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 gustavo-meilus/aiboarding --skill audit-agent-onboardinggit clone --depth 1 https://github.com/gustavo-meilus/aiboardingWrote 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/gustavo-meilus/aiboarding/audit-agent-onboarding)<a href="https://agentmods.dev/skills/gustavo-meilus/aiboarding/audit-agent-onboarding"><img src="https://agentmods.dev/badge/skills/gustavo-meilus/aiboarding/audit-agent-onboarding/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/gustavo-meilus/aiboarding/audit-agent-onboarding"><img src="https://agentmods.dev/badge/skills/gustavo-meilus/aiboarding/audit-agent-onboarding.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 | $0.00076 | $0.00864 |
| Opus 5 | $0.00038 | $0.00432 |
| Sonnet 5 | $0.00015 | $0.00173 |
| Haiku 4.5 | $0.00008 | $0.00086 |
Grade A, and why
audit-agent-onboarding 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditing agent onboarding files
Static + cross-reference linter for instruction-file smells. Read-only: this
skill never writes files. It produces a findings report; applying fixes is
update-agent-onboarding's job (content) or the user's (structure).
Announce at start: "Using audit-agent-onboarding to lint the onboarding files."
Usage: audit-agent-onboarding [--stats]
--stats: compression receipts
Read .aiboarding/state.json:receipts and render a table: file, level, bytes and
lines before/after, percent saved, measured-at. Label token figures approximate
when the receipt does (they are byte/4 estimates unless a real tokenizer produced
them). Since instruction files load every session, per-session savings compound -
present "per-session saved × sessions" only as a clearly labeled estimate. For each
receipt, render optional high_consequence_regions evidence as location, category,
outcome (preserved or rewritten), and explicit opt-in status, without instruction
text. Render an empty array as none; when field is absent, render not recorded.
Keep existing level, byte, line, date, and token output unchanged. Then stop.
Evidence-aware audit
Run .aiboarding/tools/audit-onboarding-evidence <repo-root> first (fallback:
the plugin's templates/tools/audit-onboarding-evidence). Capture its lines and
exit status. Exit 0 means no computed FAIL; 1 means computed validation FAIL;
2 is an operational error: stop and report tooling failure, never render it as an
audit finding. Do not write onboarding files in either case.
Each finding has independent severity (FAIL, WARN, INFO) and provenance
(computed, inferred). Preserve validator provenance and render one report
ordered FAIL, WARN, INFO. Never upgrade model judgment to computed.
| Category | Evidence |
|---|---|
| Local size budget; Codex project-chain budget; wrapper integrity | Computed |
| Stale commands | Mixed: validator only for explicit paths, package scripts, Make/Just targets; infer unresolved context-dependent cases |
| Duplication; contradictions; vague commands; missing sections; skill leakage; lint leakage; rules extraction candidates; unsafe content | Inferred |
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 · 68 lines · 76 tokens per session scan A a9f77d14c198
audit-agent-onboarding is a skill published in the GitHub repository gustavo-meilus/aiboarding (2 stars, last pushed 12d ago), licensed MIT. It adds 76 tokens to every session and 864 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.
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