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 goiltpatpat/patpat --skill patpat-evalgit clone --depth 1 https://github.com/goiltpatpat/patpatWrote 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/goiltpatpat/patpat/patpat-eval)<a href="https://agentmods.dev/skills/goiltpatpat/patpat/patpat-eval"><img src="https://agentmods.dev/badge/skills/goiltpatpat/patpat/patpat-eval/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/goiltpatpat/patpat/patpat-eval"><img src="https://agentmods.dev/badge/skills/goiltpatpat/patpat/patpat-eval.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 | $0.00035 | $0.00570 |
| Opus 5 | $0.00017 | $0.00285 |
| Sonnet 5 | $0.00007 | $0.00114 |
| Haiku 4.5 | $0.00003 | $0.00057 |
Grade A, and why
patpat-eval 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 5d 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.
What it actually says
Patpat Eval
When invoked directly, read the operating protocol in full. Do not load the router.
Read proof over proxy and apply the behavioral evaluation playbook.
Define the target behavior and rubric before running a trial. Include at least one prompt that should trigger the skill and one neighboring prompt that should not. Run trials in isolated temporary workspaces with equivalent context, organic names, and no hidden access to the expected conclusion.
Judge produced artifacts, commands, observations, scope control, and cleanup. Do not treat an agent's explanation or confidence as evidence. Record environmental limits and keep comparisons sequential unless isolation and integration proof have earned parallel execution.
Freeze the rubric before the first trial. Record PASS only when inspectable evidence satisfies every predeclared criterion; record FAIL when observed behavior violates any criterion and INCONCLUSIVE when required evidence is missing or uninspectable. Never weaken or reinterpret the rubric after observing output. Do not rewrite a failed trial as a pass; record a corrected candidate as a new trial.
Promote the skill only when structural validation passes and the behavioral evidence receives PASS under the frozen rubric.
For a revision-bound Codex contract canary, run ../../scripts/probe_codex_behavior.py manually against a clean committed revision and an explicit requested model. Keep the generated JSONL and private receipt.json outside every Git worktree. The probe also writes a strict allowlisted attestation.json bound to the receipt and raw-evidence digests; inspect it before explicitly posting or uploading it. Generation alone does not make the attestation external evidence. Promote is the gate: scripts/publish_codex_attestation.py re-hashes raw receipt.json and event streams; generation is still not external. External inspectability is the Actions artifact, not a Git blob. The probe checks bounded task behavior, observed mutation-capable commands, and response-shape conformance; it does not prove independent review, runtime enforcement, host-attested skill activation, every route, the resolved provider snapshot, or every possible transient side effect. Its producer timestamps are wall-clock observations, not trusted timestamps. Apply its result only to the recorded Codex version, requested model selection, Patpat revision, and Git tree. Do not transfer it to Cursor, Grok, Antigravity, or a later commit.
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.
- 5d ago Changed · +4 lines 5268ef250503
- 9d ago First seen · 17 lines · 35 tokens per session scan A 3c5983c75542
patpat-eval is a skill published in the GitHub repository goiltpatpat/patpat (1 stars, last pushed 3d ago), licensed MIT. It adds 35 tokens to every session and 570 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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