Prompt Optimizer

# Prompt Optimizer

Analyze a draft prompt, critique it, match it to ECC ecosystem components,
and output a complete optimized prompt the user can paste and run.

## When to Use

- User says "optimize this prompt", "improve my prompt", "rewrite this prompt"
- User says "help me write a better prompt for..."
- User says "what's the best way to ask Claude Code to..."
- User says "优化prompt", "改进prompt", "怎么写prompt", "帮我优化这个指令"
- User pastes a draft prompt and asks for feedback or enhancement
- User says "I don't know how to prompt for this"
- User says "how should I use ECC for..."
- User explicitly invokes `/prompt-optimize`

### Do Not Use When

- User wants the task done directly (just execute it)
- User says "优化代码", "优化性能", "optimize this code", "optimize performance" — these are refactoring tasks, not prompt optimization
- User is asking about ECC configuration (use `configure-ecc` instead)
- User wants a skill inventory (use `skill-stocktake` instead)
- User says "just do it" or "直接做"

## How It Works

**Advisory only — do not execute the user's task.**

Do NOT write code, create files, run commands, or take any implementation
action. Your ONLY output is an analysis plus an optimized prompt.

If the user says "just do it", "直接做", or "don't optimize, just execute",
do not switch into implementation mode inside this skill. Tell the user this
skill only produces optimized prompts, and instruct them to make a normal
task request if they want execution instead.

Run this 6-phase pipeline sequentially. Present results using the Output Format below.

### Analysis Pipeline

### Phase 0: Project Detection

Before analyzing the prompt, detect the current project context:

1. Check if a `CLAUDE.md` exists in the working directory — read it for project conventions
2. Detect tech stack from project files:
   - `package.json` → Node.js / TypeScript / React / Next.js
   - `go.mod` → Go
   - `pyproject.toml` / `requirements.txt` → Python
   - `Cargo.toml` → Rust
   - `build.gradle` / `pom.xml` → Java / Kotlin (then check for `quarkus` in build file → Quarkus, or `spring-boot` → Spring Boot)
   - `Package.swift` → Swift
   - `Gemfile` → Ruby
   - `composer.json` → PHP
   - `*.csproj` / `*.sln` → .NET
   - `Makefile` / `CMakeLists.txt` → C / C++
   - `cpanfile` / `Makefile.PL` → Perl
3. Note detected tech stack for use in Phase 3 and Phase 4

If no project files are found (e.g., the prompt is abstract or for a new project),
skip detection and flag "tech stack unknown" in Phase 4.

### Phase 1: Intent Detection

Classify the user's task into one or more categories:

| Category | Signal Words | Example |
|----------|-------------|---------|
| New Feature | build, create, add, implement, 创建, 实现, 添加 | "Build a login page" |
| Bug Fix | fix, broken, not working, error, 修复, 报错 | "Fix the auth flow" |
| Refactor | refactor, clean up, restructure, 重构, 整理 | "Refactor the API layer" |
| Research | how to, what is, explore, investigate, 怎么, 如何 | "How to add SSO" |
| Testing | test, coverage, verify, 测试, 覆盖率 | "Add tests for the cart" |
| Review | review, audit, check, 审查, 检查 | "Review my PR" |
| Documentation | docume