SkillEvaluator

Run the SkillEvaluator CLI on agent skills and report what the evidence shows: Tier 1 validation (static, security, quality), Tier 2 deduplication, and Tier 3 live agent evaluation that compares runs with and without the skill.

Purpose

Give an agent one reliable procedure for evaluating skills with SkillEvaluator: pick the right tier, check readiness, run it, and report scores, findings and exit codes truthfully.

When To Use

Use this skill when the user wants to:

• check whether a skill is well-formed, safe, and good enough to publish
• get a quality score, rubric score, security or PII scan for a skill
• find repeated guidance inside a skill or overlap across a skill collection
• measure whether a skill actually helps an agent (Skill Lift, pass@k)
• write or fix <skill-dir>/evals/evals.json, <skill-dir>/evals/config.yml, custom graders, or Harbor tasks
• read a SkillEvaluator report, verdict, or exit code
• add SkillEvaluator checks to GitHub Actions or GitLab CI

Do not use it to write the target skill's domain content, to review ordinary application code, or to evaluate an LLM without a skill. For converting an existing benchmark into a custom grader, read references/custom-graders.md.

Prerequisites

• A target directory containing SKILL.md (or a folder of skills, .mdc rules, or a plugin manifest).
• The CLI: pip install --upgrade "skillevaluator[all]", or from a checkout uv run --python 3.13 --extra all skillevaluator .... Check with skillevaluator --version. Extras: llm (rubric), security (scanners), tier2, tier3, all, dashboard.
• No key for keyless commands (quality-check, security-scan, pii-scan, lint-scripts, tier1 PATH --no-llm, validate PATH --tiers 1).
• A provider for LLM stages, Tier 2 and Tier 3. Simplest: export SKILL_EVAL_LLM_PROVIDER=nv_build and export NVIDIA_API_KEY=.... Other providers: references/providers.md.
• For Tier 3: an agent (opencode, codex, or claude-code) and a sandbox. Harbor is installed with the tier3 extra; you do not run it yourself. The default --env-mode docker needs a Docker daemon. Without Docker, use --env-mode local (experimental): it runs the agent CLI already installed on the host under Bubblewrap (Linux) or Seatbelt (macOS); trusted skills only.

Never print, echo, or commit API keys; keep them in the host environment.

Choose The Command

Full flag tables: Tier 1, validate, Tier 2, Tier 3 and setup.

Workflow

1. Locate the target. Confirm the path has SKILL.md and that its frontmatter name matches the directory name. For a folder of skills, the commands evaluate each skill inside it.
2. Check readiness before paid runs. Run skillevaluator health-check --tier 1 for scanners, skillevaluator doctor --tier 2 for providers, and skillevaluator doctor --env-mode docker (add --verify-models) for Tier 3. Fix every failing row first.
3. Start keyless. Run skillevaluator tier1 <skill-dir> --no-llm. Fix CRITICAL and HIGH findings before anything else; lint findings are advisory.
4. Add LLM checks when a provider is set: skillevaluator tier1 <skill-dir> (rubric, LLM security, verification) and skillevaluator tier2 <skill-dir>.
5. Prepare Tier 3 cases. If <skill-dir>/evals/evals.json is missing, generate one with skillevaluator create-eval-dataset <skill-dir> --full, then review it: positive cases (explicit, implicit, contextual) and at least one negative case with "expected_skill": null. Required shape: top-level skill_name and an evals array; each case has id, prompt, expected_output, and expected_skill (optional assertions, files). Validate with skillevaluator tier3 validate <skill-dir> --strict. Format: references/tier3-datasets.md; run policy and scoring: references/tier3-running.md.
6. Run live evaluation. skillevaluator tier3 <skill-dir> (or skillevaluator tier3 evaluate <skill-dir> -a opencode --env-mode docker --n-attempts 3). Use --max-cases 1 for a cheap smoke run first; such runs are never publication evidence.
7. Read the results (see below), open skillevaluator view <skill-dir>, and for follow-up questions run skillevaluator tier3 feedback <run-dir>.
8. Iterate. Change the skill, rerun the same command with the same cases, agent and model, and compare. Treat a single run's lift as directional.

Read The Results

Exit codes: 0 success, 1 a gate failed, evidence is INCOMPLETE, or the run could not complete, 2 usage error (bad flag or missing path; fix the command, not the skill).

• Quality (0-100): Correctness 35%, Discoverability 25%, Reliability 25%, Efficiency 15%. Grades A>=90, B>=80, C>=70. Passing needs --min-score (default 70) and no HIGH/CRITICAL quality finding.
• Rubric (0-100): nine criteria scored 0-10; passing needs the weighted score >= --min-score and every criterion >= 7.
• Tier 2: HIGH or CRITICAL duplicate findings exit 1 in tier2 PATH.
• Tier 3: five dimensions (Security, Correctness, Discoverability, Effectiveness, Efficiency), each PASS >= 0.50, NEUTRAL 0.40-0.50, FAIL < 0.40. Verdict PASS needs every dimension >= 0.50 for at least one agent. Skill Lift = with-skill overall - baseline overall; PASS >= +0.05. INCONCLUSIVE: skill never activated means no positive trial loaded the skill; fix the description or prompts, not the scores. tier3 PATH exits 0 on a FAIL verdict; only an incomplete run exits 1.
• Missing scores are NO SCORE or None, never 0. Do not average them in.
• Publication: require publication_status == "pass" in the JSON report, not just overall_passed.

Report anatomy and JSON fields: references/reports-and-ci.md.

Examples

# Keyless first look
skillevaluator quality-check <skill-dir> -r cli -r json -o <report-dir>

# Static gate for a whole collection, complete report even on failures
skillevaluator validate <skills-dir> --profile default --tiers 1 -c -r json,markdown -o reports

# LLM-backed Tier 1 and Tier 2, dedup advisory
skillevaluator validate <skill-dir> --tiers 1,2 --llm --no-block-on-dedup

# Live A/B evaluation with two agents, retaining trajectories for debugging
skillevaluator tier3 evaluate <skill-dir> -a opencode,codex --n-attempts 3 --harbor-keep-jobs

# Baseline only, to calibrate cases before the skill exists
skillevaluator tier3 evaluate <skill-dir> --skip-with-skill

Limitations

• Tier 1 and the quality score are static: they check structure and wording, not whether the skill works. Only Tier 3 observes agent behavior.
• LLM judges (rubric, Tier 3 correctness/effectiveness) vary between runs and models; compare runs only with the same model, cases, and attempts.
• Tier 3 costs model tokens and sandbox time per case x attempt x agent x 2 arms; a generated starter case gives limited coverage.
• validate --tiers always includes Tier 1; --tiers 2 or --tiers 3 alone is rejected. Use tier2 PATH or tier3 PATH to run a tier on its own.
• Agent credentials come only from the host environment; <skill-dir>/evals/config.yml runtime_env rejects provider keys.

Troubleshooting

More cases: references/troubleshooting.md.

Final Response

Report to the user:

• the exact commands run and their exit codes
• scores and verdicts per tier, quoting the report values (not estimates)
• the top findings to fix, each with its file and suggested change
• report paths (<report-dir>/..., <skill-dir>/evals/results/<run-id>/report.html)
• what was not run (skipped tiers, missing keys, --max-cases smoke runs) and therefore not proven