tobihagemann/audit

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:

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

Launch the analysis agents below in parallel. 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.

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> | <N> |
| Dead Code | <Pass/Warn/Fail> | <N> | <N> |
| Agentic Setup | <Pass/Warn/Fail> | <N> | <N> |
| Threat Model | <Present/Missing> | — | — |

### Health Thresholds

- **Pass** — zero P0/P1 findings in this category
- **Warn** — P1 findings present but no P0
- **Fail** — P0 findings present

## Detailed Findings

### Correctness
<findings from $review-code correctness>

### Security
<findings from $review-code security>

### API Usage
<findings from $review-code api-usage>

### Consistency
<findings from $review-code consistency>

### Simplicity
<findings from $review-code simplicity>

### Test Coverage
<findings from $review-code coverage>

### Dependencies
<findings from $review-dependencies>

### Tooling
<findings from $review-tooling>

### Dead Code
<findings from $find-dead-code>

### Agentic Setup
<findings from $review-agentic-setup>

### Threat Model
<status and summary>

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.