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refactor: simplify prompt-enhancer skill description and internal analysis
- Update description: focus on intelligent analysis and session memory - Simplify trigger mechanism to only -e/--enhance flags - Condense internal analysis section to single concise statement - Remove verbose semantic analysis details for cleaner documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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catlog22
@@ -1,6 +1,6 @@
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---
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name: Prompt Enhancer
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description: Transform vague prompts into actionable specs using session memory ONLY (no file analysis). AUTO-TRIGGER on (1) -e/--enhance flags, (2) vague keywords (fix/improve/refactor/修复/优化/重构), (3) unclear refs (it/that/这个/那个), (4) multi-module scope. Supports English + Chinese semantic recognition.
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description: Transform vague prompts into actionable specs using intelligent analysis and session memory. Trigger with -e/--enhance flags.
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allowed-tools: (none)
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---
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@@ -10,35 +10,9 @@ allowed-tools: (none)
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**Languages**: English + Chinese (中英文语义识别)
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## Triggers
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| Priority | Condition | Examples | Action |
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|----------|-----------|----------|--------|
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| **P1** | `-e` / `--enhance` flag | "fix auth -e", "优化性能 --enhance" | Immediate enhancement |
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| **P2** | Vague keywords | EN: fix/improve/refactor<br>CN: 修复/优化/重构/更新/改进 | Semantic analysis |
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| **P3** | Unclear references | EN: it/that/the code<br>CN: 这个/那个/它/代码 | Context extraction |
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| **P4** | Multi-module scope | >3 modules or critical systems | Dependency analysis |
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## Process (Internal → Direct Output)
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**Internal Analysis (Silent)**:
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1. **Semantic Analysis**
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- Intent keywords (EN/CN): fix/修复, improve/优化, add/添加, refactor/重构
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- Scope identification: file → module → system
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- Domain mapping: auth/API/DB/UI/Performance
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2. **Memory Extraction** (NO File Reading)
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- Recent user requests and context
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- Tech stack mentioned in session (frameworks, libraries, patterns)
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- Design patterns discussed or implied
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- User preferences and constraints
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- Known dependencies from conversation
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3. **Enhancement Dimensions**
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- **Structure**: Convert to INTENT/CONTEXT/ACTION/ATTENTION format
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- **Supplement**: Add tech stack, design patterns, testing requirements
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- **Clarify**: Make intent explicit, resolve ambiguous references
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**Internal Analysis**: Intelligently extract session context, identify tech stack, and structure into actionable format.
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**Output**: Direct structured prompt (no intermediate steps shown)
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@@ -66,18 +40,6 @@ ATTENTION: [Critical constraints / 关键约束]
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- [Security/compatibility/performance concerns]
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- [Design pattern requirements]
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```
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## Semantic Patterns (EN + CN)
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| Intent (EN/CN) | Semantic Meaning | Enhancement Focus |
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|----------------|------------------|-------------------|
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| fix/修复 + vague target | Debug & resolve | Root cause + tech stack + testing |
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| improve/优化 + no metrics | Enhance/optimize | Performance metrics + patterns + benchmarks |
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| add/添加 + feature | Implement feature | Integration points + edge cases + tests |
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| refactor/重构 + module | Restructure | Design patterns + backward compatibility |
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| update/更新 + version | Modernize | Migration path + breaking changes |
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| clean up/清理 + area | Simplify/organize | Code quality patterns + tech debt |
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## Workflow
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```
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@@ -134,87 +96,3 @@ Trigger → Internal Analysis → Direct Output
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3. **Added tech stack section** - Supplement with technologies
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4. **Enhanced internal analysis** - 3 dimensions (structure + supplement + clarify)
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5. **Focus on memory** - Session context only, no file reading
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## Examples
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### Example 1: Vague Fix Request
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**Input**: "fix auth -e" / "修复认证 --enhance"
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**Internal Analysis**:
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- Semantic: "fix/修复" → debug intent
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- Memory: Recent mention of JWT, token expiration issues
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- Tech stack: JWT, Express.js middleware (from session)
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- Enhancement: Add testing, security patterns
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**Output**:
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```
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📋 ENHANCED PROMPT
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INTENT: Fix authentication token expiration handling in JWT-based auth system
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TECH STACK:
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- JWT: Token generation and validation
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- Express.js: Middleware implementation
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- Jest: Unit testing framework
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CONTEXT:
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- User reported token expiration issues in previous conversation
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- Session indicates JWT-based authentication system
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- Preference for backward compatibility with existing tokens
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ACTION:
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1. Analyze JWT token lifecycle and expiration validation logic
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2. Implement proper token refresh mechanism with sliding expiration
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3. Add middleware for token validation with detailed error handling
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4. Write unit tests for token expiration and refresh scenarios
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5. Update API documentation for token refresh endpoints
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ATTENTION:
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- Must maintain backward compatibility with existing valid tokens
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- Security: Follow JWT best practices (HS256/RS256, secure secret storage)
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- Testing: Cover edge cases (expired, malformed, missing tokens)
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- Performance: Consider token validation caching for high-traffic endpoints
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```
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### Example 2: Vague Optimization Request
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**Input**: "optimize performance --enhance" / "优化性能 -e"
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**Internal Analysis**:
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- Semantic: "optimize/优化" → performance intent
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- Memory: Previous discussion about API response times, React rendering
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- Tech stack: React, Node.js, PostgreSQL (from session)
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- Enhancement: Add metrics, profiling, specific optimizations
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**Output**:
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```
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📋 ENHANCED PROMPT
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INTENT: Optimize API response times and React component rendering performance
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TECH STACK:
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- React: Component optimization (memo, useMemo, useCallback)
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- Node.js: API endpoint optimization
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- PostgreSQL: Query optimization and indexing
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CONTEXT:
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- User mentioned slow API responses (>2s) in previous conversation
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- React app shows rendering performance issues on large lists
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- PostgreSQL database queries identified as bottleneck
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ACTION:
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1. Profile API endpoints using Node.js profiler to identify slow queries
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2. Optimize PostgreSQL queries with proper indexing and query analysis (EXPLAIN)
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3. Implement React.memo for expensive list components
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4. Add useMemo/useCallback for derived data and event handlers
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5. Implement pagination/virtualization for large data lists
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6. Add performance monitoring with metrics (response time, render time)
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7. Write performance benchmarks and regression tests
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ATTENTION:
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- Metrics: Target <500ms API response, <100ms render time
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- Patterns: Use React.memo judiciously (avoid over-optimization)
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- Testing: Add performance regression tests with benchmarks
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- Compatibility: Ensure optimizations don't break existing functionality
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```
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