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 ahnafyy/skills-evals --skill setup-skills-evalsgit clone --depth 1 https://github.com/ahnafyy/skills-evalsWrote 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/ahnafyy/skills-evals/setup-skills-evals)<a href="https://agentmods.dev/skills/ahnafyy/skills-evals/setup-skills-evals"><img src="https://agentmods.dev/badge/skills/ahnafyy/skills-evals/setup-skills-evals/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/ahnafyy/skills-evals/setup-skills-evals"><img src="https://agentmods.dev/badge/skills/ahnafyy/skills-evals/setup-skills-evals.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 | $0.00087 | $0.01624 |
| Opus 5 | $0.00044 | $0.00812 |
| Sonnet 5 | $0.00017 | $0.00325 |
| Haiku 4.5 | $0.00009 | $0.00162 |
Grade A, and why
setup-skills-evals 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 10d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Setting up skills-evals
You are helping the user adopt skills-evals — a zero-dependency library that validates, trigger-tests, and regression-tests agent artifacts (skills, instructions, custom agents, Cursor rules, prompt files). Follow this workflow in order. Ask the interview questions one at a time and wait for answers.
Step 1 — Inventory what exists
npx skills-evals list
Show the user the output. If nothing is discovered, stop and help them create their first artifact (e.g. a SKILL.md) before setting up evals.
Step 2 — Interview the user
Ask these questions one at a time:
- "Which of these do you want to write eval tests for?" — show the discovered artifacts. For each one they pick, also ask:
- "What are 3–5 things users actually say that should trigger it?" (realistic phrasing, not the description restated)
- "What nearby asks should it NOT handle, and which artifact owns those instead?"
- "Is this repo on CI?"
- GitHub Actions → offer the
ahnafyy/[email protected]action or a plainnpx skills-evals runstep. - Other CI (CircleCI, GitLab, Jenkins…) → add
npx skills-evals runto their existing pipeline config. - No CI → skip; local runner only.
- GitHub Actions → offer the
- "How do you want to run it locally?" — offer to hook into whatever they use:
- npm/pnpm/yarn → add a script to
package.json - Gradle → add an
Exectask - Make → add a target
- plain shell → create an
evals.sh
- npm/pnpm/yarn → add a script to
Step 3 — Scaffold and fill the eval cases
npx skills-evals init
This creates evals/cases/<name>.json stubs. Fill each one from the interview answers, following these rules:
- ≥ 3
positiveprompts. Use the user's real phrasing. Never copy the artifact's description — that games the eval. Use"top_k": 1for the artifact's signature ask. - ≥ 2
negativeprompts, each with"owner": "<other-artifact>"when another artifact should win — that makes it a real pairwise routing test. - For glob-routed artifacts (
applyToinstructions, Cursorglobsrules) use{ "path": "src/example.ts" }entries instead of prompts. - Optionally add behavioral
evals[](Anthropic skill-creator schema):prompt,expected_output,expectations[], andfiles[]pointing intoevals/fixtures/. Mark fixture-less evals"trust_level": "provisional".
What ships with it
1 file 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.
- 10d ago First seen · 141 lines · 87 tokens per session scan A 800f7353d28d
setup-skills-evals is a skill published in the GitHub repository ahnafyy/skills-evals (4 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 1,624 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.
Other skills, from other repositories
evaluate-skill
Measure a skill's reliability — run it k times for a pass@k score, design or interpret its eval, or compare it against the base agent. Use when the user wants to run, design, or interpret a skill's eval, or write an .eval.yaml spec.
claude-code-smoke
Use when asked to run the Claude Code smoke evaluation.
grill-skill
Build and harden a skill with evals — interview to design its eval tasks, then run, measure, and iterate. Use when the user wants to create or improve a skill's eval, or run the create → test → improve loop for a skill.
agent-eval-skills
Evaluate an existing agent skill with Promptfoo-native behavior, routing, or role-play tests. Use when a skill needs executable evidence that its product promise works. Do not use to author the domain skill itself.
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code.
test-driven-development
Drives development with tests using the red-green-refactor loop. Use when implementing any logic, fixing any bug, or changing any behavior. Use when you need to prove that code works, when a bug report arrives, or when you're about to modify existing functionality.