MCP Server Trello

# Claude Code Configuration - SPARC Development Environment ## 🚨 CRITICAL: CONCURRENT EXECUTION & FILE MANAGEMENT **ABSOLUTE RULES**: 1. ALL operations MUST be concurrent/parallel in a single message 2. **NEVER save working files, text/mds and tests to the root folder** 3. ALWAYS organize files in appropriate subdirectories ### ⚡ GOLDEN RULE: "1 MESSAGE = ALL RELATED OPERATIONS" **MANDATORY PATTERNS:** - **TodoWrite**: ALWAYS batch ALL todos in ONE call (5-10+ todos minimum) - **Task tool**: ALWAYS spawn ALL agents in ONE message with full instructions - **File operations**: ALWAYS batch ALL reads/writes/edits in ONE message - **Bash commands**: ALWAYS batch ALL terminal operations in ONE message - **Memory operations**: ALWAYS batch ALL memory store/retrieve in ONE message ### 📁 File Organization Rules **NEVER save to root folder. Use these directories:** - `/src` - Source code files - `/tests` - Test files - `/docs` - Documentation and markdown files - `/config` - Configuration files - `/scripts` - Utility scripts - `/examples` - Example code ## Project Overview This project uses SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) methodology with Claude-Flow orchestration for systematic Test-Driven Development. ## SPARC Commands ### Core Commands - `npx claude-flow sparc modes` - List available modes - `npx claude-flow sparc run <mode> "<task>"` - Execute specific mode - `npx claude-flow sparc tdd "<feature>"` - Run complete TDD workflow - `npx claude-flow sparc info <mode>` - Get mode details ### Batchtools Commands - `npx claude-flow sparc batch <modes> "<task>"` - Parallel execution - `npx claude-flow sparc pipeline "<task>"` - Full pipeline processing - `npx claude-flow sparc concurrent <mode> "<tasks-file>"` - Multi-task processing ### Build Commands - `npm run build` - Build project - `npm run test` - Run tests - `npm run lint` - Linting - `npm run typecheck` - Type checking ## SPARC Workflow Phases 1. **Specification** - Requirements analysis (`sparc run spec-pseudocode`) 2. **Pseudocode** - Algorithm design (`sparc run spec-pseudocode`) 3. **Architecture** - System design (`sparc run architect`) 4. **Refinement** - TDD implementation (`sparc tdd`) 5. **Completion** - Integration (`sparc run integration`) ## Code Style & Best Practices - **Modular Design**: Files under 500 lines - **Environment Safety**: Never hardcode secrets - **Test-First**: Write tests before implementation - **Clean Architecture**: Separate concerns - **Documentation**: Keep updated ## 🚀 Available Agents (54 Total) ### Core Development `coder`, `reviewer`, `tester`, `planner`, `researcher` ### Swarm Coordination `hierarchical-coordinator`, `mesh-coordinator`, `adaptive-coordinator`, `collective-intelligence-coordinator`, `swarm-memory-manager` ### Consensus & Distributed `byzantine-coordinator`, `raft-manager`, `gossip-coordinator`, `consensus-builder`, `crdt-synchronizer`, `quorum-manager`, `security-manager` ### Performance & Optimization `perf-analyzer`, `performance-benchmarker`, `task-orchestrator`, `memory-coordinator`, `smart-agent` ### GitHub & Repository `github-modes`, `pr-manager`, `code-review-swarm`, `issue-tracker`, `release-manager`, `workflow-automation`, `project-board-sync`, `repo-architect`, `multi-repo-swarm` ### SPARC Methodology `sparc-coord`, `sparc-coder`, `specification`, `pseudocode`, `architecture`, `refinement` ### Specialized Development `backend-dev`, `mobile-dev`, `ml-developer`, `cicd-engineer`, `api-docs`, `system-architect`, `code-analyzer`, `base-template-generator` ### Testing & Validation `tdd-london-swarm`, `production-validator` ### Migration & Planning `migration-planner`, `swarm-init` ## 🎯 Claude Code vs MCP Tools ### Claude Code Handles ALL: - File operations (Read, Write, Edit, MultiEdit, Glob, Grep) - Code generation and programming - Bash commands and system operations - Implementation work - Project navigation and analysis - TodoWrite and task management - Git operations - Package management - Testing and debugging ### MCP Tools ONLY: - Coordination and planning - Memory management - Neural features - Performance tracking - Swarm orchestration - GitHub integration **KEY**: MCP coordinates, Claude Code executes. ## 🚀 Quick Setup ```bash # Add Claude Flow MCP server claude mcp add claude-flow npx claude-flow@alpha mcp start ``` ## MCP Tool Categories ### Coordination `swarm_init`, `agent_spawn`, `task_orchestrate` ### Monitoring `swarm_status`, `agent_list`, `agent_metrics`, `task_status`, `task_results` ### Memory & Neural `memory_usage`, `neural_status`, `neural_train`, `neural_patterns` ### GitHub Integration `github_swarm`, `repo_analyze`, `pr_enhance`, `issue_triage`, `code_review` ### System `benchmark_run`, `features_detect`, `swarm_monitor` ## 📋 Agent Coordination Protocol ### Every Agent MUST: **1️⃣ BEFORE Work:** ```bash npx claude-flow@alpha hooks pre-task --description "[task]" npx claude-flow@alpha hooks session-restore --session-id "swarm-[id]" ``` **2️⃣ DURING Work:** ```bash npx claude-flow@alpha hooks post-edit --file "[file]" --memory-key "swarm/[agent]/[step]" npx claude-flow@alpha hooks notify --message "[what was done]" ``` **3️⃣ AFTER Work:** ```bash npx claude-flow@alpha hooks post-task --task-id "[task]" npx claude-flow@alpha hooks session-end --export-metrics true ``` ## 🎯 Concurrent Execution Examples ### ✅ CORRECT (Single Message): ```javascript [BatchTool]: // Initialize swarm mcp__claude-flow__swarm_init { topology: "mesh", maxAgents: 6 } mcp__claude-flow__agent_spawn { type: "researcher" } mcp__claude-flow__agent_spawn { type: "coder" } mcp__claude-flow__agent_spawn { type: "tester" } // Spawn agents with Task tool Task("Research agent: Analyze requirements...") Task("Coder agent: Implement features...") Task("Tester agent: Create test suite...") // Batch todos TodoWrite { todos: [ {id: "1", content: "Research", status: "in_progress", priority: "high"}, {id: "2", content: "Design", status: "pending", priority: "high"}, {id: "3", content: "Implement", status: "pending", priority: "high"}, {id: "4", content: "Test", status: "pending", priority: "medium"}, {id: "5", content: "Document", status: "pending", priority: "low"} ]} // File operations Bash "mkdir -p app/{src,tests,docs}" Write "app/src/index.js" Write "app/tests/index.test.js" Write "app/docs/README.md" ``` ### ❌ WRONG (Multiple Messages): ```javascript Message 1: mcp__claude-flow__swarm_init Message 2: Task("agent 1") Message 3: TodoWrite { todos: [single todo] } Message 4: Write "file.js" // This breaks parallel coordination! ``` ## Performance Benefits - **84.8% SWE-Bench solve rate** - **32.3% token reduction** - **2.8-4.4x speed improvement** - **27+ neural models** ## Hooks Integration ### Pre-Operation - Auto-assign agents by file type - Validate commands for safety - Prepare resources automatically - Optimize topology by complexity - Cache searches ### Post-Operation - Auto-format code - Train neural patterns - Update memory - Analyze performance - Track token usage ### Session Management - Generate summaries - Persist state - Track metrics - Restore context - Export workflows ## Advanced Features (v2.0.0) - 🚀 Automatic Topology Selection - ⚡ Parallel Execution (2.8-4.4x speed) - 🧠 Neural Training - 📊 Bottleneck Analysis - 🤖 Smart Auto-Spawning - 🛡️ Self-Healing Workflows - 💾 Cross-Session Memory - 🔗 GitHub Integration ## Integration Tips 1. Start with basic swarm init 2. Scale agents gradually 3. Use memory for context 4. Monitor progress regularly 5. Train patterns from success 6. Enable hooks automation 7. Use GitHub tools first ## Support - Documentation: https://github.com/ruvnet/claude-flow - Issues: https://github.com/ruvnet/claude-flow/issues --- Remember: **Claude Flow coordinates, Claude Code creates!**