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 arcasilesgroup/ai-engineering --skill ai-audit-docsgit clone --depth 1 https://github.com/arcasilesgroup/ai-engineeringWrote 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/arcasilesgroup/ai-engineering/ai-audit-docs)<a href="https://agentmods.dev/skills/arcasilesgroup/ai-engineering/ai-audit-docs"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-audit-docs/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/arcasilesgroup/ai-engineering/ai-audit-docs"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-audit-docs.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 | $0.00115 | $0.01624 |
| Opus 5.5 | $0.00046 | $0.00650 |
| Sonnet 5.5 | $0.00023 | $0.00325 |
| Haiku 4.5 | $0.00012 | $0.00162 |
Grade A, and why
ai-audit-docs scanned grade A with 1 finding 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 22d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
(`git`, `ls`, `cat`, `curl`, `jq`, etc.) -> **warning** finding. How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit docs against repo reality
Lifecycle
Lane: light Writes: nothing — report-only, never modifies files Read by: humans (consumed as a findings table) Dies: on completion Next: ai-verify (to validate fixes if user acts on findings)
What it produces
A ranked markdown table of findings. Every finding names the doc file, the line, the severity, and the broken claim.
| Severity | Meaning |
|---|---|
| critical | Doc points users at something that does not exist — they will hit a wall |
| warning | Claim is outdated or inaccurate but the doc is still roughly usable |
| info | Cosmetic: stale version number, unused anchor, minor nit |
Steps
-
Determine scope. If the user gave a path, audit that. Otherwise default to
README.md,AGENTS.md, and anydocs/directory at the repo root. Glob for**/*.mdunder docs if the directory exists. Never audit vendored or generated files (node_modules/,dist/,*.generated.*). -
Read every target doc. Extract all of these per file:
- File links: markdown links pointing at local paths (e.g.
./path,../path,path/to/file). Skip anchors (#section) and bare URLs — those are checked in step 3. - External URLs: bare
http:///https://links. Collect for batch HEAD-check. - Command references: inline code or code blocks containing shell commands
(
npm run X,yarn X,make X,cargo X,python -m X,npx X). Strip arguments; extract just the command name. - Version strings:
v1.2.3,1.2.3,>=1.2.3, pinned deps in prose. - Feature claims: sentences that assert existence — "supports X", "includes X", "has X", "provides X", "X is built-in", "X is enabled by default".
- File links: markdown links pointing at local paths (e.g.
-
Cross-check file links. For each local path extracted from a markdown link:
- Resolve it relative to the doc file's directory.
findorreadto confirm the target exists. If not -> critical finding.
-
Cross-check command references. For each command name:
grepthe repo for a script/package-bin definition (package.jsonscripts,Makefiletargets,Cargo.tomlbin,setup.py/pyproject.tomlentry points, shell scripts inbin/orscripts/).- If no definition exists AND the command is not a well-known system binary
(
git,ls,cat,curl,jq, etc.) -> warning finding. - If a script IS defined, check that the arguments the doc uses still match the script's actual flags — flag mismatches -> info.
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.
- 22d ago First seen · 130 lines · 115 tokens per session scan A 2753344016c4
ai-audit-docs is a skill published in the GitHub repository arcasilesgroup/ai-engineering (61 stars, last pushed 2d ago), licensed Apache-2.0. It adds 115 tokens to every session and 1,624 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-24.
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