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。
Self-critique specification documents using extended thinking — surfaces hidden assumptions, contradictions, missing edge cases, and scope creep before implementation
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。
Critically review specification documents for accuracy, completeness, and feasibility. Use extended thinking to find issues BEFORE implementation begins.
Core principle: Use extended thinking (deep analysis). Find problems BEFORE implementation.
Always:
Exceptions:
NO IMPLEMENTATION WITHOUT SPEC CRITIQUE FOR COMPLEX TASKS
Complex tasks (external integrations, multi-service changes) must have spec critique.
Before critiquing, ensure you have:
# Read the spec
cat .claude/context/specs/[task-name]-spec.md
# Read original requirements
cat .claude/context/requirements/[task-name].md
# Read project context
cat .claude/context/tech-stack.md
Understand:
CRITICAL: Use extended thinking for this phase. Think deeply about:
Compare spec against requirements and codebase:
Check for common spec errors:
Flag any mismatches.
Check against requirements:
Flag any gaps.
Check within spec:
Flag any inconsistencies.
Check practicality:
Flag any concerns.
Cross-reference with requirements:
Flag any divergences.
Create a list of all issues found:
## Issues Found
### 1. [SEVERITY: HIGH] Package name incorrect
- **Spec says**: "[incorrect]"
- **Should be**: "[correct]"
- **Location**: Line 45, Requirements section
### 2. [SEVERITY: MEDIUM] Missing edge case
- **Requirement**: "Handle connection failures"
- **Spec**: No error handling specified
- **Location**: Implementation Notes section
### 3. [SEVERITY: LOW] Inconsistent terminology
- **Issue**: Uses both "memory" and "episode" for same concept
- **Location**: Throughout document
For each issue found, fix it directly in the spec:
For each fix:
# Spec Critique Report
**Spec**: [spec-name]
**Date**: [timestamp]
## Summary
| Category | Status | Issues |
| ---------------------- | --------- | ------- |
| Technical Accuracy | PASS/FAIL | [count] |
| Completeness | PASS/FAIL | [count] |
| Consistency | PASS/FAIL | [count] |
| Feasibility | PASS/FAIL | [count] |
| Requirements Alignment | PASS/FAIL | [count] |
## Issues Found and Fixed
### High Severity
1. [Issue] - FIXED: [what was changed]
### Medium Severity
1. [Issue] - FIXED: [what was changed]
### Low Severity
1. [Issue] - FIXED: [what was changed]
## No Issues Found (if applicable)
Spec is well-written with no significant issues found.
## Confidence Level
[HIGH/MEDIUM/LOW]
## Recommendations
- [Any remaining concerns or suggestions]
After making changes:
# Verify spec is still valid markdown
head -50 .claude/context/specs/[task-name]-spec.md
# Check key sections exist
grep -E "^##? Overview" spec.md
grep -E "^##? Requirements" spec.md
grep -E "^##? Success Criteria" spec.md
HIGH - Will cause implementation failure:
MEDIUM - May cause issues:
LOW - Minor improvements:
Before completing spec critique:
When analyzing, think through:
"Looking at this spec, I need to deeply analyze it against the requirements...
First, let me check all package names. The requirements mention [X], but the spec says [Y]. This is a mismatch that needs fixing.
Next, looking at the API patterns. The requirements show initialization requires [steps], but the spec shows [different steps]. Another issue.
For completeness, the requirements mention [X, Y, Z]. The spec covers X and Y but I don't see Z addressed anywhere. This is a gap.
Looking at consistency, I notice '[term1]' and '[term2]' used interchangeably. Should standardize on one term.
For feasibility, the setup seems correct. The configuration matches.
Overall, I found [N] issues that need fixing before this spec is ready for implementation."
Why it's wrong: Skimming misses critical issues.
Do this instead: Use extended thinking. Analyze each section deeply.
Why it's wrong: Just listing issues doesn't improve the spec.
Do this instead: Fix issues directly. Make the spec better.
This skill works well with:
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Approving specs with contradictions | Downstream implementation resolves contradictions incorrectly | Flag all contradictions, even minor ones, before approval |
| Missing hidden assumptions | Assumptions become mismatched requirements during implementation | Explicitly surface every assumption the spec relies on |
| Skipping edge case review | Error scenarios discovered too late during implementation | Validate all boundary conditions and error paths in the spec |
| Critiquing implementation details | Distracts from specification quality; spec defines WHAT not HOW | Focus critique on completeness, consistency, and testability |
| Accepting vague success criteria | Delivery cannot be verified as complete | Require measurable, testable acceptance criteria for every requirement |
Before starting:
Read .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: 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。