Claude-skill-registry agentic-layer-audit
Audit codebase for agentic layer coverage and identify gaps. Use when assessing agentic layer maturity, identifying investment opportunities, or evaluating primitive coverage.
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 "$T/skills/data/agentic-layer-audit" ~/.claude/skills/majiayu000-claude-skill-registry-agentic-layer-audit && rm -rf "$T"
manifest:
skills/data/agentic-layer-audit/SKILL.mdsource content
Agentic Layer Audit Skill
Evaluate a codebase's agentic layer maturity and identify investment opportunities.
When to Use
- Assessing current agentic layer coverage
- Identifying gaps in automation
- Planning agentic layer investments
- Measuring progress toward 50%+ agentic time
Core Concept
"Am I working on the agentic layer or am I working on the application layer?"
This skill helps answer that question by auditing what exists.
Audit Checklist
1. Commands Directory
Check for
.claude/commands/ or equivalent:
Look for: - chore.md # Chore planning template - bug.md # Bug fix template - feature.md # Feature planning template - implement.md # Implementation HOP - test.md # Test execution template - review.md # Review template
2. Specs Directory
Check for
specs/ or equivalent:
Look for: - Issue-based specs (issue-*.md) - Generated plans (chore-*.md, feature-*.md) - Deep specs (complex multi-file architectures)
3. ADW Directory
Check for
adws/ or equivalent:
Look for: - adw_modules/agent.py # Core agent execution - Gateway scripts (adw_prompt.py, adw_slash_command.py) - Composed workflows (adw_*_*.py) - Triggers (trigger_*.py)
4. Hooks Directory
Check for
.claude/hooks/ or equivalent:
Look for: - pre_tool_use hooks - post_tool_use hooks - user_prompt_submit hooks
5. Agent Output Directory
Check for
agents/ or equivalent:
Look for: - ADW ID directories - State files (adw_state.json) - Output files (cc_*.jsonl, cc_*.json)
6. Worktree Support
Check for
trees/ or equivalent:
Look for: - Git worktree setup - Isolation configuration - Port allocation patterns
Coverage Scoring
| Component | Points | Present? |
|---|---|---|
| .claude/commands/ | 20 | |
| specs/ | 15 | |
| adws/ | 25 | |
| adw_modules/agent.py | 20 | |
| hooks/ | 10 | |
| agents/ | 5 | |
| trees/ | 5 |
Total: 100 points
| Score | Level | Recommendation |
|---|---|---|
| 0-20 | None | Start with minimum viable layer |
| 21-40 | Basic | Add composed workflows |
| 41-60 | Developing | Add hooks and triggers |
| 61-80 | Advanced | Add worktree isolation |
| 81-100 | Complete | Focus on optimization |
Key Memory References
- @agentic-layer-structure.md - What to look for
- @the-guiding-question.md - Why this matters
- @agentic-vs-application.md - Layer separation
Output Format
## Agentic Layer Audit Report **Project:** {name} **Audit Date:** {date} **Coverage Score:** {score}/100 ### Components Found - [x] .claude/commands/ (5 templates) - [x] specs/ (12 specs) - [ ] adws/ (not found) - [ ] hooks/ (not found) ### Maturity Level {Level} - {Recommendation} ### Gaps Identified 1. No ADW scripts for workflow orchestration 2. No hooks for event-driven automation 3. No worktree isolation for parallelization ### Recommended Investments 1. Create adws/adw_modules/agent.py 2. Add gateway script (adw_prompt.py) 3. Create composed workflow for common tasks ### Time Investment Analysis - Current: ~20% agentic layer - Target: 50%+ agentic layer - Gap: Need 30% more investment in agentic work
Anti-Patterns to Identify
- Commands exist but no specs (templates unused)
- Specs exist but no ADWs (manual execution)
- Many one-off scripts instead of composed workflows
- Application layer dominant (>70% of codebase)
Version History
- v1.0.0 (2025-12-26): Initial release
Last Updated
Date: 2025-12-26 Model: claude-opus-4-5-20251101