Smart Tree - ST

# Smart Tree Mode Selection Guide ## 🚀 Quick Start - Recommended Workflow 1. **Always start with `quick_tree`** - Gets you a 3-level overview instantly 2. **Use `analyze_directory` with `mode='ai'`** (default) for detailed analysis 3. **Switch to `mode='claude'`** when you hit token limits (10x compression!) ## 📊 Mode Comparison | Mode | Compression | Use Case | Token Efficiency | |------|-------------|----------|------------------| | **claude** | 10x | 🤖 Maximum AI efficiency | ⭐⭐⭐⭐⭐ | | **ai** | 5x | 🤖 Balanced AI/human readable | ⭐⭐⭐⭐ | | **quantum** | 8x | 🤖 Native compression format | ⭐⭐⭐⭐⭐ | | **classic** | 1x | 👁️ Visual tree for humans | ⭐ | | **hex** | 3x | 🔧 Technical analysis | ⭐⭐⭐ | | **json** | 2x | 💻 Machine parsing | ⭐⭐ | | **stats** | 10x | 📈 Quick metrics only | ⭐⭐⭐⭐ | ## 🎯 Mode Selection by Use Case ### For AI Assistants (You!) ```javascript // Initial exploration - ALWAYS start here! await quick_tree({ path: "/project" }) // 3 levels, compressed // Detailed analysis - default mode await analyze_directory({ path: "/project", mode: "ai" // Default, good balance }) // Hit token limits? Switch to claude mode! await analyze_directory({ path: "/huge-project", mode: "claude", // 10x compression! compress: true // Even more compression }) ``` ### For Human Developers ```javascript // Visual tree structure await analyze_directory({ path: "/project", mode: "classic" // Traditional tree view }) // Just the stats await analyze_directory({ path: "/project", mode: "stats" // Summary only }) ``` ### For Data Processing ```javascript // Machine-readable format await analyze_directory({ path: "/project", mode: "json" // Parse with JSON.parse() }) // Spreadsheet import await analyze_directory({ path: "/project", mode: "csv" // Import to Excel/Sheets }) ``` ## 💡 Pro Tips ### 1. **Token Budget Management** - Small projects (<1000 files): Use `mode='ai'` freely - Medium projects (1000-10000 files): Start with `quick_tree`, then `mode='ai'` - Large projects (>10000 files): Use `quick_tree`, then `mode='claude'` - Massive projects (>100000 files): Always use `mode='claude'` with `compress=true` ### 2. **Compression Stacking** You can combine mode selection with compression for maximum efficiency: ```javascript await analyze_directory({ path: "/chromium", mode: "claude", // 10x from mode compress: true // Additional 10x from zlib // Total: 100x compression! }) ``` ### 3. **Mode-Specific Features** **AI Mode** includes: - Smart summaries - File type distribution - Size analysis - Date ranges - Largest files **Claude Mode** adds: - Quantum compression - Base64 encoding - Minimal redundancy - Token-optimized output **Classic Mode** provides: - ASCII tree branches - Colored output (if terminal supports) - Human-friendly formatting - No compression ## 📈 Real-World Examples ### Analyzing node_modules (156K files) ```javascript // ❌ BAD: Classic mode uses too many tokens await analyze_directory({ path: "./node_modules", mode: "classic" }) // Output: 42MB of text! // ✅ GOOD: Start with overview await quick_tree({ path: "./node_modules" }) // Output: 3-level summary, instantly // ✅ BETTER: Use AI mode for details await analyze_directory({ path: "./node_modules", mode: "ai" }) // Output: 8MB, well-structured // ✅ BEST: Use claude mode for huge directories await analyze_directory({ path: "./node_modules", mode: "claude", compress: true }) // Output: 412KB! (99% reduction) ``` ### Quick Project Analysis ```javascript // Perfect workflow for new projects: const overview = await quick_tree({ path: "/new-project" }) // Get a feel for the structure const details = await analyze_directory({ path: "/new-project", mode: "ai", max_depth: 5 // Don't go too deep }) // Now you understand the project! ``` ## 🎯 Decision Tree ``` Start ↓ Is this your first look at this directory? YES → Use quick_tree() NO ↓ Do you need full details? NO → Use mode="stats" YES ↓ Is the directory large (>10K files)? YES → Use mode="claude" with compress=true NO ↓ Do you need visual tree structure? YES → Use mode="classic" NO → Use mode="ai" (default) ``` ## 🚨 Common Mistakes 1. **Using classic mode for large directories** - This wastes tokens! 2. **Not starting with quick_tree** - Miss the forest for the trees 3. **Forgetting compress=true on claude mode** - Leave 10x compression on the table 4. **Using JSON when you don't need parsing** - AI mode is more efficient ## 💰 Token Cost Examples (GPT-4) Analyzing Linux kernel (79K files): - **classic mode**: 18MB ≈ 4.7M tokens ≈ $47 - **ai mode**: 3.6MB ≈ 950K tokens ≈ $9.50 - **claude mode**: 1.8MB ≈ 475K tokens ≈ $4.75 - **claude + compress**: 287KB ≈ 75K tokens ≈ $0.75 That's a 98% cost reduction by choosing the right mode! --- Remember: **Start with quick_tree, use ai mode by default, switch to claude mode when needed!** 🚀