Skill: terwox/skill-evaluator (version 1.0.0)

--- name: skill-evaluator description: Evaluate Clawdbot skills for quality, reliability, and publish-readiness using a multi-framework rubric (ISO 25010, OpenSSF, Shneiderman, agent-specific heuristics). Use when asked to review, audit, evaluate, score, or assess a skill before publishing, or when checking skill quality. Runs automated structural checks and guides manual assessment across 25 criteria. --- # Skill Evaluator Evaluate skills across 25 criteria using a hybrid automated + manual approach. ## Quick Start ### 1. Run automated checks ```bash python3 scripts/eval-skill.py /path/to/skill python3 scripts/eval-skill.py /path/to/skill --json # machine-readable python3 scripts/eval-skill.py /path/to/skill --verbose # show all details ``` Checks: file structure, frontmatter, description quality, script syntax, dependency audit, credential scan, env var documentation. ### 2. Manual assessment Use the rubric at [references/rubric.md](references/rubric.md) to score 25 criteria across 8 categories (0–4 each, 100 total). Each criterion has concrete descriptions per score level. ### 3. Write the evaluation Copy [assets/EVAL-TEMPLATE.md](assets/EVAL-TEMPLATE.md) to the skill directory as `EVAL.md`. Fill in automated results + manual scores. ## Evaluation Process 1. **Run `eval-skill.py`** — get the automated structural score 2. **Read the skill's SKILL.md** — understand what it does 3. **Read/skim the scripts** — assess code quality, error handling, testability 4. **Score each manual criterion** using [references/rubric.md](references/rubric.md) — concrete criteria per level 5. **Prioritize findings** as P0 (blocks publishing) / P1 (should fix) / P2 (nice to have) 6. **Write EVAL.md** in the skill directory with scores + findings ## Categories (8 categories, 25 criteria) | # | Category | Source Framework | Criteria | |---|----------|-----------------|----------| | 1 | Functional Suitability | ISO 25010 | Completeness, Correctness, Appropriateness | | 2 | Reliability | ISO 25010 | Fault Tolerance, Error Reporting, Recoverability | | 3 | Performance / Context | ISO 25010 + Agent | Token Cost, Execution Efficiency | | 4 | Usability — AI Agent | Shneiderman, Gerhardt-Powals | Learnability, Consistency, Feedback, Error Prevention | | 5 | Usability — Human | Tognazzini, Norman | Discoverability, Forgiveness | | 6 | Security | ISO 25010 + OpenSSF | Credentials, Input Validation, Data Safety | | 7 | Maintainability | ISO 25010 | Modularity, Modifiability, Testability | | 8 | Agent-Specific | Novel | Trigger Precision, Progressive Disclosure, Composability, Idempotency, Escape Hatches | ## Interpreting Scores | Range | Verdict | Action | |-------|---------|--------| | 90–100 | Excellent | Publish confidently | | 80–89 | Good | Publishable, note known issues | | 70–79 | Acceptable | Fix P0s before publishing | | 60–69 | Needs Work | Fix P0+P1 before publishing | | <60 | Not Ready | Significant rework needed | ## Deeper Security Scanning This evaluator covers security basics (credentials, input validation, data safety) but for thorough security audits of skills under development, consider [SkillLens](https://www.npmjs.com/package/skilllens) (`npx skilllens scan <path>`). It checks for exfiltration, code execution, persistence, privilege bypass, and prompt injection — complementary to the quality focus here. ## Dependencies - Python 3.6+ (for eval-skill.py) - PyYAML (`pip install pyyaml`) — for frontmatter parsing in automated checks


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)