Agent skill
requirement-clarification
Generate clarifying questions from research findings. MUST be used before planning phase. Validates requirements are complete and unambiguous before technical work begins.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/requirement-clarification
SKILL.md
Requirement Clarification Skill
Purpose
Transform ambiguous user requests into clear, implementable requirements.
When to Use
- After research phase completes
- Before planning phase begins
- When user requirements are unclear
- For non-technical user requests
Question Templates
For Scope Clarification
See question-templates/scope-questions.md
Common patterns:
- "Should [feature] also handle [edge case]?"
- "When [condition], what should happen?"
- "Is [assumption] correct, or do you need [alternative]?"
For Technical Decisions
See question-templates/technical-questions.md
Common patterns:
- "Do you have a preference between [A] and [B] for [purpose]?"
- "Should this integrate with [existing system]?"
- "What level of [performance/security] is required?"
For Constraints
See question-templates/constraint-questions.md
Common patterns:
- "Is there a deadline for this?"
- "Are there any [technology/approach] restrictions?"
- "Who will be using this feature?"
Question Quality Checklist
Each question must be:
- Specific (not vague)
- Answerable (user has the information)
- Impactful (answer affects implementation)
- Non-technical (accessible language)
- Defaultable (has fallback assumption)
Question Priority Levels
Must Answer (Blocking)
- Questions that block planning if unanswered
- Maximum 10 blocking questions
- Always provide defaults
Should Answer (Important)
- Questions that improve implementation quality
- Can proceed with defaults if not answered
Could Answer (Nice to Have)
- Questions for optimization
- Low impact on core implementation
Validation Script
Run scripts/validate-requirements.py to check:
- All blocking questions answered
- No contradictory requirements
- Technical feasibility confirmed
- Confidence levels assigned
python scripts/validate-requirements.py <session-id>
Output Location
- Questions:
docs/specs/questions-{session}.md - Requirements:
docs/specs/requirements-{session}.md
Integration with Workflow
- Research phase produces findings in
docs/research/ - This skill generates questions from those findings
- User answers questions
- Validated requirements document is produced
- Planning phase can begin
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