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 escoffier-labs/skillet --skill checkgit clone --depth 1 https://github.com/escoffier-labs/skilletWrote 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/escoffier-labs/skillet/check)<a href="https://agentmods.dev/skills/escoffier-labs/skillet/check"><img src="https://agentmods.dev/badge/skills/escoffier-labs/skillet/check/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/escoffier-labs/skillet/check"><img src="https://agentmods.dev/badge/skills/escoffier-labs/skillet/check.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 | $0.00069 | $0.01968 |
| Opus 5.5 | $0.00028 | $0.00787 |
| Sonnet 5.5 | $0.00014 | $0.00394 |
| Haiku 4.5 | $0.00007 | $0.00197 |
Grade A, and why
check 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.
This is a copy
92% identical to check — 26 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
check
"Check" is the expeditor's call: every plate gets looked at before it leaves the pass. Not the cook's word that it's right, not how it looked ten minutes ago - this plate, looked at now. This skill is that look, applied to any claim of done: run the thing, read the output, then say what the output says.
Core principle: evidence before claims, always. A completion claim without fresh verification is not optimism, it is a false report. Violating the letter of this rule is violating its spirit.
The iron law
No completion claim without fresh verification evidence in this same response. Have not run the proving command since the last change? Then "it works" is not available to you, and neither are its synonyms, paraphrases, or implications.
Brigade-wired repos: verify with capture
When the repo has .brigade/ (or brigade status --target . succeeds), do not run the proving command raw. Route it through Brigade so the exit code becomes a receipt the outcome ledger can score:
brigade work verify run --target . --command "<proving command>" --capture <skill-or-card-id>
# or after a brigade run that changed code:
brigade outcome capture <skill-or-card-id> --run-receipt latest
Then, when GraphTrail/MiseLedger are installed, feed the next run:
brigade receipts export miseledger --target . --new-only --import
Raw pytest / npm test proves the claim for this response but leaves Brigade dormant (outcome rank says "ranking: none"). The differentiated path is receipts that feed the next run's context with a measured brief hit rate. This skill owns the loop's evidence gate. The full loop and the other gates are defined in the Brigade process model.
The gate
Before any claim of success, satisfaction, or readiness:
- Name the command that proves the claim. "The script works" is proved by running the script, not by running its prettiest component.
- Run it, fresh and whole. After the last change, the full thing: the actual script, the whole suite, the real build. Stale runs and partial runs prove nothing about the present.
- Read the output and the exit code. All of it. Count the failures yourself.
- Compare claim to evidence. Output contradicts the claim? The evidence wins; report the actual state.
- Then claim, with the evidence attached. Quote the command you ran and its actual output lines in the report, not a paraphrase of them. "All 34 pass" next to the run that shows it.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +6 lines 3b87956dd8f6
- 1mo ago First seen · 104 lines · 69 tokens per session scan A c687f1ac3a30
check is a skill published in the GitHub repository escoffier-labs/skillet (4 stars, last pushed today), licensed MIT. It adds 69 tokens to every session and 1,968 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to check, differing in 26 lines, and is treated as a copy.
Other skills, from other repositories
craft-testing
Craftsman standard for automated testing: strategy, unit/integration/e2e selection, refactor-proof design, flaky tests, mocking boundaries, deterministic data, and merge-gate policy. Use WHENEVER work touches tests: writing/reviewing tests, strategy, "add tests", "why is this flaky", "what should I test", "tests pass…
critique
Adversarial design critique of the current work — render it, look at it, and argue for rejection. Run after the gates are green, never instead of them.
ship
Pre-release gate — run the full gate, responsive + render checks, then produce the release checklist (README badge/current/changelog). Use before tagging a release.
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
ai-eval-plan
Design an evaluation plan for an LLM or AI feature before shipping it. Use when asked how to evaluate a prompt/model/agent, set up an eval harness, define quality metrics for an AI feature, or build a regression gate. Produces an eval plan — task definition, datasets, metrics & rubrics, baselines, automated + human…
browser-test-loop
Use to actually verify a web change in a real browser instead of assuming it works — click through a flow, catch console errors, check a form submits, confirm a fix on the page. Trigger when a UI change needs proof, a bug only reproduces in the browser, or the user asks to test, check, or screenshot the running app.