audit

name	audit
description	Project-wide health audit pipeline that fans out to all analysis skills in parallel, evaluates findings, and produces a unified report at .turbo/audit.md. Use when the user asks to "audit the project", "run a full audit", "project health check", "audit my code", "codebase audit", or "comprehensive review".

Audit

Project-wide health audit. Fans out to all analysis skills, evaluates findings, and writes .turbo/audit.md and .turbo/audit.html. Analysis-only — does not apply fixes.

Task Tracking

At the start, use update_plan to track each phase, restating any remaining steps of a parent workflow alongside them:

1. Scope and partition
2. Threat model
3. Run analysis skills
4. Run $evaluate-findings skill
5. Generate markdown report
6. Generate HTML report

Step 1: Scope and Partition

If $ARGUMENTS specifies paths, use those directly (skip the question).

Otherwise, use request_user_input to confirm scope:

• All source files — audit everything
• Specific paths — user provides directories or file patterns
• Critical paths — heuristically identify high-risk areas (entry points, auth, data handling, payment processing)

Once scope is determined:

1. Glob for source files in the selected scope. Exclude generated and vendored directories (node_modules/, dist/, build/, vendor/, __pycache__/, .build/, DerivedData/, target/, .tox/, and others appropriate to the project).
2. Partition files by top-level source directory. Cap at 10 partitions. If more than 10 top-level directories exist, group related directories or use request_user_input to narrow scope. If a single directory contains 50+ files, sub-partition it by its immediate subdirectories.

Step 2: Threat Model

Check if .turbo/threat-model.md exists. If it does, continue to Step 3.

If missing, use request_user_input to ask whether to create one before proceeding. The security review benefits from threat model context, but creating one adds time.

• Yes — launch a Codex sub-agent call (inherited model defaults) whose prompt instructs it to invoke the $create-threat-model skill by reading and following the installed skill instructions. Wait for completion before continuing.
• No — continue without a threat model.

Step 3: Launch All Analysis Agents

Before dispatching, read the project's test configuration and CI workflow to identify any test tier that resets a shared external resource between tests, such as a database, a fixed port, or a cache. Such tiers have no cross-process interlock, so sub-agents running them concurrently wipe each other's state and return failures indistinguishable from defects in the code under review. Name any such tier to every sub-agent as off-limits.

Issue all analysis spawn_agent calls below in one batch, then collect their results with wait_agent. Do not issue one and await its result before issuing the rest. Each sub-agent inherits the parent model. Each sub-agent's prompt instructs it to invoke its assigned skill by reading and following the installed skill instructions, with the partition's file list passed in for partitioned skills, and to treat the shared working tree and its git index as read-only — any empirical check runs in an isolated git worktree created under $TMPDIR and discarded afterward. HEAD stays where it is: read other refs with git show <ref>:<path> rather than git checkout or git switch. Give that worktree its own dependency install rather than reaching the shared tree's install by any route: removing a worktree deletes through symlinks, and a redirected suite writes into the shared install. When its own install is not possible, the check is left unrun and reported as such. Afterward the sub-agent verifies that git worktree list no longer shows the worktree, that git status --short is clean, that HEAD is still on the branch it started on, and that the shared tree's dependency directory still resolves (a destroyed install leaves git status clean, since it is gitignored). Damage the sub-agent cannot repair is reported with the exact repair command in place of findings.

Expect (6 partitioned rows × number of partitions, plus 5 project-wide rows) Codex sub-agent calls total. State the count explicitly before emitting the batch.

Partitioned Skills

For each skill below, launch one sub-agent per partition with the partition's file list in the prompt. Pass (skip peer review) annotations through to $review-code as an opt-out so it runs internal reviews only — $peer-review is scheduled as its own row to avoid duplicate peer-review runs.

Skill	Scope
$review-code with correctness (skip peer review)	File list
$review-code with security (skip peer review)	File list
$review-code with api-usage (skip peer review)	File list
$review-code with consistency (skip peer review)	File list
$review-code with simplicity (skip peer review)	File list
$peer-review	File list

Project-Wide Skills

Skill	Notes
$review-code with coverage (skip peer review)	Project-wide
$review-dependencies	Project-wide
$review-tooling	Project-wide
$review-agentic-setup	Project-wide
$find-dead-code	Has its own partitioning

Step 4: Run $evaluate-findings Skill

Aggregate all findings from all agents. Run the $evaluate-findings skill once on the combined set.

Step 5: Generate Markdown Report

Write .turbo/audit.md using the template below. Populate the dashboard by counting findings per category and applying health thresholds. Output the dashboard as text before writing the file.

Report Template

# Audit Report

**Date:** <date>
**Scope:** <what was audited>

## Dashboard

| Category | Health | Findings | Critical |
|---|---|---|---|
| Correctness | <Pass/Warn/Fail> | <N> | <N> |
| Security | <Pass/Warn/Fail> | <N> | <N> |
| API Usage | <Pass/Warn/Fail> | <N> | <N> |
| Consistency | <Pass/Warn/Fail> | <N> | <N> |
| Simplicity | <Pass/Warn/Fail> | <N> | <N> |
| Test Coverage | <Pass/Warn/Fail> | <N> | <N> |
| Dependencies | <Pass/Warn/Fail> | <N> | <N> |
| Tooling | <Pass/Warn/Fail> | <N> |  |
| Dead Code |  |  |  |
| Agentic Setup |  |  |  |
| Threat Model |  | — | — |



  — zero P0/P1 findings in this category
  — P1 findings present but no P0
  — P0 findings present

Step 6: Generate HTML Report

Convert the markdown report into a styled, interactive HTML page.

1. Run the $frontend-design skill to load design principles.
2. Read .turbo/audit.md for the full report content.
3. Write a self-contained .turbo/audit.html (single file, no external dependencies beyond Google Fonts) that presents all findings from the markdown report with:
   • Dashboard health grid with severity color-coding (red=Fail, amber=Warn, green=Pass)
   • Severity summary bar (P0/P1/P2/P3 counts)
   • Sticky navigation between report sections
   • Collapsible category sections
   • Finding tables with file, line, and description columns
   • Severity badges and color-coded group labels
   • Entrance animations and hover states
   • Print-friendly styles via @media print
   • Responsive layout for mobile

Rules

• If any skill is unavailable or fails, proceed with findings from the remaining skills and note the failure in the report.
• $peer-review covers all concerns (correctness, security, api-usage, consistency, simplicity, coverage). Distribute its findings into their matching category sections. Deduplicate findings that overlap with the specialized reviewers.
• Does not modify source code, stage files, or commit.