skill-evaluation
FeaturedEvaluate any agent skill against a merged framework — Anthropic's Claude Code best practices plus Matt Pocock's writing-great-skills methodology — across 4 axes (Trigger, Structure, Steering, Pruning). Produces an evidence-cited scorecard (0–100), a weighted overall score, and diagnosed failure modes with prioritized fixes. Use when the user asks to evaluate, rate, or audit a skill ("evaluate this skill", "skill scorecard", "review SKILL.md"), or to compare two skills.
Install
Quality Score: 88/100
Skill Content
Details
- Author
- fabricioctelles
- Repository
- fabricioctelles/skills
- Created
- 7 months ago
- Last Updated
- 4 days ago
- Language
- Python
- License
- Apache-2.0
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
skill-evaluation-graph
Deeply analyze, audit, score, and optimize agent skills conforming to the Agent Skills standard or Antigravity/Codex/Claude formats. Use when reviewing an existing skill, diagnosing why an agent misfires or burns context, pruning skill bloat, refactoring monolithic instructions into progressive disclosure, or benchmarking skill quality. Do not use for creating skills from scratch without an existing procedure (use workflow-skill-creator).
skill-eval-harness
Write, evaluate and maintain agent skills with measured evidence rather than assertion. Covers SKILL.md structure and frontmatter, writing a description that triggers reliably without firing on neighbouring topics, progressive disclosure through references and scripts, and building eval suites across three families - usefulness with skill on versus off, discovery, and adversarial discovery. Use when creating a new skill, reviewing or debugging one that does not trigger or does not help, deciding whether a skill earns its context cost, writing an eval suite or grader, or interpreting skill eval results. Also use when asked whether a skill is any good.
skill-eval-loop
Measure whether an agent skill actually helps: prompt sets with should/should-not trigger, with/without skill runs, deterministic checks, rubric grading, CI regressions. Use when hardening a SKILL.md, deciding if a rewrite improved quality, or adding a quality gate for skills.