audit

A codebase audit that measures project quality, counts individual problems, and ranks possible fixes by how many score points each iteration may address.

In plain words
What is it for?
Finding deficiencies in type checks, builds, tests, and linting, then choosing what to fix first.
Why use it?
It turns broad code-quality issues into a prioritized repair list and can continue into focused improvement loops.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/benmarte/autoimprove/audit
Any agent
npx skills add benmarte/autoimprove --skill audit
Clone the repo
git clone --depth 1 https://github.com/benmarte/autoimprove

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,536 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00040 $0.01536
Opus 5 $0.00020 $0.00768
Sonnet 5 $0.00008 $0.00307
Haiku 4.5 $0.00004 $0.00154

Measured 2d ago against content hash 0ee37b41d39a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

audit scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/audit/SKILL.md · 154 lines

How it starts

The opening of the file, as written. The whole thing — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Audit Skill

Pre-flight

  1. Check .claude/autoimprove/config.md exists. If not, stop: "Run /autoimprove:setup first."
  2. Check git is available and working tree is clean.

Step 1: Run Measurement Suite

Run the measure skill to get the composite score and per-metric breakdown. Capture both the scores AND the raw command output for each metric.

For each metric defined in the config:

  • Run the command (e.g., tsc --noEmit 2>&1, pnpm test 2>&1, pnpm lint 2>&1)
  • Record the score (using the measure skill's scoring logic)
  • Also capture the raw output for deficiency counting

Step 2: Count Individual Deficiencies

Parse the raw output from each command to count specific issues:

  • Type errors: Count lines matching error patterns (e.g., error TS for TypeScript, error: for Rust). Group by file.
  • Build: Pass/fail only — no granular count. If build fails, it becomes top priority.
  • Tests: Count passing vs total from test runner output. Identify failing test names if any.
  • Lint: Count warning/error lines from linter output. Group by rule if possible.

Step 3: Calculate Efficiency

For each metric with a gap (score < max):

  1. Point gap = max_weight - current_score
  2. Estimated iterations = ceil(issue_count / issues_per_iteration) using these heuristics:
    • Type errors: ~3 per iteration (clustered in files)
    • Lint warnings: ~2.5 per iteration
    • Tests (new): ~1 per iteration
    • Build fix: ~1-2 iterations
    • These are initial estimates — actual results will vary
  3. Efficiency = point_gap / estimated_iterations
  4. Estimated tokens = estimated_iterations × 22000 (rough average per iteration)

Read weights dynamically from .claude/autoimprove/config.md. If a metric is not applicable, skip it and redistribute weight as the measure skill already handles.

Sort areas by efficiency (highest pts/iteration first).

Step 4: Display Report

Print the audit report:

━━━ Codebase Audit ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📊 Current Score: XX/100

  [Metric 1]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed
  [Metric 2]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed
  [Metric 3]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed
  [Metric 4]:  XX/WW  [progress bar]  (gap pts to max) or ✓ maxed

━━━ Fastest Path to 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  #  Area          Gap    Issues  Est. iterations  Efficiency
  1  [best area]   Xpts   N items  M iterations    X.X pts/iter ← best
  2  [next area]   Xpts   N items  M iterations    X.X pts/iter
  ...

  Total: ~N iterations to reach 100/100
  ⚡ Estimated token usage: ~XXXK tokens (rough estimate, actual usage varies)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Read the full file on GitHub · 154 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 154 lines · 40 tokens per session scan A 0ee37b41d39a

Subscribe to this mod's changes

audit is a skill published in the GitHub repository benmarte/autoimprove (5 stars, last pushed 5mo ago), licensed MIT. It adds 40 tokens to every session and 1,536 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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