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。
Socratic design refinement before implementation — challenges assumptions, surfaces alternatives, identifies risks before code is written
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。
Help turn ideas into fully formed designs and specs through natural collaborative dialogue.
Start by understanding the current project context, then ask questions one at a time to refine the idea. Once you understand what you're building, present the design in small sections (200-300 words), checking after each section whether it looks right so far.
Understanding the idea:
Exploring approaches:
Presenting the design:
Documentation:
docs/plans/YYYY-MM-DD-<topic>-design.mdImplementation (if continuing):
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Proposing only one solution | Anchors user; forecloses better alternatives | Always offer 2-3 approaches with explicit trade-offs |
| Accepting first requirement as complete | First articulation rarely captures all constraints | Ask at least one clarifying question about scope |
| Asking 3+ questions at once | User answers the easy ones; skips the hard ones | One question per response; wait for answer |
| Jumping to implementation | Design flaws found late are expensive to fix | Confirm design decisions before writing code |
| Skipping YAGNI challenge | Unneeded features accumulate as tech debt | Explicitly challenge each feature not strictly required |
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.
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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。