ensure docs are still AI-executable - When workflows fail because agents "didn't understand" - Periodic health checks for AI-maintained projects ## Agent Selection Guide | Situation | Use Agent | Why | |-----------|-----------|-----| | "Will another Claude session understand this?" | **context-auditor** | Checks for ambiguous references, implicit knowledge, incomplete examples | | "Will this workflow actually execute?" | **workflow-validator** | Verifies steps are discrete, ordered, and include verification | | "Can a new session pick up where I left off?" | **handoff-checker** | Validates SESSION.md, phase tracking, context preservation | | Full project health audit | All three | Comprehensive AI-readiness assessment | ## Key Principles ### 1. Literal Interpretation AI agents follow instructions literally. Documentation that works for humans (who fill in gaps) may fail for agents. **Human-friendly** (ambiguous): > "Update the config file with your settings" **AI-friendly** (explicit): > "Edit `wrangler.jsonc` and set `account_id` to your Cloudflare account ID (find it at dash.cloudflare.com → Overview → Account ID)" ### 2. Explicit Over Implicit Never assume the agent knows: - Which file you mean - What "obvious" next steps are - Environment state or prerequisites - What success looks like ### 3. Verification at Every Step Agents can't tell if something "feels right". Include verification: - Expected output after each command - How to check if a step succeeded - What to do if it failed ## Agents ### context-auditor **Purpose**: Evaluate AI-readability of documentation **Checks**: - Instructions use imperative verbs (actionable) - File paths are explicit (not "the config file") - Success criteria are measurable - No ambiguous references ("that thing", "as discussed") - Code examples are complete (not fragments) - Dependencies/prerequisites stated explicitly - Error handling documented **Output**: AI-Readability Score (0-100) with specific issues ### workflow-validator **Purpose**: Verify processes are executable when followed literally **Checks**: - Steps are discrete and ordered - Each step has clear input/output - No implicit knowledge required - Environment assumptions documented - Verification step after each action - Failure modes and recovery documented - No "obvious" steps omitted **Output**: Executability Score (0-100) with step-by-step analysis ### handoff-checker **Purpose**: Ensure session continuity for multi-session work **Checks**: - SESSION.md or equivalent exists - Current phase/status clear - Next actions documented - Blockers/decisions needed listed - Context for future sessions preserved - Git checkpoint pattern in use - Architecture decisions documented with rationale **Output**: Handoff Quality Score (0-100) with continuity gaps ## Templates ### AI-Readable Documentation Template See `templates/AI_READABLE_DOC.md` for a template that ensures AI-readability. Key sections: - **Prerequisites** (explicit environment/state requirements) - **Steps** (numbered, discrete, with verification) - **Expected Output** (what success looks like) - **Troubleshooting** (common failures and fixes) ### Handoff Checklist See `templates/HANDOFF_CHECKLIST.md` for ensuring clean session handoffs. ## Anti-Patterns ### 1. "See Above" References ```markdown # Bad As mentioned above, configure the database. # Good Configure the database by running: `npx wrangler d1 create my-db` ``` ### 2. Implicit File Paths ```markdown # Bad Update the config with your API key. # Good Add your API key to `.dev.vars`: ``` API_KEY=your-key-here ``` ``` ### 3. Missing Verification ```markdown # Bad Run the migration. # Good Run the migration: `npx wrangler d1 migrations apply my-db --local` Verify with: `npx wrangler d1 execute my-db --local --command "SELECT name FROM sqlite_master WHERE type='table'"` Expected output: Should show your table names. ``` ### 4. Assumed Context ```markdown # Bad Now deploy (you know the drill). # Good Deploy to production: `npx wrangler deploy` Verify deployment at: https://your-worker.your-subdomain.workers.dev ``` ## Relationship to Other Tools | Tool | Focus | Audience | |------|-------|----------| | `project-docs-auditor` | Traditional doc quality (links, freshness, structure) | Human readers | | `project-health` skill | AI-agent readiness (executability, clarity, handoff) | Claude sessions | | `docs-workflow` skill | Creating/managing specific doc files | Both | ## Quick Start 1. **Full audit**: "Run all project-health agents on this repo" 2. **Check one aspect**: "Use context-auditor to check AI-readability" 3. **Before handoff**: "Use handoff-checker before I end this session" ## Success Metrics A healthy project scores: - **Context Auditor**: 80+ (AI can understand without clarification) - **Workflow Validator**: 90+ (steps execute literally without failure) - **Handoff Checker**: 85+ (new session can resume immediately) Projects below these thresholds have documentation debt that will slow future AI sessions.