Claude-skill-registry 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.
install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/leverage-point-audit" ~/.claude/skills/majiayu000-claude-skill-registry-leverage-point-audit && rm -rf "$T"
manifest:
skills/data/leverage-point-audit/SKILL.mdsource content
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)
- Context - CLAUDE.md, README, project docs
- Model - Appropriate model selection
- Prompt - Clear instructions and templates
- Tools - Required capabilities available
Through-Agent (External)
- Standard Out - Logging for visibility
- Types - Information Dense Keywords (IDKs)
- Documentation - Agent-specific context
- Tests - Self-correction capability (HIGHEST LEVERAGE)
- Architecture - Navigable codebase structure
- Plans - Meta-work communication
- Templates - Reusable prompts (slash commands)
- ADWs - Autonomous workflows
Audit Workflow
Step 1: Check Context (Leverage Points 1-4)
CLAUDE.md presence:
Search for: CLAUDE.md, .claude/CLAUDE.md Check: Does it explain the project? Conventions? Common commands?
README.md quality:
Search for: README.md Check: Does it explain structure? How to run? How to test?
Permissions configuration:
Search for: .claude/settings.json Check: Are required tools allowed?
Step 2: Check Visibility (Leverage Point 5)
Standard out patterns:
Search for: print(, console.log(, logger., logging. Check: Are success AND error cases logged? Check: Can agent see what's happening?
Anti-pattern detection:
Look for: Silent returns, bare except blocks, empty catch blocks These prevent agent visibility.
Step 3: Check Searchability (Leverage Point 6)
Type definitions:
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:
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:
Search for: test_*.py, *.test.ts, *.spec.ts, *_test.go Check: Do tests exist? Are they comprehensive?
Test commands:
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:
Check: Are entry points obvious? (main.py, index.ts, server.py)
File organization:
Check: Consistent structure? Related files grouped? Check: File sizes reasonable? (< 1000 lines)
Step 7: Check Templates (Leverage Point 11)
Slash commands:
Search for: .claude/commands/ Check: Are common workflows automated?
Step 8: Check ADWs (Leverage Point 12)
Automation:
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:
- [Highest Impact] - Specific recommendation
- [High Impact] - Specific recommendation
- [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
## 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