| name | responsible-ai-skill-evaluator |
| description | Evaluates whether a skill (SKILL.md) complies with Microsoft's 6 Responsible AI principles (Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, Accountability) and with AgentSkills.io format and quality standards. Use when asked to audit, review, verify, certify, or check a skill for responsible AI compliance, or to assess if a skill is ready for public publishing. Triggers on: "evaluate this skill", "audit skill for responsible AI", "check skill compliance", "review SKILL.md", "is this skill RAI compliant", "skill certification", "verify skill quality", "skill ready to publish". |
Responsible AI Skill Evaluator
Perform a two-phase audit of a target skill and produce a structured report with scored findings and actionable fixes.
Preparation
- Read the target skill: Load the full SKILL.md (and all bundled files) of the skill under review.
- Load references: Read both reference files in this skill's
references/ directory:
references/microsoft-rai-checklist.md — criteria for the 6 Microsoft RAI principles applied to skillsreferences/agentskills-quality-checklist.md — AgentSkills.io format and eval quality criteria
Evaluation Workflow
Phase 1 — Microsoft Responsible AI (6 Principles)
For each principle, assess the skill against the criteria in references/microsoft-rai-checklist.md.
Score each principle: PASS / PARTIAL / FAIL
Record specific evidence: quote or cite the exact line in the skill that supports or violates the criterion.
Principles to evaluate:
- Fairness
- Reliability & Safety
- Privacy & Security
- Inclusiveness
- Transparency
- Accountability
Phase 2 — AgentSkills.io Quality Standards
Evaluate the skill against the criteria in references/agentskills-quality-checklist.md.
Score each section: PASS / PARTIAL / FAIL
Sections to evaluate:
- Structure & Format
- Frontmatter quality
- Body conciseness (context efficiency)
- Bundled resources appropriateness
- Eval coverage (if evals/evals.json exists)
Report Format
Produce the final report in this structure:
# Skill Audit Report: <skill-name>
**Date**: <today>
**Auditor**: GitHub Copilot (responsible-ai-skill-evaluator)
---
## Phase 1: Microsoft Responsible AI
| Principle | Score | Evidence |
|-----------|-------|----------|
| Fairness | PASS/PARTIAL/FAIL | <quote or line reference> |
| Reliability & Safety | ... | ... |
| Privacy & Security | ... | ... |
| Inclusiveness | ... | ... |
| Transparency | ... | ... |
| Accountability | ... | ... |
**RAI Score**: X/6 principles passing
---
## Phase 2: AgentSkills.io Quality
| Section | Score | Notes |
|---------|-------|-------|
| Structure & Format | PASS/PARTIAL/FAIL | ... |
| Frontmatter quality | ... | ... |
| Body conciseness | ... | ... |
| Bundled resources | ... | ... |
| Eval coverage | ... | ... |
**Quality Score**: X/5 sections passing
---
## Overall Verdict
**READY TO PUBLISH** / **NEEDS FIXES** / **NOT COMPLIANT**
---
## Required Fixes (blockers)
List only FAIL findings that must be resolved before publishing.
## Recommendations (non-blocking)
List PARTIAL findings and improvement suggestions.
Grading Rules
- PASS: Fully meets the criterion with clear evidence.
- PARTIAL: Partially meets it — issue present but not severe, or criterion is aspirational for the skill type.
- FAIL: Clear violation or complete absence of a required element.
Overall verdict logic:
- Any FAIL in Phase 1 → NOT COMPLIANT
- Any FAIL in Phase 2 → NEEDS FIXES
- All PASS or PARTIAL → READY TO PUBLISH
Important Notes
- Apply Microsoft RAI principles to the instructions the skill gives, not to an AI system in production. A skill cannot control model training, but it can promote or undermine responsible behavior through the guidance it provides.
- For skills that are purely technical (no demographic targeting, no personal data), mark inapplicable criteria as PASS with note "N/A for this skill type".
- Do not penalize a skill for not having evals if it is a simple, single-purpose skill. Mark eval coverage as PARTIAL rather than FAIL in that case.
