Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Rates responses and plans against quality rubrics. Used for plan validation, response quality audits, and multi-agent consensus.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use appropriate rubric for the content type:
For Plans:
| Dimension | Weight | Description |
|---|---|---|
| Completeness | 20% | All required sections present |
| Feasibility | 20% | Plan is realistic and achievable |
| Risk Mitigation | 20% | Risks identified with mitigations |
| Agent Coverage | 20% | Appropriate agents assigned |
| Integration | 20% | Fits with existing systems |
For Responses:
| Dimension | Weight | Description |
|---|---|---|
| Correctness | 25% | Technically accurate |
| Completeness | 25% | Addresses all requirements |
| Clarity | 25% | Easy to understand |
| Actionability | 25% | Provides clear next steps |
Score each dimension 1-10:
## Dimension Scores
### Completeness: 8/10
- Has objectives, steps, and timeline
- Missing risk assessment section
### Feasibility: 7/10
- Most steps are achievable
- Step 3 timeline is aggressive
### Risk Mitigation: 5/10
- Only 1 risk identified
- No mitigation strategies
### Agent Coverage: 9/10
- All steps have assigned agents
- Good agent-task matching
### Integration: 8/10
- Uses existing APIs
- Minor compatibility concerns
Average the weighted scores:
Overall = (8×0.2) + (7×0.2) + (5×0.2) + (9×0.2) + (8×0.2) = 7.4/10
Provide actionable improvements:
## Recommendations
### High Priority
1. Add risk assessment section with 3-5 risks
2. Include mitigation strategies for each risk
### Medium Priority
3. Extend Step 3 timeline by 2 days
4. Add fallback plan for external API dependency
### Low Priority
5. Add success metrics for each step
Apply minimum score thresholds:
| Task Type | Minimum Score |
|---|---|
| Standard | 7/10 |
| Enterprise | 8/10 |
| Critical | 9/10 |
## Decision
**Score**: 7.4/10
**Threshold**: 7/10 (Standard)
**Result**: PASS ✅
Plan approved with recommendations for improvement.
</execution_process>
<best_practices>
</best_practices> </instructions>
<examples> <usage_example> **Plan Rating Request**:Rate this plan for user authentication implementation
Response:
## Plan Rating: User Authentication
### Scores
- Completeness: 8/10 - Good structure, missing tests section
- Feasibility: 9/10 - Realistic timeline and approach
- Risk Mitigation: 6/10 - Only 1 risk identified
- Agent Coverage: 9/10 - Good agent assignments
- Integration: 8/10 - Compatible with existing auth
### Overall: 8.0/10 ✅ PASS
### Top Recommendations
1. Add risk section with security and dependency risks
2. Include test plan for each authentication flow
3. Add rollback procedure for failed deployment
</usage_example> </examples>
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Using different rubric dimensions for comparable content | Scores cannot be compared across sessions; the rating loses its evaluative value | Always use the same rubric (plans rubric for plans, responses rubric for responses) |
| Omitting score justification for individual dimensions | Scores without justification cannot be reviewed, verified, or acted upon | Document specific evidence for each dimension score (what was present, what was missing) |
| Setting thresholds arbitrarily per session | Inconsistent thresholds invalidate the pass/fail gate; teams lose confidence in approvals | Always apply the defined thresholds: 7/10 standard, 8/10 enterprise, 9/10 critical |
| Providing vague recommendations ("improve quality", "add more detail") | Vague feedback cannot be acted upon; no change results from the review | Reference the specific dimension, score gap, and required concrete change for each recommendation |
| Listing recommendations without priority ordering | Equal-weight feedback causes raters to address low-impact items first | Always order by impact: High (affects pass/fail threshold) before Medium before Low |
Before starting:
cat .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: Your context may reset. If it's not in memory, it didn't happen.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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