leverage-point-audit
Audit a codebase for the 12 leverage points of agentic coding. Identifies gaps and provides prioritized recommendations. Use when improving agentic coding capability, analyzing why agents fail, or optimizing a codebase for autonomous work.
What this skill does
# Leverage Point Audit Audit a codebase against the 12 leverage points framework to identify gaps and improve agentic coding success. ## When to Use - Before starting a new agentic coding project - When agents are failing or requiring many attempts - When KPIs (Size, Attempts, Streak, Presence) are not improving - For periodic health checks of agentic capability ## The 12 Leverage Points ### In-Agent (Core Four) 1. **Context** - CLAUDE.md, README, project docs 2. **Model** - Appropriate model selection 3. **Prompt** - Clear instructions and templates 4. **Tools** - Required capabilities available ### Through-Agent (External) 1. **Standard Out** - Logging for visibility 2. **Types** - Information Dense Keywords (IDKs) 3. **Documentation** - Agent-specific context 4. **Tests** - Self-correction capability (HIGHEST LEVERAGE) 5. **Architecture** - Navigable codebase structure 6. **Plans** - Meta-work communication 7. **Templates** - Reusable prompts (slash commands) 8. **ADWs** - Autonomous workflows ## Audit Workflow ### Step 1: Check Context (Leverage Points 1-4) **CLAUDE.md presence:** ```yaml Search for: CLAUDE.md, .claude/CLAUDE.md Check: Does it explain the project? Conventions? Common commands? ``` **README.md quality:** ```yaml Search for: README.md Check: Does it explain structure? How to run? How to test? ``` **Permissions configuration:** ```yaml Search for: .claude/settings.json Check: Are required tools allowed? ``` ### Step 2: Check Visibility (Leverage Point 5) **Standard out patterns:** ```yaml Search for: print(, console.log(, logger., logging. Check: Are success AND error cases logged? Check: Can agent see what's happening? ``` **Anti-pattern detection:** ```text Look for: Silent returns, bare except blocks, empty catch blocks These prevent agent visibility. ``` ### Step 3: Check Searchability (Leverage Point 6) **Type definitions:** ```yaml Search for: interface, type, class, BaseModel, dataclass Check: Are names information-dense? (Good: UserAuthToken, Bad: Data) ``` ### Step 4: Check Documentation (Leverage Point 7) **Internal docs:** ```yaml Search for: *.md files, docstrings, comments Check: Do they explain WHY, not just WHAT? ``` ### Step 5: Check Validation (Leverage Point 8) - HIGHEST PRIORITY **Test presence:** ```yaml Search for: test_*.py, *.test.ts, *.spec.ts, *_test.go Check: Do tests exist? Are they comprehensive? ``` **Test commands:** ```yaml Check: Is there a simple test command? (npm test, pytest, etc.) Check: Do tests run quickly? ``` ### Step 6: Check Architecture (Leverage Point 9) **Entry points:** ```yaml Check: Are entry points obvious? (main.py, index.ts, server.py) ``` **File organization:** ```yaml Check: Consistent structure? Related files grouped? Check: File sizes reasonable? (< 1000 lines) ``` ### Step 7: Check Templates (Leverage Point 11) **Slash commands:** ```yaml Search for: .claude/commands/ Check: Are common workflows automated? ``` ### Step 8: Check ADWs (Leverage Point 12) **Automation:** ```yaml Search for: GitHub Actions, hooks, triggers Check: Are workflows automated? ``` ## Output Format After audit, provide: ### Summary Table | Leverage Point | Status | Priority | Recommendation | | --- | --- | --- | --- | | Context | Good/Fair/Poor | High/Med/Low | Specific action | | ... | ... | ... | ... | ### Priority Actions List top 3-5 improvements in order of impact: 1. **[Highest Impact]** - Specific recommendation 2. **[High Impact]** - Specific recommendation 3. **[Medium Impact]** - Specific recommendation ### Detailed Findings For each leverage point: - Current state - Specific gaps found - Recommended improvements - Example of what good looks like ## Example Audit Output ```markdown ## Leverage Point Audit Results ### Summary - Tests: POOR (no test files found) - HIGHEST PRIORITY - Standard Out: FAIR (some logging, missing error cases) - Architecture: GOOD (clear structure, reasonable file sizes) ### Priority Actions 1. Add test suite - enables self-correction 2. Add error logging to API endpoints - enables visibility 3. Create /prime command - enables quick context ### Detailed Findings [... specific recommendations ...] ``` ## Related Memory Files - @12-leverage-points.md - Complete framework reference - @agentic-kpis.md - How to measure improvement - @agent-perspective-checklist.md - Quick pre-task checklist ## Version History - **v1.0.0** (2025-12-26): Initial release --- ## Last Updated **Date:** 2025-12-26 **Model:** claude-opus-4-5-20251101
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