prompt-improver

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Translated

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.

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Sourceseverity1/claude-code-prompt-improver
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NPX Install

npx skill4agent add severity1/claude-code-prompt-improver prompt-improver

Tags

Translated version includes tags in frontmatter
prompt-improvementskill-developmentai-agent-workflowprompt-clarificationresearch-strategy

SKILL.md Content

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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
  3. Gather additional context as needed:
    • Read local documentation files
    • WebFetch for online documentation
    • WebSearch for best practices, common approaches, current information
  4. 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.