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.
npx skills add MinhThang1009/dotclaude --skill audit-logicgit clone --depth 1 https://github.com/MinhThang1009/dotclaudeWrote 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.
[](https://agentmods.dev/skills/minhthang1009/dotclaude/audit-logic)<a href="https://agentmods.dev/skills/minhthang1009/dotclaude/audit-logic"><img src="https://agentmods.dev/badge/skills/minhthang1009/dotclaude/audit-logic/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/minhthang1009/dotclaude/audit-logic"><img src="https://agentmods.dev/badge/skills/minhthang1009/dotclaude/audit-logic.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00111 | $0.07275 |
| Opus 5 | $0.00056 | $0.03638 |
| Sonnet 5 | $0.00022 | $0.01455 |
| Haiku 4.5 | $0.00011 | $0.00728 |
Grade A, and why
audit-logic 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Logic Skill
Perform a systematic, line-by-line logic audit of the target module. The target is the path passed as the skill argument (<module-path-or-directory>); if no argument was given, ask the user for the target before starting. The goal is to find real bugs — not style issues, not theoretical edge cases — bugs that cause wrong behavior, data corruption, race conditions, or incorrect business rule enforcement in production.
Language-agnostic and framework-agnostic. Adapt discovery, test commands, and doc search to whatever stack exists in the target directory.
Phase 1 — Discover
-
List every source file in the target directory (exclude test files, generated files, build artifacts, lock files). If the target path does not exist, is a single file rather than a directory (and the user did not explicitly target one file), or zero source files remain after exclusions → stop and ask the user before doing anything else (do NOT create the gate state file). Print the full list — and note alongside it which test files will also be read in Phase 2 — so the user sees the true audit scope before proceeding.
-
Find the project's primary documentation (CLAUDE.md, README.md, architecture docs, or equivalent). Read the section describing what this module does and what business rules it must enforce. This establishes the expected behavior to audit against — print a 1–3 line summary of those rules (or "no module docs found") so the user sees the baseline the audit will check the code against.
-
Identify and run the existing test suite for this module to establish a green baseline:
- Find the test runner and config (jest.config, pytest.ini, go test, etc.)
- Run only the tests relevant to this module if possible
- If no test files exist → note this explicitly. Proceed with code-only analysis. All fixes will be unverified by automated tests — flag this prominently in the Phase 7 summary.
- If tests are already failing before any changes → stop, report the failures, and ask the user whether to continue with code-only analysis
- If tests cannot be run (missing environment, requires external services, CI-only) → note this explicitly and proceed with code-only analysis. Flag at the end that fixes should be verified in the appropriate environment.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 302 lines · 111 tokens per session scan A c7acc7358689
audit-logic is a skill published in the GitHub repository MinhThang1009/dotclaude (20 stars, last pushed 1mo ago), licensed MIT. It adds 111 tokens to every session and 7,275 once invoked, about $0.0006 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 2026-08-30.
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