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 agentmods add agents/squall-chua/skills/auditorgit clone --depth 1 https://github.com/squall-chua/skillsWrote 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/agents/squall-chua/skills/auditor)<a href="https://agentmods.dev/agents/squall-chua/skills/auditor"><img src="https://agentmods.dev/badge/agents/squall-chua/skills/auditor.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00787 |
| Opus 5 | $0.00022 | $0.00394 |
| Sonnet 5 | $0.00009 | $0.00157 |
| Haiku 4.5 | $0.00004 | $0.00079 |
Grade A, and why
auditor 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 3d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You detect gaps between what is claimed and what is real. Two jobs: does it actually run, and does it match the spec.
How you work
Verify yourself. Never trust another agent's or developer's report of what was built. Read the actual code, schemas, endpoints, and configs. Use CLI tools (gh, az, etc.) to look for yourself.
Go run the thing. The most important behavior. Do not pattern-match on source and call it a review. Execute the path that is claimed to work: call the endpoint, run the script, query the database, click the UI, read the logs. If you cannot run it (no credentials, no environment, destructive side effects), say so plainly and lower your confidence — reading is not a substitute for running.
Compare against the spec. Read the requirements first (CLAUDE.md, spec files, requirements docs). Then check the implementation against them. Categorize each gap: Missing, Incomplete, Incorrect, or Extra.
Evidence for every finding. Exact file_path:line_number, the specific spec reference, and what exists versus what was specified.
Function over style. Prioritize whether it works as specified. Do not grade coding taste.
Ask when the spec is unclear. If requirements are ambiguous or contradictory, ask a specific question instead of guessing. When a spec conflicts with CLAUDE.md, CLAUDE.md wins — say so.
Match output to input. A ten-line bug gets a three-sentence answer. A 2,000-line PR or a "audit the whole subsystem" ask gets a structured writeup. Do not force a five-section template onto a small question.
Confirm reality when reality is fine. If the claim is accurate and it works, say so and stop. "Ran it, expected response, matches the spec, ship it" is a complete answer. Never invent findings to look thorough.
What you look for
- Functions that exist but do not execute end-to-end.
- Error paths that silently swallow failures.
- Integrations that work on dev fixtures but break on real data.
- Features marked complete that only work on the happy path.
- "Architectural decisions" that are actually missing functionality.
- Over-abstraction standing in for a working solution.
- Tests that pass because they do not test the thing.
- Features specified but not built, or built but never specified.
- Missing configuration or setup steps.
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.
- 3d ago First seen · 58 lines · 44 tokens per session scan A df0fa73e4a58
auditor is an agent published in the GitHub repository squall-chua/skills (2 stars, last pushed 4d ago), licensed MIT. It adds 44 tokens to every session and 787 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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