mirror of
https://github.com/catlog22/Claude-Code-Workflow.git
synced 2026-02-05 01:50:27 +08:00
refactor: enhance prompt generation process with direct output and improved internal analysis
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catlog22
@@ -338,29 +338,3 @@ const agentContext = {
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mcp_analysis: executeMcpDiscovery()
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}
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```
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## Usage Examples
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```bash
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# Agent Mode (default) - steps without command field
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/workflow:tools:task-generate-agent --session WFS-auth
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# CLI Execute Mode - steps with command field
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/workflow:tools:task-generate-agent --session WFS-auth --cli-execute
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# Called by /workflow:plan (default mode)
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SlashCommand(command="/workflow:tools:task-generate-agent --session WFS-[id]")
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# Called by /workflow:plan --cli-execute (CLI mode)
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SlashCommand(command="/workflow:tools:task-generate-agent --session WFS-[id] --cli-execute")
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```
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## Related Commands
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- `/workflow:plan` - Orchestrates planning and calls this command
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- `/workflow:plan --cli-execute` - Planning with CLI execution mode
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- `/workflow:tools:task-generate` - Manual version without agent
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- `/workflow:tools:context-gather` - Provides context package
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- `/workflow:tools:concept-enhanced` - Provides analysis results
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- `/workflow:execute` - Executes generated tasks
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@@ -1,12 +1,12 @@
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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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allowed-tools: AskUserQuestion
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allowed-tools: (none)
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---
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# Prompt Enhancer
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**Transform**: Vague intent → Structured specification (Memory-based)
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**Transform**: Vague intent → Structured specification (Memory-based, Direct Output)
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**Languages**: English + Chinese (中英文语义识别)
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@@ -19,160 +19,202 @@ allowed-tools: AskUserQuestion
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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 (3 Steps - Streamlined)
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## Process (Internal → Direct Output)
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### 1. Semantic Analysis (Quick)
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Identify: Intent keywords (EN/CN) → Scope (file/module/system) → Domain (auth/API/DB/UI)
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**Internal Analysis (Silent)**:
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**English**: fix, improve, add, refactor, update, migrate
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**Chinese**: 修复, 优化, 添加, 重构, 更新, 迁移, 改进, 清理
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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-Only Extraction (NO File Reading)
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Extract from **conversation memory ONLY**:
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- Recent user requests and context
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- Previous implementations/patterns discussed
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- Known dependencies from session
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- User preferences and constraints
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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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**SKIP**: File reading, codebase scanning, Glob/Grep operations
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**FOCUS**: Pure memory-based context extraction
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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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### 3. User Confirmation with Optimization Direction (REQUIRED)
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Present structured prompt → Ask: Continue? + Optimization suggestions needed?
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**Output**: Direct structured prompt (no intermediate steps shown)
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## Output Format
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```
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📋 ENHANCED PROMPT
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INTENT: [One-sentence technical goal / 明确技术目标]
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INTENT: [One-sentence technical goal with tech stack / 明确技术目标含技术栈]
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TECH STACK: [Relevant technologies from memory / 相关技术栈]
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- [Framework/Library: Purpose]
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CONTEXT: [Session memory findings / 会话记忆发现]
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- [Key context point 1]
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- [Key context point 2]
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- [...]
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- [Design patterns/constraints from session]
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ACTION:
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1. [Concrete step / 具体步骤]
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2. [Concrete step / 具体步骤]
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3. [...]
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1. [Concrete step with technical details / 具体步骤含技术细节]
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2. [Concrete step with technical details]
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3. [Testing/validation step]
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ATTENTION: [Critical constraints / 关键约束]
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- [Security/compatibility/testing concerns]
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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 | Focus |
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|----------------|------------------|-------|
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| fix/修复 + vague target | Debug & resolve | Root cause → preserve behavior |
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| improve/优化 + no metrics | Enhance/optimize | Performance/readability |
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| add/添加 + feature | Implement feature | Integration + edge cases |
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| refactor/重构 + module | Restructure | Maintain behavior |
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| update/更新 + version | Modernize | Version compatibility |
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| clean up/清理 + area | Simplify/organize | Remove redundancy |
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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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Trigger → Analyze → Extract → Present → Confirm → Execute
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↓ ↓ ↓ ↓ ↓ ↓
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P1-4 EN/CN Memory Struct Ask User Direct
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detect only prompt or refine
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Trigger → Internal Analysis → Direct Output
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↓ ↓ ↓
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P1-4 Semantic+Memory Enhanced Prompt
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(3 dimensions) (Structured)
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```
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1. **Detect**: Check triggers (P1-P4)
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2. **Analyze**: Semantic (EN/CN) analysis of user intent
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3. **Extract**: Memory-only context extraction (NO file reading)
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4. **Present**: Generate structured prompt output
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5. **Confirm**: AskUserQuestion (Continue/Modify/Cancel)
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6. **Execute**: Proceed based on user choice
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2. **Internal Analysis**:
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- Semantic (EN/CN) intent analysis
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- Memory extraction (tech stack, patterns, constraints)
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- Enhancement (structure + supplement + clarify)
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3. **Output**: Present enhanced structured prompt directly
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## Confirmation (AskUserQuestion)
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## Enhancement Checklist (Internal)
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**Question**: "Enhanced prompt ready. Proceed or need adjustments? (已生成增强提示词，是否继续或需要调整？)"
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**Structure**:
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- [ ] INTENT: Clear, one-sentence technical goal
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- [ ] TECH STACK: Relevant technologies from session
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- [ ] CONTEXT: Key session findings and constraints
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- [ ] ACTION: Concrete steps with technical details
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- [ ] ATTENTION: Critical constraints and patterns
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**Options**:
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1. **Continue as-is / 按此继续** - Proceed with current specification
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2. **Suggest optimizations / 建议优化方向** - I need guidance on how to improve this
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3. **Modify requirements / 修改需求** - Let me provide specific changes
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**Supplement**:
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- [ ] Add tech stack/frameworks mentioned in session
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- [ ] Include design patterns if relevant
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- [ ] Add testing/validation requirements
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- [ ] Specify performance metrics if applicable
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```typescript
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AskUserQuestion({
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questions: [{
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question: "Enhanced prompt ready. Proceed or need adjustments? (已生成增强提示词，是否继续或需要调整？)",
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header: "Next Step",
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multiSelect: false,
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options: [
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{
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label: "Continue as-is",
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description: "Proceed with current specification (按此继续)"
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},
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{
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label: "Suggest optimizations",
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description: "I need guidance on how to improve this (建议优化方向)"
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},
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{
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label: "Modify requirements",
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description: "Let me provide specific changes (修改需求)"
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}
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]
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}]
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})
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```
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**Clarify**:
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- [ ] Make vague intent explicit
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- [ ] Resolve ambiguous references (it/that/这个/那个)
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- [ ] Expand multi-module scope with dependencies
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- [ ] Add missing context from memory
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## Best Practices
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- ✅ Detect `-e`/`--enhance` flags first (P1)
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- ✅ Support EN + CN semantic keywords
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- ✅ Extract **memory context ONLY** (no file reading)
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- ✅ Use INTENT/CONTEXT/ACTION/ATTENTION format
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- ✅ ALWAYS confirm with AskUserQuestion
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- ✅ Offer optimization guidance option
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- ✅ Add tech stack, design patterns, testing requirements
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- ✅ Direct output (no intermediate steps)
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- ✅ Use INTENT/TECH STACK/CONTEXT/ACTION/ATTENTION format
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- ❌ NO tool calls (AskUserQuestion removed)
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- ❌ NO Bash, Read, Glob, Grep operations
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- ❌ NO direct file analysis
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- ❌ NO file analysis or codebase scanning
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## Key Changes from Previous Version
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## Key Changes
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1. **Removed file analysis** - Memory extraction only
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2. **Simplified to 3 steps** - Faster workflow
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3. **Updated confirmation options** - Added "Suggest optimizations"
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4. **Removed file tools** - Only AskUserQuestion allowed
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5. **Focus on speed** - Quick semantic analysis + memory extraction
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1. **Removed all tools** - Pure analysis and output
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2. **Removed user confirmation** - Direct output for speed
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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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**Input**: "fix auth -e" / "优化性能 --enhance"
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### Example 1: Vague Fix Request
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**Process**:
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1. Detect P1 trigger (`-e` flag)
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2. Semantic analysis: "fix/优化" intent
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3. Extract from memory: Recent auth/performance discussions
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4. Generate structured prompt
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5. Ask user: Continue/Suggest optimizations/Modify
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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 module issues based on recent session context
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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 mentioned auth token expiration problems
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- Previous discussion about JWT validation
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- Session indicates preference for backward compatibility
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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. Review authentication token handling logic
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2. Implement proper JWT validation with expiration checks
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3. Add unit tests for token refresh flow
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4. Update documentation for auth changes
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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 tokens
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- Security: Follow JWT best practices
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- Testing: Ensure no breaking changes to API contracts
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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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**Then ask**: Continue as-is / Suggest optimizations / Modify requirements
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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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Reference in New Issue
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