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 d-oit/rust-2026-template --skill skill-evaluatorgit clone --depth 1 https://github.com/d-oit/rust-2026-templateWrote 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/d-oit/rust-2026-template/skill-evaluator)<a href="https://agentmods.dev/skills/d-oit/rust-2026-template/skill-evaluator"><img src="https://agentmods.dev/badge/skills/d-oit/rust-2026-template/skill-evaluator/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/d-oit/rust-2026-template/skill-evaluator"><img src="https://agentmods.dev/badge/skills/d-oit/rust-2026-template/skill-evaluator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00055 | $0.00988 |
| Opus 5.5 | $0.00022 | $0.00395 |
| Sonnet 5.5 | $0.00011 | $0.00198 |
| Haiku 4.5 | $0.00006 | $0.00099 |
Grade A, and why
skill-evaluator 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.
How it starts
The opening of the file, as written. The whole thing — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill Evaluator
Evaluate local skills with a repeatable loop: inspect structure, read eval definitions, run one or more realistic prompts, then score the output with explicit assertions and evidence.
Required Inputs
At minimum, identify:
SKILL_PATH: absolute or workspace-relative path to the skill directory
GOAL: structure check / eval review / live run / baseline comparison
Evaluation Workflow
1. Structure Check
Confirm the skill directory is sane before judging outputs.
Expected layout:
skill-name/
SKILL.md
evals/evals.json # recommended
references/ # recommended
scripts/ # optional but useful
Flag these issues explicitly:
- missing
SKILL.md - nested duplicate directory like
skill-name/skill-name/ evals/exists butevals/evals.jsonis missing or invalid JSON- eval cases missing
id,prompt, orexpected_output
2. Quantitative Evaluation
Consult .agents/metrics.jsonl to assess skill performance over time.
Analyze trends in:
- Success rate
- Human intervention rate
- Token usage and duration
3. Eval Review
Read evals/evals.json if present and assess whether each case is realistic.
Good evals include:
- a real user prompt
- a short success definition
- optional input files
- assertions that are concrete and checkable
Weak evals include:
- vague prompts
- purely subjective assertions
- no evidence path for pass/fail
4. Live Run
Run at least one representative prompt from the eval set or create a focused ad hoc prompt.
For each live run:
- load the target skill
- read only the files the skill itself points to
- produce the answer or output
- grade against assertions with evidence
5. Baseline Comparison
When useful, rerun the same prompt without the skill or against a snapshot of the older skill.
Compare:
- pass rate
- missing details
- format compliance
- time or token cost if available
6. Verdict
End with one of:
PASS— structure is sound and live output meets assertionsNEEDS_WORK— usable, but structure gaps or output gaps remainFAIL— skill is broken, misleading, or missing core pieces
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.
- 1mo ago Changed · -6 lines 7abb9d418f4e
- 2mo ago First seen · 172 lines · 55 tokens per session scan A 00c17771c40a
skill-evaluator is a skill published in the GitHub repository d-oit/rust-2026-template (14 stars, last pushed 3d ago), licensed MIT. It adds 55 tokens to every session and 988 once invoked, about $0.0002 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
clade-verification
Verification, testing, review, screenshots, and evidence rules. Use when testing, reviewing, debugging, collecting evidence, or claiming completion.
check
Run the full local quality gate (prek hooks + tox test matrix) before committing or opening a pull request. Use when asked to "check", "lint", "run CI locally", or verify a change is ready.
review
Review code changes, pull requests, patches, or a scoped code area for actionable correctness, security, compatibility, and test risks with file and line evidence. Use for review or audit requests; do not use for general proofreading, feature implementation, or debugging a reported failure when the user wants a fix.
debug
Diagnose a concrete failure, regression, crash, hang, flaky test, or incorrect runtime behavior by reproducing it, testing hypotheses, and identifying the evidence-backed root cause. Use when symptoms or failing output exist; do not use for feature implementation without a failure, general code review, or a conceptual…
review-backend
Performs a strict code review of Rust backend code (files, diffs, or snippets). Trigger whenever the user shares backend code (Rust, SQL) and asks for a review, feedback, or says "review this" — including partial snippets.
review-frontend
Performs a strict code review of the Svelte 5 frontend code (files, diffs, or snippets). Trigger whenever the user shares frontend code (Svelte, TypeScript, CSS) and asks for a review, feedback, or says "review this" — including partial snippets.