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 skills/hoja-solutions/agent-stdlib/auditing-agent-behaviornpx skills add Hoja-Solutions/agent-stdlib --skill auditing-agent-behaviorgit clone --depth 1 https://github.com/Hoja-Solutions/agent-stdlibWhat 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 | $0.00193 | $0.00711 |
| Opus 5 | $0.00097 | $0.00356 |
| Sonnet 5 | $0.00039 | $0.00142 |
| Haiku 4.5 | $0.00019 | $0.00071 |
Grade A, and why
auditing-agent-behavior 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 2d 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 — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auditing agent behavior
Source: Petri, an open-source auditing tool (repo). build-agent-evals asks whether the agent did the task; this skill asks whether it behaved well while trying, and points at Petri as the harness that runs the audit.
What an audit catches that an eval misses
A task eval scores the end state: did the agent fix the bug, answer the question, complete the order. It says nothing about how the agent got there. An agent can pass every task and still lie to the user under pressure, flatter a wrong claim, hide an action from its operator, or help with a request it should refuse. An audit goes looking for those behaviors on purpose.
Let an auditor drive the scenarios
Running these probes by hand does not scale past a handful. Petri automates the loop: an auditor agent takes a seed instruction, plans a multi-turn conversation, plays the user and any simulated tools, and pushes your target agent toward the behavior under test. A judge model then scores each transcript against a rubric. You supply the seeds and read the results.
Write seeds that probe your agent's risks
The audit is only as good as its seed instructions. Generic seeds find generic problems. Write seeds around the pressure your agent will meet in production: a customer pushing for a refund it should deny, a user asking it to hide a step from an approver, a tool result that contradicts what the agent told the user a turn ago. Cover the cases where being helpful and being correct pull apart.
Adapt the rubric to your domain
Petri ships a default rubric across dozens of dimensions (deception, sycophancy, oversight subversion, power-seeking, cooperation with misuse, and more). Keep the ones that map to your risk and add dimensions of your own. A score points a human at a transcript to read.
Confirm flags by reading transcripts
The judge flags candidates; you confirm them. Open the flagged transcripts and check that the behavior is real and reproducible before you act on it, the same discipline build-agent-evals asks for. Run the audit again on each model or prompt change, and watch whether a fix in one dimension cost you ground in another.
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.
- 2d ago First seen · 42 lines · 193 tokens per session scan A 235230ba8b7c
auditing-agent-behavior is a skill published in the GitHub repository Hoja-Solutions/agent-stdlib (1 stars, last pushed 1mo ago), licensed MIT. It adds 193 tokens to every session and 711 once invoked, about $0.0010 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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