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name: ai-audit description: Run a full AI Playbook audit against an existing agentic application or AI feature. Orchestrates Decision Record reverse-engineering, security review, eval audit, launch check, and consolidated remediation planning in one flow. Use when the user explicitly runs /ai-audit or says "audit my AI app", "verify my agent against the playbook", "is my existing AI code production-ready", "review my agentic system", or similar phrasing for verifying existing code (not building new). disable-model-invocation: true allowed-tools: Read, Write, Glob, Grep last-reviewed: 2026-05-17

AI Application Audit

Iron Law: Do not auto-fix. This skill produces reports and a remediation plan — not code changes. Every finding becomes a separate task for a follow-up session.

Run a comprehensive audit of an existing agentic application or AI feature against the AI Playbook. This is for verifying existing code, not designing new features.

The audit runs in five phases with explicit confirmation gates between each. Each phase produces a written artifact in the repo. The final output is a prioritized remediation plan.

When to use this vs. the individual skills

• Use /ai-audit when you want a full pass against existing code and you're willing to let it run end-to-end.
• Use the individual skills (/ai-decision-record, /ai-launch-check, /ai-incident-response) and subagents (@ai-security-reviewer, @ai-eval-designer) when you want finer control between phases or you're only doing part of the work.

Operating procedure

Walk through the five phases below in order. Do not skip phases. Each phase has an explicit confirmation gate where the user can pause, redirect, or abort.

Before starting, load the ai-playbook skill if it isn't already in context.

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Phase 0: Scoping (no artifact)

Before any audit work, get clarity on what's being audited. Ask the user:

1. What is the feature slug? (Used for directory naming. If unsure, propose one from the repo structure.)
2. Is this a single feature audit or a multi-feature codebase? (If multi-feature, ask which feature to audit first — running the full audit on multiple features in one shot is too token-heavy.)
3. Where is the AI code? (Path or paths. If unknown, offer to scan and report back before continuing.)
4. Is there an existing Decision Record? (Check docs/ai-decisions/<feature-slug>.md. If yes, this skips Phase 1 and uses it as input.)

After confirming scope, summarize the plan:

Audit plan for: <feature-slug>
- AI code location: <paths>
- Phases to run:
  1. Decision Record reverse-engineering (or skip if exists)
  2. Security review via @ai-security-reviewer subagent
  3. Eval audit via @ai-eval-designer subagent
  4. Launch check (gate)
  5. Consolidated remediation plan
- Estimated wall time: 60–120 minutes depending on codebase size
- Estimated token cost: significant (two subagents + main session). Confirm before proceeding.

Proceed? (yes / scope-down / abort)

Do not proceed without explicit confirmation. This audit consumes meaningful tokens and produces artifacts in the repo.

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Phase 1: Decision Record (reverse-engineered)

Skip this phase if a Decision Record already exists at docs/ai-decisions/<feature-slug>.md. In that case, read it, summarize what it claims, and ask the user to confirm it still reflects reality before moving to Phase 2.

If no Decision Record exists, reverse-engineer one:

1. Scan the AI code. Identify every AI surface: LLM calls, agent loops, tool definitions, prompts (system / user / templates), RAG/retrieval, model output handlers, side-effect points (writes, sends, payments).

2. Determine the pattern (assistant / workflow / agent / autonomous) based on what the code does, not what it's named. Multi-step planning with tools is an agent. A single LLM call wrapped in retries is a workflow.

3. Propose a risk tier based on actual blast radius. Sum across a session, not per action. Be honest. If the code can send emails to customers, that's at least Tier 3a regardless of how it's framed.

4. Ask clarifying questions for what the code can't tell you. Ask one at a time:

   • Who is the named human owner?
   • What is the user problem this solves?
   • What is the cost ceiling per task?
   • What is the current eval set (if any)?
   • What is the SLA for human review (if any review queue exists)?
   • What is the 12-month AI tax estimate?
   • Where is the kill switch?
   • When was it last tested?

5. Write the Decision Record to docs/ai-decisions/<feature-slug>.md using the template from the ai-decision-record skill / Layer 3 §3.3 of the playbook.

6. Confirmation gate: Summarize the Decision Record. Highlight any fields where you had to guess. Ask the user to verify before proceeding.

Decision Record drafted at docs/ai-decisions/<feature-slug>.md.

Honest summary:
- Pattern: <X>
- Risk tier: <N>
- Top three risks I identified: ...
- Fields I had to guess (please verify): ...

Proceed to Phase 2 (security review)? (yes / edit-record / abort)

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Phase 2: Security review

Confirmation before invoking the subagent. Subagents consume real tokens. Confirm scope:

About to invoke @ai-security-reviewer on:
- <paths>

The subagent will run in its own context. Expected output: structured findings report (Critical / High / Medium).

Proceed? (yes / narrow-scope / abort)

After confirmation, invoke:

@ai-security-reviewer audit the AI code at <paths> against the ai-playbook security defenses (Layer 2 §2.5) and the adversarial test catalog (Layer 3 §3.2).

Focus on:
- LLM call sites
- Tool definitions and agent loops
- RAG/retrieval code
- Anywhere user input, retrieved documents, or tool outputs flow into prompts
- Anywhere model output triggers side effects

Output the standard findings report. For each finding, cite file:line and explain why a prompt-level mitigation is insufficient.

Decision Record context: docs/ai-decisions/<feature-slug>.md

Write the subagent's report to docs/ai-audits/<feature-slug>/02-security-review.md.

Confirmation gate:

Security review complete. Summary:
- Critical findings: <N>
- High findings: <N>
- Medium findings: <N>

Top three blockers: ...

Full report: docs/ai-audits/<feature-slug>/02-security-review.md

Proceed to Phase 3 (eval audit)? (yes / pause-to-remediate / abort)

If there are Critical findings, recommend pausing to remediate before continuing. The launch check (Phase 4) will fail on Criticals anyway; fixing them first makes the rest of the audit more useful.

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Phase 3: Eval audit

Confirmation before invoking the subagent:

About to invoke @ai-eval-designer on:
- Existing tests/evals at: <paths>
- Risk tier from Decision Record: <N>

The subagent will identify eval gaps and generate a starter eval set for the biggest ones.

Proceed? (yes / narrow-scope / abort)

After confirmation, invoke:

@ai-eval-designer audit eval coverage for this agentic app.

1. Find existing tests and evals at: <paths>
2. Read the Decision Record at docs/ai-decisions/<feature-slug>.md (risk tier: <N>).
3. Compare current eval coverage against the playbook's risk-tiered minimums (Layer 2 §2.14).
4. Identify gaps across happy path, edge cases, adversarial, negative.
5. Generate a starter eval set at evals/<feature-slug>/cases/starter.json filling the most critical gaps — especially adversarial cases from Layer 3 §3.2.
6. Output a gap analysis report.

Write the report to docs/ai-audits/<feature-slug>/03-eval-audit.md.

Confirmation gate:

Eval audit complete. Summary:
- Current eval set size: <N>
- Required minimum for Tier <N>: <M>
- Gap: <M-N> cases
- Categories most under-covered: ...

Starter eval set generated at: evals/<feature-slug>/cases/starter.json
Full report: docs/ai-audits/<feature-slug>/03-eval-audit.md

Proceed to Phase 4 (launch check)? (yes / abort)

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Phase 4: Launch check

This is the audit's accountability moment. The launch check demands evidence for every item — and on existing code, expect to fail it on the first pass. That's the audit doing its job.

Run the launch check inline (do not invoke a subagent — this needs to stay in the main session to read Phase 1–3 artifacts):

Apply the unified checklist from Layer 4 §4.1 of the playbook. For each item:

• Try to verify from the codebase directly first.
• Cross-reference Phase 2 (security) and Phase 3 (eval) findings.
• Ask the user for evidence only when code inspection is inconclusive.
• Mark ✅ / ⚠️ / ❌.

Be strict about evidence. "We have logging" without pointing to the logging schema doesn't count. "We have a kill switch" without pointing to a tested feature flag doesn't count.

Write the launch check report to docs/ai-audits/<feature-slug>/04-launch-check.md. Include:

• Decision: GO / NO-GO / GO-WITH-EXCEPTIONS
• Every checklist item with status and evidence
• Cross-references to Phase 2 and Phase 3 findings

Confirmation gate:

Launch check complete.

Decision: <GO / NO-GO / GO-WITH-EXCEPTIONS>
- ✅ Verified: <N>
- ⚠️ Partial: <N>
- ❌ Missing: <N>

Top blockers: ...

Full report: docs/ai-audits/<feature-slug>/04-launch-check.md

Proceed to Phase 5 (consolidated remediation plan)? (yes / abort)

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Phase 5: Consolidated remediation plan

Synthesize findings from all four prior phases into one prioritized backlog. Read the artifacts:

• docs/ai-decisions/<feature-slug>.md (Decision Record)
• docs/ai-audits/<feature-slug>/02-security-review.md
• docs/ai-audits/<feature-slug>/03-eval-audit.md
• docs/ai-audits/<feature-slug>/04-launch-check.md

Group every finding into one of four priority tiers:

1. Critical — fix before any further production traffic. Security criticals; missing or untested kill switch; missing output contract on Tier-3+ features; missing tenant isolation; secrets in model context.
2. High — fix within 2 weeks. High security findings; eval set below risk-tiered floor; missing observability; missing incident response plan; rollback path not tested.
3. Medium — schedule in the next quarter. Medium security findings; drift detection gaps; vendor abstraction; multi-modal handling refinements; AI tax not estimated.
4. Structural — design debt to track. Full reference architecture gaps; eval flywheel not in place; canary deployment infrastructure; full Layer 2 coverage.

For each item, include:

• Source: which audit phase surfaced it
• Playbook reference: which section/layer
• Concrete next action: what to actually do
• Suggested owner role (named human in Decision Record; if unspecified, propose the role)
• Acceptance criteria: how you'll know it's done

Write to docs/ai-audits/<feature-slug>/05-remediation-plan.md.

Final output to user:

AI Audit Complete

Feature: <feature-slug>
Pattern: <X>
Risk tier: <N>
Launch check decision: <GO / NO-GO / GO-WITH-EXCEPTIONS>

Findings:
- Critical: <N> items
- High: <N> items
- Medium: <N> items
- Structural: <N> items

Top three things to fix this week:
1. ...
2. ...
3. ...

Full artifacts:
- Decision Record: docs/ai-decisions/<feature-slug>.md
- Security review: docs/ai-audits/<feature-slug>/02-security-review.md
- Eval audit: docs/ai-audits/<feature-slug>/03-eval-audit.md
- Launch check: docs/ai-audits/<feature-slug>/04-launch-check.md
- Remediation plan: docs/ai-audits/<feature-slug>/05-remediation-plan.md

Suggested next step:
- If Critical items: pause, fix them, re-run `/ai-audit` to verify
- If High items: schedule with owners, re-run `/ai-audit` in 2 weeks
- If only Medium/Structural: ship with exceptions filed in Decision Record; track in regular planning

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Default stance

• Be strict about evidence in Phase 4. The whole audit's value depends on the launch check being honest. If you accept verbal assertions, the audit produces false confidence.
• Treat the first run as a baseline, not a verdict. Existing code rarely passes a fresh audit. The output is a remediation backlog, not a launch decision.
• Default to recommending pause-to-remediate after Phase 2 if there are Criticals. Continuing the audit past unfixed Critical security findings makes the rest of the work less useful.
• Confirmation gates are not optional. Even if the user said "yes go through everything" at the start, each gate is a chance to course-correct. Honor them.
• Don't auto-fix. This skill produces reports and plans, not code changes. Remediation is a separate conversation per finding.

What this skill does NOT do

• It does not implement fixes. Each finding becomes a task; the human (or a follow-up Claude Code session) does the work.
• It does not replace ongoing eval maintenance, security review, or incident response. It's a snapshot audit; the playbook's lifecycle controls (eval flywheel, drift detection, incident learning) are continuous.
• It does not certify production readiness. The launch check decision is advisory; final ship/no-ship is a human + business call informed by this report.
• It does not run on multiple features in parallel. Audit one feature at a time. Repeat for the next.

Verify (after Phase 5 is complete)

✅ docs/ai-decisions/<feature-slug>.md exists (created or confirmed in Phase 1)
✅ docs/ai-audits/<feature-slug>/02-security-review.md written
✅ docs/ai-audits/<feature-slug>/03-eval-audit.md written
✅ docs/ai-audits/<feature-slug>/04-launch-check.md written with GO/NO-GO decision
✅ docs/ai-audits/<feature-slug>/05-remediation-plan.md written with priority tiers
✅ No audit phase was silently skipped — every abort was explicit and user-confirmed

If this skill cannot proceed

Abort condition: user declines scope confirmation at Phase 0
→ Stop. Do not run any phases.
→ Output: "Audit cancelled at scoping. No artifacts written."

Abort condition: Critical security findings in Phase 2
→ Recommend: "Pause audit. Fix Critical findings first. Re-run /ai-audit to continue."
→ Do not mark audit complete — it is paused, not done.

Abort condition: Decision Record missing and user refuses to reverse-engineer one
→ Stop at Phase 1. Output: "Cannot proceed without a Decision Record. Run /ai-decision-record first."

References

• AI Playbook: .claude/skills/ai-playbook/playbook.md (Layers 1–4)
• Decision Record template: .claude/skills/ai-decision-record/SKILL.md
• Launch checklist: .claude/skills/ai-launch-check/SKILL.md (Layer 4 §4.1)
• Security adversarial catalog: Layer 3 §3.2 of playbook.md
• Eval sizing table: Layer 2 §2.14 of playbook.md
• OWASP LLM Top 10: https://owasp.org/www-project-top-10-for-large-language-model-applications/