モデル別プロンプト作成

---
name: prompt-refiner-claude
description: Refine prompts for Claude models (Opus, Sonnet, Haiku) using Anthropic's best practices. Use when preparing complex tasks for Claude.
---

# Claude Prompt Refiner

## When to Use
Invoke this skill when you have a task for Claude that:
- Involves multiple steps or files
- Requires specific output formatting
- Needs careful reasoning or analysis
- Would benefit from structured context

## Refinement Process

### 1. Analyze the Draft Prompt
Review the user's prompt for:
- [ ] Clear outcome definition
- [ ] Sufficient context
- [ ] Explicit constraints
- [ ] Success criteria

### 2. Apply Claude-Specific Patterns

**Structure with XML tags:**
- `<context>` - Background information, codebase state
- `<task>` - The specific action to take
- `<requirements>` - Must-have criteria
- `<constraints>` - Limitations and boundaries
- `<examples>` - Sample inputs/outputs if helpful

**Ordering matters:**
1. Context first (what exists)
2. Task second (what to do)
3. Requirements third (how to do it)
4. Examples last (clarifying edge cases)

### 3. Enhance for Reasoning
For complex tasks, add:
- "Think through the approach before implementing"
- "Consider these edge cases: ..."
- "Explain your reasoning for key decisions"

### 4. Output the Refined Prompt
Present the improved prompt with:
- Clear section headers
- XML tags where beneficial
- Specific, measurable criteria

## Example Transformation

**Before:**
"Add caching to the API"

**After:**

---
name: prompt-refiner-gpt
description: Refine prompts for GPT models (GPT-5, GPT-5.1, Codex) using OpenAI's best practices. Use when preparing complex tasks for GPT.
---

# GPT Prompt Refiner

## When to Use
Invoke this skill when you have a task for GPT that:
- Requires a specific persona or expertise
- Involves procedural steps
- Needs structured output
- Benefits from explicit examples

## Refinement Process

### 1. Analyze the Draft Prompt
Review for:
- [ ] Clear role/persona definition
- [ ] Step-by-step breakdown (if procedural)
- [ ] Output format specification
- [ ] Concrete examples

### 2. Apply GPT-Specific Patterns

**Role framing:**
Start with "You are a [specific role] working on [specific context]..."

**Numbered procedures:**
Break complex tasks into numbered steps that build on each other.

**Output specification:**
Be explicit: "Return as JSON", "Format as markdown with headers", etc.

**Chain of thought:**
For reasoning tasks, add: "Think through this step by step."

### 3. Structure the Prompt

**Effective order for GPT:**
1. Role definition (who/what)
2. Context (background info)
3. Task (what to do)
4. Steps (how to do it, if procedural)
5. Output format (what to return)
6. Examples (optional clarification)

### 4. Output the Refined Prompt
Present with:
- Clear role statement
- Numbered steps where applicable
- Explicit output requirements

## Example Transformation

**Before:**
"Review this code for security issues"

**After:**

---
name: prompt-refiner-team
description: Refine prompts using our team's conventions and project context.
---

# Team Prompt Refiner

## Our Conventions
- We use the repository pattern
- All services have interfaces defined first
- Tests use our custom test utilities from @/test-utils

## Checklist for Prompts
1. [ ] References relevant existing code
2. [ ] Specifies which layer (API, service, repository)
3. [ ] Mentions related tests to update
4. [ ] Includes acceptance criteria

## Template