prompt-improver（及时改进）

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:

1. Check conversation history first - Avoid redundant exploration if context already exists
2. 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

1. Gather additional context as needed:

- Read local documentation files - WebFetch for online documentation - WebSearch for best practices, common approaches, current information

1. 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.

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:

1. Check conversation history for recent errors
2. Explore codebase for failing tests
3. Grep for TODO/FIXME comments
4. 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:

1. 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

1. Assume Vagueness: Skill is only invoked for vague prompts (evaluation done by hook)
2. Research First: Always gather context before formulating questions
3. Ground Questions: Use research findings, not assumptions or base knowledge
4. Be Specific: Provide concrete options from actual codebase/context
5. Stay Focused: Max 1-6 questions, each addressing one decision point
6. Systematic Approach: Follow 4-phase workflow (Research → Questions → Clarify → Execute)

Progressive Disclosure

This SKILL.md contains the core workflow and essentials. For deeper guidance:

• Research strategies: references/research-strategies.md
• Question patterns: references/question-patterns.md
• Comprehensive examples: references/examples.md

Load these references only when detailed guidance is needed on specific aspects of prompt improvement.