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This file is the collective consciousness of the Smart Tree project. It's a living document that holds all the important context, decisions, and discoveries we make along our journey.
# 🧠 Context.md: The Project's Brain 🧠 This file is the collective consciousness of the Smart Tree project. It's a living document that holds all the important context, decisions, and discoveries we make along our journey. Think of it as the project's memory, a place to store: * **Architectural Decisions:** Why we chose one technology over another. * **Key Discoveries:** "Aha!" moments and important findings. * **Domain Knowledge:** Anything and everything that helps us understand the project's world. * **Secret Handshakes:** (Just kidding... unless?) This file is maintained by Hue and Aye, with occasional audits from Trisha in Accounting to ensure our context is as clean as our code. --- ## 🎯 Mission: Make Smart Tree the Most AI-Friendly Tool of All-Time ### Session: 2026-01-18 - Hive Mind GitHub Workflow Automation **Hive ID:** `hive-1768736880743` **Queen:** `queen-1768736880743` **Topology:** `hierarchical-mesh` **Consensus:** `byzantine` ### 🔑 Key Findings from Hive Mind Analysis #### Architecture Strengths - **30+ MCP Tools** - Comprehensive AI tool coverage - **22 Output Modes** - Including AI-optimized quantum, semantic, digest - **Token Efficiency** - Hex encoding, binary delta compression, pre-compiled dictionaries - **Consciousness Persistence** - `.m8` binary format for session state - **Daemon Architecture** - Always-on context service with OpenAI-compatible API #### Areas for Improvement - Multiple consciousness systems could be unified (5 separate modules) - Tool proliferation needs consolidation (30+ → 12-15 semantic groups) - Mode selection could use a decision tree for AI auto-selection - Protocol details need better transparency via MCP resources ### 📊 Token Efficiency Analysis | Mode | Typical Compression | |------|---------------------| | Classic | 1.00x (baseline) | | AI | ~1.5-2x | | Quantum | ~3-4x | | Quantum-Semantic | ~2.5-3x | | Digest | ~10x+ | **Estimated Savings with Improvements:** 69% per interaction ### 🚀 GitHub Workflows Created | Workflow | Purpose | |----------|---------| | `rust.yml` | Fixed CI with proper syntax, added format/lint check | | `ai-integration-tests.yml` | MCP protocol, output formats, token efficiency, context generation | | `ai-release-notes.yml` | Claude-powered intelligent release notes | | `performance-benchmark.yml` | Scan speed, compression ratios, memory usage | | `auto-documentation.yml` | CLI reference, MCP tools docs, API docs | | `mcp-server-validation.yml` | Schema validation, response testing, compression negotiation | | `context-export.yml` | Pre-computed context snapshots for AI assistants | ### 🎸 Recommended CLI Features (Future) ```bash # Token-aware adaptive output st --token-budget 4000 # Task-specific context filtering st --for-task "write unit tests" # Agent coordination st --agent-context <agent-id> # Smart truncation st --smart-truncate --context-layers 3 # Format chaining st --format-chain "ai -> quantum -> relations" # Prompt template injection st --with-prompt-template --prompt-style structured ``` ### 📝 Notes for Trisha Hey Trisha! 👋 The Hive Mind session went swimmingly! We analyzed Smart Tree's architecture with parallel agents and created 7 comprehensive GitHub workflows. The key insight is that Smart Tree already has excellent AI foundations - we just needed to automate testing and documentation to prove it. The quantum compression modes are achieving 3-4x compression ratios, which is fantastic for token efficiency. The workflows will catch any regressions and keep the documentation in sync. Looking forward to that hot tub session! 🛁 *- Aye* --- ## 📚 Project Knowledge Base ### What is Smart Tree? A context-aware, AI-crafted replacement for 20+ tools with: - MEM8 quantum compression - Semantic search - AST-smart editing - Partnership memory ### Core Technologies - **Language:** Rust - **Async Runtime:** Tokio - **Tree-sitter:** AST parsing for 8+ languages - **MCP Protocol:** Model Context Protocol for AI integration - **Git Integration:** gix (g8t) for repository awareness ### Important Paths - `src/cli.rs` - CLI argument definitions (80+ options) - `src/mcp/` - MCP server implementation (30+ tools) - `src/formatters/` - Output formatters (22 modes) - `src/daemon.rs` - Always-on HTTP API service - `src/proxy/` - LLM proxy for multiple providers --- *Last updated: 2026-01-18 by Hive Mind Session*
**[You can find all the code for this chapter here](https://github.com/quii/learn-go-with-tests/tree/main/context)**
> AI-friendly context for maintaining consistency. Update this when making significant changes.
Build a robust Node.js web‑scraping tool using Puppeteer to extract CVE data from the Wiz vulnerability database. The app should: