Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Socratic design refinement before implementation — challenges assumptions, surfaces alternatives, identifies risks before code is written
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
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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.
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Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
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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.
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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
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