A repository-wide consistency review that uses four specialist agents to look for factual mistakes, code bugs, incorrect counts, and documents that disagree.

Auditing repository infrastructure, checking mechanical rules, fixing inconsistencies, and preparing for major releases.

It helps find problems spread across many files after broad changes or before a release, then repeats the checks until the repository is clean.

deep-audit — Skill for Claude Code by pedrohcgs

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo

git clone --depth 1 https://github.com/pedrohcgs/Claude-Mini

agentmods

npx agentmods add skills/pedrohcgs/claude-mini/deep-audit

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/pedrohcgs/claude-mini/deep-audit.svg)](https://agentmods.dev/skills/pedrohcgs/claude-mini/deep-audit)

Your own site

<a href="https://agentmods.dev/skills/pedrohcgs/claude-mini/deep-audit"><img src="https://agentmods.dev/badge/skills/pedrohcgs/claude-mini/deep-audit.svg" alt="Measured on agentmods" height="20"></a>

Per session 76 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,499 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00076	$0.02499
Opus 5	$0.00038	$0.01249
Sonnet 5	$0.00015	$0.00500
Haiku 4.5	$0.00008	$0.00250

Measured 8d ago against content hash fd04f3570de2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

deep-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 8d 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.

.claude/skills/deep-audit/SKILL.md · 181 lines

How it starts

The opening of the file, as written. The whole thing — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/deep-audit — Repository Infrastructure Audit

Run a comprehensive consistency audit across the entire repository, fix all issues found, and loop until clean.

When to Use

After broad changes (new skills, rules, hooks, guide edits)
Before releases or major commits
When the user asks to "find inconsistencies", "audit", or "check everything"

Workflow

PHASE 0: Mechanical checks (run FIRST, cheap, deterministic)

Before spawning agents, run the mechanical parity checks:

python3 scripts/check-skill-integrity.py --verbose

This catches four classes of bug that agent-based audits have historically missed:

Frontmatter allowed-tools ↔ body tool-invocation parity (e.g. body spawns Task but Task not in allowed-tools — the v1.7.0 PR #92 miss).
argument-hint ↔ body flag parity (flags documented but not advertised, or vice versa).
Internal markdown anchors resolve (no broken [text](path#anchor) links — the #category-11-numerical-discipline miss on PR #87).
Rule paths: ↔ skill implementation parity (rule claims skill follows protocol but skill body has none of the protocol keywords — the /interview-me miss on PR #92).

If Phase 0 reports P0 or P1 findings, fix them (or tune the regex if they are false positives) before launching the 4 agents. The mechanical layer is cheaper and more precise than agent prompts for these classes.

PHASE 1: Launch 4 Parallel Audit Agents

Launch these 4 agents simultaneously using Task with subagent_type=general-purpose. Each agent's prompt must tell it to read .claude/references/audit-pet-peeves.md and explicitly check for each class of bug before reporting clean. The pet-peeves file is a living catalogue of drift patterns review bots have caught; it grows with each PR.

Agent 1: Guide Content Accuracy

Focus: guide/workflow-guide.qmd

All numeric claims match reality (skill count, agent count, rule count, hook count)
All file paths mentioned actually exist on disk
All skill/agent/rule names match actual directory names
Code examples are syntactically correct
Cross-references and anchors resolve
No stale counts from previous versions

Read the full file on GitHub · 181 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.

8d ago First seen · 181 lines · 76 tokens per session scan A fd04f3570de2

Subscribe to this mod's changes

deep-audit is a skill published in the GitHub repository pedrohcgs/Claude-Mini (11 stars, last pushed 4mo ago), licensed MIT. It adds 76 tokens to every session and 2,499 once invoked, about $0.0004 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.

Code review Debugging

adversarial-reviewer

Adversarial code review that assumes bugs exist and hunts for them. Use when asked to review code, find bugs, audit for correctness, stress-test a PR, or when someone says "tear this apart" or "what's wrong with this". Give no benefit of the doubt — every line is guilty until proven innocent.

emdash-cms/emdash · 71 tokens

gsd-ns-review

Route to the appropriate quality / review skill based on the user's intent. gsd-code-review-fix was absorbed by gsd-code-review --fix in #2790.

open-gsd/gsd-core · 16 tokens

issue

Use when starting a chain from a GitHub issue — turning an issue URL or number into a triaged, planned, dispatched, and reviewed pull request. Classifies the thread (bug → root-cause discipline, feature → plan chain, question → drafted reply), synthesizes a spec from the issue's own acceptance criteria, then runs the…

jeremylongshore/tons-of-skills-marketplace · 115 tokens

deep-audit