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。
Unified skill that guides spec creation through structured, interactive process.
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。
Unified skill that guides spec creation through structured, interactive process.
Wraps these existing skills:
Question: "What are you building?"
Auto-detect from description:
type: featuretype: bugtype: choretype: refactortype: docsInvoke context-compressor (progressive disclosure mode) with adaptive algorithm:
const { AdaptiveQuestioner } = require('.claude/lib/utils/adaptive-discloser.cjs');
const { ContextAccumulator } = require('.claude/lib/utils/context-accumulator.cjs');
// Determine domain from detected type
const domainMap = {
feature: 'general',
bug: 'debugging',
chore: 'general',
refactor: 'architecture',
docs: 'documentation',
};
const domain = domainMap[detectedType] || 'general';
const aq = new AdaptiveQuestioner(domain);
const ca = new ContextAccumulator();
let history = [];
let questionCount = 0;
while (questionCount < 7) {
const context = ca.getContext();
const result = await aq.getNextQuestion(context, history);
// Check if we should stop early
const readiness = await aq.detectOptimalStop(history, context);
if (readiness.shouldStop) {
break;
}
// Ask the question
const answer = await AskUserQuestion({ question: result.question });
// Store answer with metadata
ca.addAnswer(result.question, answer, { domain, priority: 'HIGH' });
history.push({ question: result.question, answer });
questionCount++;
}
// Summary from accumulated context
const summary = ca.buildSummary();
Key Improvements over v1:
Auto-populate spec from answers:
# SPEC: [Feature Name]
## 1. Overview
**Title**: [From question 1]
**Type**: [Detected type]
**Objective**: [User summary]
**User Story**: As a [user type], I want [capability], so that [benefit]
**Acceptance Criteria**: [From question 5]
## 2. Problem Statement
- **Current State**: [From question 1 answers]
- **Pain Points**: [Extracted from answers]
- **Impact**: [Quantified if possible]
## 3. Proposed Solution
- **Approach**: [From user input]
- **Key Features**: [From answers]
- **Scope**: [What's in/out]
## 4. Implementation Approach
- **Phase 1**: [Design/spike if needed]
- **Phase 2**: [Core implementation]
- **Phase 3**: [Testing]
- **Phase 4**: [Documentation]
## 5. Success Metrics
- **Quantitative**: [From question 3]
- **Qualitative**: [User satisfaction]
- **Timeline**: [From question 4]
## 6. Effort Estimate
- **Design**: 1 day
- **Implementation**: 3 days
- **Testing**: 2 days
- **Documentation**: 1 day
- **Total**: 7 days
## 7. Dependencies
- **Required**: [Extracted from context]
- **Blocking**: [What must complete first]
- **Risk**: [Key risks identified]
## 8. Acceptance Criteria Checklist
- [ ] Feature implemented per spec
- [ ] All tests passing
- [ ] Documentation updated
- [ ] No breaking changes
- [ ] Performance targets met
Validate spec against schema:
After spec approved:
Skill({ skill: "plan-generator", args: { specPath: "..." } })Save spec to:
.claude/context/artifacts/specs/[feature-name]-spec-YYYYMMDD.md
Track metadata:
User: "I want to add dark mode to the UI"
spec-init workflow:
1. Detect: type = "feature"
2. Ask: 5 questions about dark mode
3. User answers in <5 minutes
4. Generate spec
5. Validate against schema
6. Store and offer plan generation
User: "There's a memory leak in the scheduler"
spec-init workflow:
1. Detect: type = "bug"
2. Ask: 5 questions (reproduce steps, impact, etc)
3. Generate bug fix spec
4. Suggest acceptance criteria
5. Ready for planner
.claude/context/artifacts/specs/ with the correct naming convention| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Asking 10-12 fixed questions | Over-questioning reduces user engagement | Use progressive disclosure; stop at 5-7 questions when context is sufficient |
| Skipping intent type detection | Questions don't adapt to the task type | Always classify the request as feature/bug/chore/refactor/docs first |
| Generating spec without validation | Incomplete specs reach the planner | Validate all required sections before saving the spec |
| Missing track metadata | Spec cannot be tracked or referenced by downstream agents | Always populate trackId, type, status, and created_at fields |
| Saving to wrong location | Specs are not discoverable by other agents | Always save to .claude/context/artifacts/specs/ with standard naming |
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。