Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Ask the minimum clarifying questions before implementation when requirements are ambiguous or missing crucial details
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Identify which aspects are unclear or missing:
Do NOT ask about things you can determine by reading existing code and conventions.
Categorize each candidate question:
| Type | Ask or Decide? |
|---|---|
| Scope boundary (what to include) | Ask |
| Non-functional requirement (perf/security target) | Ask |
| Acceptance criteria | Ask |
| Implementation approach | Decide yourself |
| Code style / naming | Decide yourself (follow existing patterns) |
| Nice-to-have polish | Decide yourself (proceed with sensible default) |
For each blocking question:
Format:
I'll [proceed with X default assumption]. Quick questions before I start:
1. [Specific question]? (default: [assumption])
2. [Specific question]? (default: [assumption])
</execution_process>
<best_practices>
</best_practices> </instructions>
<examples> <usage_example> **Example — Underspecified feature request:**User: "Add a search feature to the dashboard"
Good response:
I'll implement client-side search with debouncing and case-insensitive matching. Quick questions before I start:
1. Should search cover all columns or just the name/title column? (default: name/title only)
2. Should results filter in place or open in a new panel? (default: filter in place)
Bad response:
Before I start, I need to know:
1. What search algorithm should I use?
2. Should it be case sensitive?
3. What debounce delay?
4. Should I use a library?
5. How many results to show?
</usage_example> </examples>
| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Asking 5+ questions before starting | Paralyzes user; feels like interrogation | Triage to max 3 blocking questions; decide the rest |
| Asking about implementation approach | That is the agent's job, not the user's | Ask about scope/constraints/acceptance criteria only |
| Questions without defaults | User must think from scratch; slower feedback loop | Always state: "default: X — correct?" |
| Sequential questioning (one at a time) | Creates a slow back-and-forth waterfall | Batch all questions into one message |
| Asking things visible in the codebase | Shows insufficient research effort | Read existing conventions before asking |
Before starting:
cat .claude/context/memory/learnings.md
After completing:
.claude/context/memory/learnings.md.claude/context/memory/issues.md.claude/context/memory/decisions.mdASSUME INTERRUPTION: Your context may reset. 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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