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docs: Remove all pycli references and obsolete Python backend documentation
Changes: - Removed pycli backend references from README.md - Removed pycli backend references from README_CN.md - Deleted obsolete python-tools-strategy.md file - Updated project structure documentation to reflect current scripts - Replaced Python backend section with enhanced test generation features This cleanup ensures documentation accurately reflects the current system without the removed Python CLI components. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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---
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name: python-tools-strategy
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description: Strategic framework for Python-based intelligent tool selection
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type: strategic-guideline
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---
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# Python Tools Selection Strategy
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## ⚡ Core Framework
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**Python CLI (pycli)**: Unified interface for intelligent context discovery and tool execution
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**Vector Database**: Hierarchical semantic search with automatic parent discovery
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**Smart Analysis**: Context-aware file selection with similarity scoring
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### Decision Principles
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- **Context first** - Use vector search for intelligent file discovery
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- **Hierarchical by default** - Leverage parent directory vector databases automatically
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- **Semantic over syntactic** - Find relevant code by meaning, not just keywords
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- **Tool integration** - Seamlessly combine context discovery with Gemini/Codex execution
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### Quick Decision Rules
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1. **Need context discovery?** → Start with `pycli --analyze --query`
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2. **Know exact files?** → Use `pycli --analyze -p` directly
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3. **First time in project?** → Run `pycli --init` first
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4. **Files changed?** → Update with `pycli --update-embeddings`
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### Core Execution Rules
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- **Default Tool**: Gemini for analysis, Codex for development
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- **Similarity Threshold**: 0.3 minimum for relevant results
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- **Hierarchical Search**: Automatic parent directory vector database discovery
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- **Command Pattern**: Always use `pycli` wrapper for consistent interface
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## 🎯 Universal Command Template
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### Standard Format (REQUIRED)
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```bash
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# Smart Context Discovery
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pycli --analyze --query "
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PURPOSE: [clear analysis goal]
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SEARCH: [semantic search terms]
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TOOL: [gemini/codex/both]
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EXPECTED: [expected context and output]
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" --tool [gemini/codex]
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# Direct Tool Execution
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pycli --analyze --tool [gemini/codex] -p "
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PURPOSE: [clear execution goal]
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TASK: [specific execution task]
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CONTEXT: [known file references]
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EXPECTED: [expected deliverables]
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"
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```
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### Template Structure
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- [ ] **PURPOSE** - Clear goal and intent for analysis
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- [ ] **SEARCH/TASK** - Semantic search terms or specific task
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- [ ] **TOOL** - Gemini for analysis, Codex for development
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- [ ] **CONTEXT** - File references and project context
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- [ ] **EXPECTED** - Clear expected results and format
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## 📊 Tool Selection Matrix
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| Task Type | Command | Use Case | Context Strategy |
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|-----------|---------|----------|------------------|
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| **Context Discovery** | `pycli --analyze --query` | Code exploration, pattern finding | Vector similarity search |
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| **Targeted Analysis** | `pycli --analyze --tool gemini -p` | Architecture review, understanding | Known file analysis |
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| **Development** | `pycli --analyze --tool codex -p` | Feature implementation, bug fixes | Smart context + execution |
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| **Setup** | `pycli --init` | Project initialization | Vector database creation |
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| **Maintenance** | `pycli --update-embeddings` | Index updates after changes | Incremental vectorization |
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| **Health Check** | `pycli --status` | System verification | Database validation |
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## 🚀 Usage Patterns
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### Workflow Integration (REQUIRED)
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When planning any coding task, **ALWAYS** integrate Python CLI tools:
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1. **Discovery Phase**: Use `pycli --analyze --query` for context
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2. **Analysis Phase**: Use Gemini for understanding with smart context
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3. **Implementation Phase**: Use Codex for development with relevant files
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4. **Validation Phase**: Update embeddings and verify results
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### Common Scenarios
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```bash
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# Project Context Discovery
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pycli --analyze --query "
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PURPOSE: Understand authentication architecture
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SEARCH: authentication patterns, login systems, user management
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TOOL: gemini
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EXPECTED: Architecture overview and key implementation files
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" --tool gemini
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# Feature Development with Context
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pycli --analyze --query "
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PURPOSE: Implement user registration
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SEARCH: user creation, validation patterns, database models
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TOOL: codex
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EXPECTED: Complete registration module with tests
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" --tool codex
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# Code Quality Analysis
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pycli --analyze --query "
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PURPOSE: Review error handling patterns
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SEARCH: exception handling, error middleware, logging
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TOOL: gemini
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EXPECTED: Error handling assessment and recommendations
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" --tool gemini
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```
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## 📋 Planning Checklist
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For every development task:
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- [ ] **Discovery completed** - Context discovery with vector search
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- [ ] **Purpose defined** - Clear goal and intent documented
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- [ ] **Tool selected** - Gemini for analysis, Codex for development
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- [ ] **Context gathered** - Relevant files identified through similarity
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- [ ] **Template applied** - Standard command format used
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- [ ] **Embeddings updated** - Vector database reflects current state
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- [ ] **Results validated** - Output quality and relevance verified
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## 🎯 Key Features
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### Python CLI (pycli)
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- **Command**: `pycli --analyze`
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- **Strengths**: Hierarchical vector search, semantic similarity, context discovery
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- **Best For**: Intelligent file selection, context-aware analysis, project exploration
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### Vector Database
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- **Hierarchical**: Automatic parent directory discovery
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- **Semantic**: Meaning-based similarity scoring
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- **Efficient**: Incremental updates and smart caching
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- **Scalable**: Project-wide context with subdirectory support
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### Context Patterns
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- Query-based: `--query "semantic search terms"`
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- Direct prompt: `-p "specific task"`
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- Tool selection: `--tool [gemini|codex|both]`
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- Similarity control: `--top-k N --similarity-threshold X`
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## 🔧 Best Practices
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- **Start with discovery** - Use `--query` for context before direct prompts
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- **Be semantic** - Use meaning-based search terms, not just keywords
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- **Update regularly** - Run `--update-embeddings` after file changes
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- **Validate context** - Check similarity scores and relevance before proceeding
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- **Document patterns** - Reference successful query patterns for reuse
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- **Leverage hierarchy** - Work in subdirectories, let parent DBs provide context
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## 📁 Hierarchical Vector System
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**Base Structure**: `~/.claude/vector_db/[project-path]/`
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### Automatic Discovery
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```
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Project Structure Vector Database Usage
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/project/ Creates: ~/.claude/vector_db/project/
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├── src/ → Uses parent DB automatically
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│ ├── auth/ → Uses parent DB automatically
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│ └── api/ → Uses parent DB automatically
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└── tests/ → Uses parent DB automatically
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```
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### Smart Context Integration
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- **Parent Discovery**: Subdirectories automatically use parent vector DB
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- **Semantic Search**: Find files by meaning, not just filename patterns
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- **Similarity Scoring**: Relevance-based file selection with configurable thresholds
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- **Incremental Updates**: Efficient re-indexing of only changed files
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### Migration Benefits
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```bash
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# Enhanced Context Discovery (vs traditional grep/find)
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# OLD: find . -name "*auth*" | head -10
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# NEW: pycli --analyze --query "authentication patterns" --tool gemini
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# OLD: grep -r "login" src/ | head -20
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# NEW: pycli --analyze --query "login implementation" --tool codex
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# OLD: ~/.claude/scripts/gemini-wrapper -p "analyze auth"
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# NEW: pycli --analyze --query "authentication architecture" --tool gemini
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```
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## 🎯 Quick Setup Guide
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### Project Setup (One-time per project)
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```bash
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# 1. Navigate to project root
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cd /path/to/project
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# 2. Initialize vector database
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pycli --init
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# 3. Verify setup
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pycli --status
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```
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### Daily Workflow
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```bash
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# 1. Update embeddings (after file changes)
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pycli --update-embeddings
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# 2. Smart context discovery
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pycli --analyze --query "your search terms" --tool gemini
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# 3. Targeted development
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pycli --analyze --query "implementation patterns" --tool codex
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```
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## 🎪 Decision Framework
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```
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Need intelligent code analysis?
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├─ Know specific files to analyze?
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│ ├─ YES → pycli --analyze --tool [gemini/codex] -p "prompt"
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│ └─ NO → pycli --analyze --query "semantic search" --tool [gemini/codex]
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├─ Vector database updated?
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│ ├─ UNSURE → pycli --status
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│ ├─ NO → pycli --update-embeddings
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│ └─ YES → Proceed with analysis
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└─ First time in project?
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└─ Run pycli --init first
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```
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