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.

16 stars

Best use case

agentic-layer-audit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Audit codebase for agentic layer coverage and identify gaps. Use when assessing agentic layer maturity, identifying investment opportunities, or evaluating primitive coverage.

Teams using agentic-layer-audit should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/agentic-layer-audit/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/ai-agents/agentic-layer-audit/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/agentic-layer-audit/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How agentic-layer-audit Compares

Feature / Agentagentic-layer-auditStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Audit codebase for agentic layer coverage and identify gaps. Use when assessing agentic layer maturity, identifying investment opportunities, or evaluating primitive coverage.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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:

```markdown
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:

```markdown
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:

```markdown
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:

```markdown
Look for:
- pre_tool_use hooks
- post_tool_use hooks
- user_prompt_submit hooks
```

### 5. Agent Output Directory

Check for `agents/` or equivalent:

```markdown
Look for:
- ADW ID directories
- State files (adw_state.json)
- Output files (cc_*.jsonl, cc_*.json)
```

### 6. Worktree Support

Check for `trees/` or equivalent:

```markdown
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

```markdown
## 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

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