Évaluation de compétence j-rig

Exécute l'évaluation binaire j-rig à sept couches sur une compétence Claude et rapporte une décision de déploiement ou non.

Spar Skills Guide Bot
TestingAvancé
1023/07/2026
Claude Code
#evaluation#qa#skill-validation#binary-gate

Recommandé pour


name: j-rig-eval description: Runs the j-rig seven-layer binary evaluation on a Claude skill and reports a ship or no-ship rollout decision. Activates when a user asks to evaluate, grade, score, or gate a SKILL.md before release.

j-rig Skill Evaluation

Evaluate a Claude skill (SKILL.md) with the j-rig binary-eval harness and return a clear ship or no-ship decision backed by evidence.

When to use this skill

Use it whenever someone wants to know whether a skill change is safe to ship — before opening a release PR, after editing a skill body, or when gating a skill in CI. The decision is binary: every criterion is a yes or no, and a single blocker failure blocks release regardless of the average.

Instructions

  1. Locate the skill directory that contains the SKILL.md under evaluation and the eval contract that lists its criteria and test cases.
  2. Run the full seven-layer evaluation against the skill: package integrity, trigger quality, functional execution, judgment, scoring, evidence persistence, and the rollout report.
  3. Read the rollout decision. A ship decision means every blocker passed and no regression-critical criterion failed. A block decision means at least one blocker failed. A warn decision means non-blocking criteria need attention. An obsolete_review decision means the naked model matched the skill and the skill may add no value.
  4. Report the decision plainly, then list any failing criteria with their reasons so the author knows exactly what to fix.

Decision rules

  • A blocker failure cannot be averaged out by passing criteria — it blocks.
  • A regression on a sacred case blocks release even if the overall score rose.
  • Observed behavior outranks claimed behavior: grade what the skill actually produced, not what its description promises.
  • The evaluator is always separate from the skill under test; the skill never judges itself.

Examples

Input: a request to evaluate a commit-message skill before release. Output: a ship-or-no-ship decision plus the per-criterion pass and fail rows, with one-line reasons for every failure.

Input: a request to gate a skill in a pull request. Output: a no-ship decision naming the single blocker that failed, so the author can fix exactly that one thing and re-run.

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