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。
Multi-step autonomous research methodology for deep investigation tasks with structured synthesis
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。
Trigger phrases: "research", "investigate", "deep dive", "find evidence for", "comprehensive analysis of"
Never synthesize without reading sources. Every claim in the final report must trace to a source retrieved during Phase 2.
Phase 1: Scope — Restate as one research question. Define 3–5 sub-questions, success criteria, constraints, and non-goals. Cannot proceed without a written scope block.
Phase 2: Multi-Source Search — Run 3+ WebSearch queries. WebFetch each source URL. pnpm search:code for prior-art. Maintain evidence log: URL, claim, confidence (HIGH/MEDIUM/LOW), sub-question. Each sub-question needs ≥2 independent sources.
Phase 3: Synthesis — Group evidence by sub-question. CONSENSUS (2+ sources) → HIGH; CONFLICT → LOW, flag for user. Build synthesis matrix. Label unsupported claims [UNVERIFIED].
Phase 4: Validation
Phase 5: Report — Save to .claude/context/reports/backend/<topic>-research-<YYYY-MM-DD>.md with sections: Executive Summary, Findings (finding + confidence + evidence per sub-question), Conflicts, Recommended Actions, Knowledge Gaps, Sources.
| Tool | Phase | Purpose |
|---|---|---|
WebSearch | 2A | Broad source discovery |
WebFetch | 2B | Extract content from URLs |
Read / Grep | 2C | Search codebase for prior art |
Write | 5 | Save report |
.claude/context/reports/backend/<topic>-research-<YYYY-MM-DD>.md.claude/context/tmp/research-evidence-<YYYY-MM-DD>.mdpnpm search:code "<query>" — primary intent-based searchSkill({ skill: 'ripgrep' }) — exact keyword/regex matchesWebSearch / WebFetch — external sourcesGrep — fallback for single-file targeted checksBefore starting:
Read .claude/context/memory/learnings.md
Check for: prior reports on same topic (avoid duplicates), unreliable sources in issues.md, relevant ADRs in decisions.md.
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: Save evidence log and partial findings before Phase 3.
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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。