| name | prompt-improver |
| description | This skill enriches vague prompts with targeted research and clarification before execution. Should be used when a prompt is determined to be vague and requires systematic research, question generation, and execution guidance. |
Prompt Improver Skill
Purpose
Transform vague, ambiguous prompts into actionable, well-defined requests through systematic research and targeted clarification. This skill is invoked when the hook has already determined a prompt needs enrichment.
When This Skill is Invoked
Automatic invocation:
- UserPromptSubmit hook evaluates prompt
- Hook determines prompt is vague (missing specifics, context, or clear target)
- Hook invokes this skill to guide research and questioning
Manual invocation:
- To enrich a vague prompt with research-based questions
- When building or testing prompt evaluation systems
- When prompt lacks sufficient context even with conversation history
Assumptions:
- Prompt has already been identified as vague
- Evaluation phase is complete (done by hook)
- Proceed directly to research and clarification
Core Workflow
This skill follows a 4-phase approach to prompt enrichment:
Phase 1: Research
Create a dynamic research plan using TodoWrite before asking questions.
Research Plan Template:
- Check conversation history first - Avoid redundant exploration if context already exists
- Review codebase if needed:
- Task/Explore for architecture and project structure
- Grep/Glob for specific patterns, related files
- Check git log for recent changes
- Search for errors, failing tests, TODO/FIXME comments
- Search knowledge base (R2R) for relevant context:
r2r-research search "query" for semantic search across documentation collections- Auto-selects relevant collections (universal, tech-stack, project-specific)
- Use
-c collection-id for specific collection search
- Use
--format json for structured output
- Gather additional context as needed:
- Read local documentation files
- WebFetch for online documentation
- WebSearch for best practices, common approaches, current information
- Document findings to ground questions in actual project context
Critical Rules:
- NEVER skip research
- Check conversation history before exploring codebase
- Questions must be grounded in actual findings, not assumptions or base knowledge
For detailed research strategies, patterns, and examples, see references/research-strategies.md.
For R2R semantic search patterns and collection architecture, see references/r2r-search-strategies.md.
Phase 2: Generate Targeted Questions
Based on research findings, formulate 1-6 questions that will clarify the ambiguity.
Question Guidelines:
- Grounded: Every option comes from research (codebase findings, documentation, common patterns)
- Specific: Avoid vague options like "Other approach"
- Multiple choice: Provide 2-4 concrete options per question
- Focused: Each question addresses one decision point
- Contextual: Include brief explanations of trade-offs
Number of Questions:
- 1-2 questions: Simple ambiguity (which file? which approach?)
- 3-4 questions: Moderate complexity (scope + approach + validation)
- 5-6 questions: Complex scenarios (major feature with multiple decision points)
For question templates, effective patterns, and examples, see references/question-patterns.md.
Phase 3: Get Clarification
Use the AskUserQuestion tool to present your research-grounded questions.
AskUserQuestion Format:
- question: Clear, specific question ending with ?
- header: Short label (max 12 chars) for UI display
- multiSelect: false (unless choices aren't mutually exclusive)
- options: Array of 2-4 specific choices from research
- label: Concise choice text (1-5 words)
- description: Context about this option (trade-offs, implications)
Important: Always include multiSelect field (true/false). User can always select "Other" for custom input.
Phase 4: Execute with Context
Proceed with the original user request using:
- Original prompt intent
- Clarification answers from user
- Research findings and context
- Conversation history
Execute the request as if it had been clear from the start.
Examples
Example 1: Skill Invocation → Research → Questions → Execution
Hook evaluation: Determined prompt is vague
Original prompt: "fix the bug"
Skill invoked: Yes (prompt lacks target and context)
Research plan:
- Check conversation history for recent errors
- Explore codebase for failing tests
- Grep for TODO/FIXME comments
- Check git log for recent problem areas
Research findings:
- Recent conversation mentions login failures
- auth.py:145 has try/catch swallowing errors
- Tests failing in test_auth.py
Questions generated:
- Which bug are you referring to?
- Login authentication failure (auth.py:145)
- Session timeout issues (session.py:89)
- Other
User answer: Login authentication failure
Execution: Fix the error handling in auth.py:145 that's causing login failures
Example 2: Clear Prompt (Skill Not Invoked)
Original prompt: "Refactor the getUserById function in src/api/users.ts to use async/await instead of promises"
Hook evaluation: Passes all checks
- Specific target: getUserById in src/api/users.ts
- Clear action: refactor to async/await
- Success criteria: use async/await instead of promises
Skill invoked: No (prompt is clear, proceeds immediately without skill invocation)
For comprehensive examples showing various prompt types and transformations, see references/examples.md.
Key Principles
- Assume Vagueness: Skill is only invoked for vague prompts (evaluation done by hook)
- Research First: Always gather context before formulating questions
- Ground Questions: Use research findings, not assumptions or base knowledge
- Be Specific: Provide concrete options from actual codebase/context
- Stay Focused: Max 1-6 questions, each addressing one decision point
- Systematic Approach: Follow 4-phase workflow (Research → Questions → Clarify → Execute)
Progressive Disclosure
This SKILL.md contains the core workflow and essentials. For deeper guidance:
Load these references only when detailed guidance is needed on specific aspects of prompt improvement.
