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 Stet-AI/stet-cli --skill stetgit clone --depth 1 https://github.com/Stet-AI/stet-cliWrote 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/stet-ai/stet-cli/stet)<a href="https://agentmods.dev/skills/stet-ai/stet-cli/stet"><img src="https://agentmods.dev/badge/skills/stet-ai/stet-cli/stet/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/stet-ai/stet-cli/stet"><img src="https://agentmods.dev/badge/skills/stet-ai/stet-cli/stet.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 | $0.00116 | $0.06323 |
| Opus 5.5 | $0.00046 | $0.02529 |
| Sonnet 5.5 | $0.00023 | $0.01265 |
| Haiku 4.5 | $0.00012 | $0.00632 |
Grade A, and why
stet 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 1mo 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
14 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.
- references/compare-and-checkin.md 52 KB
- references/dataset-build.md 83 KB
- references/dataset-factory.md 10 KB
- references/examples.md 13 KB
- references/full-evals.md 25 KB
- references/iterative-improvement.md 28 KB
- references/onboarding.md 40 KB
- references/operator-contract.md 17 KB
- references/quick-probe.md 9.6 KB
- references/release-lifecycle.md 8.4 KB
- references/rubric-authoring.md 8.0 KB
- references/rules-flow.md 69 KB
- references/rules-skill-loop.md 8.8 KB
- references/status-checkin.md 4.5 KB
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.
- 1mo ago First seen · 389 lines · 116 tokens per session scan A d3b5ffd4b61d
stet is a skill published in the GitHub repository Stet-AI/stet-cli (5 stars, last pushed yesterday), with no licence file. It adds 116 tokens to every session and 6,323 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
agent-eval
Use when measuring whether an LLM or agent system actually got better and gating merges on it: golden sets, fixing an inflated LLM-as-judge, scoring RAG (faithfulness, contextual recall) or agent trajectories (tool correctness, completion), or picking an eval framework. NOT building the agent loop, tools or RAG…
evaluate-skill
Run, read, and diagnose a skill's Caliper eval — its success rate over k attempts, whether it fires, and whether it beats the agent without it. Use when the user wants to run, validate, interpret, or compare a skill's eval, or write an .eval.yaml spec whose tasks they have already decided.
agentplane-testkit-migration
Use when migrating Agentplane tests to @agentplane/testkit, replacing local test helpers, fixing testkit export/build failures, splitting large CLI/backend suites, or diagnosing missing testkit dist exports in package builds.
grill-skill
Interview the user to decide what a skill's eval should test, then build the spec and iterate until the skill ships. Use when the user wants help designing a skill's eval, has a skill with no eval, or wants to find what an existing eval misses.
lastlight-evals
Scaffold, configure and run a Last Light EVALS workspace — the harness that runs Last Light's real workflows against a mocked GitHub and grades them deterministically. Use when the user wants to "set up / scaffold Last Light Evals", "create an evals workspace or instance", "run evals", "compare models", or author new…
repro-first
Bug-fix mode that reproduces a bug in a failing test before changing any code. Entered with /repro-first.