Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Rates responses and plans against quality rubrics. Used for plan validation, response quality audits, and multi-agent consensus.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
日本語の概要は準備中です。原文の説明を表示しています。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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
日本語の概要は準備中です。原文の説明を表示しています。