# 🚀 Claude Code & GLM Suite - Master Integration Prompt > **Complete installation with ALL sources, explanations, and real-life examples** --- ## ⚠️ BEFORE YOU BEGIN - Read This First! ### **If Using Z.AI / GLM Coding Plan (90% cheaper):** **You MUST configure GLM FIRST before using Claude Code!** **🎯 EASIEST METHOD - Use Z.AI Coding Helper Wizard:** ```bash # Step 1: Install the coding helper npm install -g @z_ai/coding-helper # Step 2: Run the interactive GLM setup wizard npx @z_ai/coding-helper init # The wizard will: # - Ask for your Z.AI API key # - Configure Claude Code for GLM automatically # - Set up proper model mappings (glm-4.5-air, glm-4.7) # - Verify everything works # Step 3: Start Claude Code with GLM configured claude # Step 4: Verify GLM is working (enter /status when prompted) /status ``` **📖 Official GLM Documentation:** https://docs.z.ai/devpack/tool/claude --- **Alternative: Manual Configuration (if you prefer):** ```bash # Step 1: Get your API key # Visit: https://z.ai/ # Sign up for GLM Coding Plan and copy your API key # Step 2: Install Claude Code (if not installed) npm install -g @anthropic-ai/claude-code # Step 3: Create Claude Code settings mkdir -p ~/.claude cat > ~/.claude/settings.json << 'EOF' { "env": { "ANTHROPIC_AUTH_TOKEN": "YOUR_ZAI_API_KEY_HERE", "ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic", "API_TIMEOUT_MS": "3000000", "ANTHROPIC_DEFAULT_HAIKU_MODEL": "glm-4.5-air", "ANTHROPIC_DEFAULT_SONNET_MODEL": "glm-4.7", "ANTHROPIC_DEFAULT_OPUS_MODEL": "glm-4.7" } } EOF # Step 4: Start Claude Code claude ``` --- ### **If Using Anthropic Claude (Official API):** ```bash # Step 1: Get your API key # Visit: https://console.anthropic.com/ # Step 2: Create Claude Code settings mkdir -p ~/.claude cat > ~/.claude/settings.json << 'EOF' { "env": { "ANTHROPIC_AUTH_TOKEN": "sk-ant-your-api-key-here", "ANTHROPIC_BASE_URL": "https://api.anthropic.com" } } EOF # Step 3: Install Claude Code (if not installed) npm install -g @anthropic-ai/claude-code # Step 4: Start Claude Code claude ``` --- ## 📋 HOW TO USE THE MASTER PROMPT **Once Claude Code is running and configured:** ### **✂️ COPY FROM HERE:** ```text ═══════════════════════════════════════════════════════════════════════════════ I want to install the Ultimate Claude Code & GLM Customization Suite with ALL integrations including agents, MCP tools, code analysis, and automation. Please perform the following complete integration: ## Overview: What We're Installing This suite includes 7 major components from open-source projects: 1. **contains-studio/agents** - 38 specialized AI agents with PROACTIVELY auto-triggering 2. **@z_ai/mcp-server** - 8 vision and analysis tools (screenshots, videos, diagrams) 3. **@z_ai/coding-helper** - Web search, GitHub integration, GLM setup wizard 4. **llm-tldr** - Token-efficient code analysis (95% reduction, semantic search) 5. **ui-ux-pro-max-skill** - Professional UI/UX design agent with PROACTIVELY auto-triggering 6. **@iannuttall/ralph** - Autonomous agent looping CLI with hooks integration (Advanced - Optional) 7. **claude-codex-settings** - MCP configuration patterns and best practices (reference) **Agent Architecture Patterns:** The PROACTIVELY coordinator agents (studio-coach, studio-producer, experiment-tracker, project-shipper) implement proven coordination patterns from autonomous agent frameworks like Ralph, using supervisor-agent delegation and shared context management. Each component is explained below with real-life examples showing the benefits. --- ## Step 1: Install Contains Studio Agents (38 agents with PROACTIVELY auto-triggering) Source: https://github.com/contains-studio/agents Clone the contains-studio/agents repository: git clone https://github.com/contains-studio/agents.git /tmp/contains-studio-agents Copy all agents to Claude Code agents directory: cp -r /tmp/contains-studio-agents/agents/* ~/.claude/agents/ ### What This Provides: 37 specialized agents across 8 departments: - **Engineering (7):** AI Engineer, Backend Architect, DevOps Automator, Frontend Developer, Mobile Builder, Rapid Prototyper, Test Writer/Fixer - **Marketing (7):** TikTok Strategist, Growth Hacker, Content Creator, Instagram Curator, Reddit Builder, Twitter Engager, App Store Optimizer - **Design (6):** Brand Guardian, UI Designer, UX Researcher, Visual Storyteller, Whimsy Injector, **UI/UX Pro Max** - **Product (3):** Feedback Synthesizer, Sprint Prioritizer, Trend Researcher - **Project Management (3):** Experiment Tracker, Project Shipper, Studio Producer - **Studio Operations (5):** Analytics Reporter, Finance Tracker, Infrastructure Maintainer, Legal Compliance Checker, Support Responder - **Testing (5):** API Tester, Performance Benchmarker, Test Results Analyzer, Tool Evaluator, Workflow Optimizer - **Bonus (3):** Studio Coach, Agent Updater, Joker ### 🎯 Auto-Triggering System: How Agents Coordinate **Architecture Overview:** The 38 agents are divided into two types: - **7 PROACTIVELY Coordinators** - Auto-trigger based on context and coordinate specialists - **1 Utility Agent** - Manually triggered for maintenance (agent-updater) - **30 Specialist Agents** - Execute specific domain tasks when called **How It Works:** There are **two pathways** to use agents: 1. **Automatic** - Coordinators auto-trigger and call specialists as needed 2. **Direct** - You manually invoke any specialist for precise control This gives you automation when you want it, control when you need it. --- **7 PROACTIVELY Agents** (meta-coordinators that auto-trigger based on context): **Plus 1 Utility Agent** (manually triggered for maintenance): #### Design Department (2) 1. **ui-ux-pro-max** - Triggers on UI/UX design work - Professional design patterns and accessibility - 50+ styles, 97 color palettes, WCAG compliance - Example: "Create a pricing page" → ui-ux-pro-max applies professional design patterns 2. **whimsy-injector** - Triggers after UI/UX changes - Adds delightful micro-interactions - Makes interfaces memorable - Example: You create a loading spinner → whimsy-injector adds bounce animation and encouraging messages #### Engineering Department (1) 3. **test-writer-fixer** - Triggers after code modifications - Automatically writes comprehensive tests - Fixes broken tests - Example: You modify authentication code → test-writer-fixer writes unit tests automatically #### Project Management Department (3) 4. **experiment-tracker** - Triggers when experiments are started or modified - Tracks A/B tests and feature experiments - Defines metrics and monitors results - Example: You add a feature flag → experiment-tracker sets up tracking and success metrics 5. **studio-producer** - Triggers when coordinating across multiple teams - Cross-team coordination and resource allocation - Workflow optimization and dependency management - Example: "Design and engineering need to collaborate" → studio-producer schedules and coordinates 6. **project-shipper** - Triggers when approaching launch milestones - Launch coordination and release management - Go-to-market strategy and stakeholder communication - Example: "We're releasing next week" → project-shipper plans launch activities #### Bonus Department (1) 7. **studio-coach** - Triggers on complex multi-agent tasks - Coordinates multiple specialists - Motivates and aligns agents when stuck - Example: "Build a viral TikTok app in 2 weeks" → studio-coach organizes frontend, backend, marketing agents #### Utility Agent (On-Demand) 8. **agent-updater** - Manually triggered for agent synchronization - Checks GitHub for agent updates - Downloads and installs new/updated agents safely - Preserves local customizations - Example: "Check for agent updates" → agent-updater fetches latest from GitHub and syncs your installation --- **🔄 Real Workflow Example:** You: "I need a viral TikTok app in 2 weeks" ↓ [studio-coach PROACTIVELY triggers] ↓ studio-coach analyzes and coordinates: → rapid-prototyper (specialist) builds MVP → tiktok-strategist (specialist) plans viral features → frontend-developer (specialist) builds UI ↓ [whimsy-injector PROACTIVELY triggers] ↓ whimsy-injector adds delightful touches ↓ [project-shipper PROACTIVELY triggers] ↓ project-shipper plans launch strategy ↓ Result: Complete app, launch-ready ✓ **Key Point:** You don't need to manually orchestrate! The 7 coordinators automatically: - Detect context (design work, code changes, launches, etc.) - Call appropriate specialist agents - Coordinate multi-agent workflows - Ensure quality and completeness You can ALSO directly call any specialist agent anytime for manual control. --- ## Step 1.5: Install UI/UX Pro Max Skill (Slash Commands) The repository includes the **ui-ux-pro-max skill** which provides design guidance and recommendations. Install from the cloned repository: ```bash # Create skills directory mkdir -p ~/.claude/skills # Copy ui-ux-pro-max skill (includes search.py for design patterns) cp -r claude-code-glm-suite/skills/ui-ux-pro-max ~/.claude/skills/ # Make Python scripts executable chmod +x ~/.claude/skills/ui-ux-pro-max/scripts/*.py ``` ### What This Provides: **ui-ux-pro-max skill** - A slash command you can invoke anytime: - Usage: Type `/ui-ux-pro-max` or use it in your prompts - Search 50+ design patterns by keyword, domain, and stack - Get recommendations for landing pages, dashboards, pricing tables, etc. - Style guidance: glassmorphism, minimalism, brutalism, dark mode - Color schemes, typography pairs, component examples - WCAG 2.1 accessibility compliance Example: "Use ui-ux-pro-max to design a professional CTA section" ## Step 1.75: Install Agent Sync Script (Keep Agents Updated) Source: https://github.rommark.dev/admin/claude-code-glm-suite Install the automated sync script to keep your agents up-to-date: # Download sync-agents.sh wget -O ~/.claude/sync-agents.sh https://raw.githubusercontent.com/github.rommark.dev/admin/claude-code-glm-suite/main/sync-agents.sh # Make it executable chmod +x ~/.claude/sync-agents.sh ### What This Provides: **Automated agent synchronization:** - Fetches latest agents from official GitHub repository - Creates timestamped backups before updating - Detects new, updated, and customized agents - Safely updates while preserving your customizations - Commits changes to git (if configured) - Pushes updates to your Gitea repository **Usage:** # Run sync manually ~/.claude/sync-agents.sh # Or set Gitea repository URL to auto-push export GITEA_REPO_URL="https://github.rommark.dev/admin/claude-code-glm-suite.git" ~/.claude/sync-agents.sh # Also update repository agents (for installers) export REPO_AGENTS_DIR="/path/to/claude-code-glm-suite/agents" ~/.claude/sync-agents.sh **How it works:** 1. Clones latest agents from `https://github.com/contains-studio/agents` 2. Compares with your local `~/.claude/agents/` 3. Identifies: - ✨ New agents to add - 📝 Updated agents to refresh - ⚠️ Custom agents to preserve 4. Creates backup: `~/.claude/agents.backup.YYYYMMDD-HHMMSS/` 5. Applies updates safely 6. Reports all changes 7. [Optional] Commits to git and pushes to Gitea **Key Features:** - **Safe Updates**: Always creates backups before modifying - **Smart Detection**: Preserves agents you've customized - **Dual Sync**: Updates both local agents AND repository agents - **Git Integration**: Commits and pushes automatically - **Detailed Logging**: Tracks all changes in `~/.claude/agents/update.log` **Keeping Agents Updated:** Run the sync script regularly (weekly or monthly) to get the latest agent improvements: # Add to crontab for automatic weekly sync 0 2 * * 0 ~/.claude/sync-agents.sh Or manually check for updates anytime: ~/.claude/sync-agents.sh **Rollback if needed:** # List backups ls -la ~/.claude/agents.backup.* # Restore a backup rm -rf ~/.claude/agents mv ~/.claude/agents.backup.YYYYMMDD-HHMMSS ~/.claude/agents --- ## Step 2: Install MCP Vision & Analysis Tools Source: https://github.com/zai-ai/mcp-server Install Z.AI MCP server for vision tools: npm install -g @z_ai/mcp-server Verify installation: npx @z_ai/mcp-server --help ### What This Provides (8 MCP Tools): 1. **analyze_image** - Understand any image (screenshots, photos, designs) 2. **analyze_video** - Analyze video content for key moments 3. **ui_to_artifact** - Convert UI screenshots to code 4. **extract_text** - OCR text extraction from images 5. **diagnose_error** - Understand errors from screenshots 6. **ui_diff_check** - Compare two UI screenshots 7. **analyze_data_viz** - Extract insights from dashboards/charts 8. **understand_diagram** - Understand technical diagrams (UML, flowcharts) **Key Features:** - Screenshot-to-code conversion - Error diagnosis from images - Video and diagram understanding - UI comparison and analysis --- ## Step 3: Install MCP Web & GitHub Tools Source: https://github.com/fcakyon/claude-codex-settings (inspired by) Install Z.AI coding helper: npm install -g @z_ai/coding-helper Verify installation: npx @z_ai/coding-helper --help ### What This Provides (3 MCP Tools): 1. **web-search-prime** - AI-optimized web search for current information 2. **web-reader** - Convert any web page to clean markdown 3. **zread** - Read and analyze entire GitHub repositories **Key Features:** - Real-time web search for current information - Repository analysis without manual file pasting - Web page to clean markdown conversion --- ## Step 4: Install llm-tldr (Token-Efficient Code Analysis) Source: https://github.com/parcadei/llm-tldr Install llm-tldr: pip install llm-tldr Initialize for current directory: tldr warm . ### What This Provides: **95% token reduction** while preserving code understanding via 5-layer analysis: - **Layer 1 (AST)** - Functions, classes, imports structure - **Layer 2 (Call Graph)** - Who calls what (forward/backward) - **Layer 3 (CFG)** - Control flow, complexity metrics - **Layer 4 (DFG)** - Data flow, variable transformations - **Layer 5 (PDG)** - Program dependence, slicing for debugging **18 MCP Tools including:** - `context` - LLM-ready summaries (95% token savings) - `semantic` - Search by behavior, not just text - `slice` - Show only code affecting a specific line - `impact` - Who calls this function? (refactoring safety) - `cfg` - Control flow graph - `dfg` - Data flow graph - And 12 more... **Key Features:** - 95% token reduction for large codebases - Semantic search by behavior (not just text) - Program slicing for precise debugging - Impact analysis for safe refactoring --- ## Step 5: Configure MCP Servers in Claude Code Create MCP configuration for TLDR: mkdir -p ~/.claude cat > ~/.claude/claude_desktop_config.json << 'EOF' { "mcpServers": { "tldr": { "command": "tldr-mcp", "args": ["--project", "."] }, "zai-vision": { "command": "npx", "args": ["@z_ai/mcp-server"] } } } EOF Note: TLDR MCP server provides 18 tools for code analysis. Vision tools from @z_ai/mcp-server are automatically available. --- ## Step 6: Configure Ralph CLI Autonomous Looping (Advanced - Optional) Source: https://github.com/iannuttall/ralph **What Ralph Provides:** Ralph is an autonomous agent orchestration tool that creates infinite loops until task completion. Unlike single-pass PROACTIVELY agents, Ralph maintains state across multiple iterations and coordinates agents through complex workflows. **Key Differences:** - **PROACTIVELY Agents**: Single-pass coordination, one interaction - **Ralph CLI**: Infinite looping, stateful, autonomous until done ### Installation: Install Ralph CLI: npm install -g @iannuttall/ralph Verify installation: ralph --version ### Create Ralph Auto-Trigger Hook: mkdir -p ~/.claude/hooks cat > ~/.claude/hooks/ralph-auto-trigger.sh << 'EOF' #!/bin/bash # Ralph Auto-Trigger Hook - Enhanced with Background Task Spawning # Automatically starts Ralph CLI in background when needed # # Modes (via RALPH_AUTO_MODE environment variable): # "always" - Start Ralph for every request # "agents" - Only for agent requests (default) # "off" - Disable auto-trigger # # Background Execution: # - Ralph runs as background process (non-blocking) # - Claude Code continues immediately # - Ralph output logged to: ~/.claude/ralph-output.log # - Ralph PID tracked in: ~/.claude/ralph.pid set -euo pipefail # Configuration CLAUDE_DIR="$HOME/.claude" RALPH_STATE_FILE="$CLAUDE_DIR/ralph-loop.local.md" RALPH_PID_FILE="$CLAUDE_DIR/ralph.pid" RALPH_LOG_FILE="$CLAUDE_DIR/ralph-output.log" RALPH_LOCK_FILE="$CLAUDE_DIR/ralph.lock" # Read hook input from stdin HOOK_INPUT=$(cat) USER_PROMPT=$(echo "$HOOK_INPUT" | jq -r '.prompt // empty' 2>/dev/null || echo "") # Fallback: if no JSON input, use first argument if [[ -z "$USER_PROMPT" && $# -gt 0 ]]; then USER_PROMPT="$1" fi # Get Ralph mode (default: agents) RALPH_AUTO_MODE="${RALPH_AUTO_MODE:-agents}" RALPH_MAX_ITERATIONS="${RALPH_MAX_ITERATIONS:-50}" # Exit if auto-trigger is disabled if [[ "$RALPH_AUTO_MODE" == "off" ]]; then exit 0 fi # Check if Ralph is already running (via lock file) if [[ -f "$RALPH_LOCK_FILE" ]]; then # Check if process is still alive LOCK_PID=$(cat "$RALPH_LOCK_FILE" 2>/dev/null || echo "") if [[ -n "$LOCK_PID" ]] && kill -0 "$LOCK_PID" 2>/dev/null; then # Ralph is already running, don't start another instance exit 0 else # Lock file exists but process is dead, clean up rm -f "$RALPH_LOCK_FILE" "$RALPH_PID_FILE" fi fi # Agent detection list (lowercase for matching) AGENTS=( "ai-engineer" "backend-architect" "devops-automator" "frontend-developer" "mobile-app-builder" "rapid-prototyper" "test-writer-fixer" "tiktok-strategist" "growth-hacker" "content-creator" "instagram-curator" "reddit-builder" "twitter-engager" "app-store-optimizer" "brand-guardian" "ui-designer" "ux-researcher" "visual-storyteller" "whimsy-injector" "ui-ux-pro-max" "feedback-synthesizer" "sprint-prioritizer" "trend-researcher" "experiment-tracker" "project-shipper" "studio-producer" "studio-coach" "analytics-reporter" "finance-tracker" "infrastructure-maintainer" "legal-compliance-checker" "support-responder" "api-tester" "performance-benchmarker" "test-results-analyzer" "tool-evaluator" "workflow-optimizer" "joker" "agent-updater" "explore" "plan" "general-purpose" ) # Detect agent request (case-insensitive) agent_detected=false detected_agent="" for agent in "${AGENTS[@]}"; do if echo "$USER_PROMPT" | grep -iq "$agent"; then agent_detected=true detected_agent="$agent" break fi done # Determine if we should start Ralph should_trigger=false case "$RALPH_AUTO_MODE" in "always") # Trigger on all prompts should_trigger=true ;; "agents") # Only trigger on agent requests OR development keywords if [[ "$agent_detected" == true ]]; then should_trigger=true elif echo "$USER_PROMPT" | grep -qiE "build|create|implement|develop|fix|add|refactor|optimize|write|generate|delegate|autonomous"; then should_trigger=true detected_agent="general-development" fi ;; esac if [[ "$should_trigger" == true ]]; then # Create Ralph state file mkdir -p "$CLAUDE_DIR" cat > "$RALPH_STATE_FILE" << EOF # Ralph Loop State - Auto-Triggered # Generated: $(date -u +"%Y-%m-%d %H:%M:%S UTC") **User Request:** $USER_PROMPT **Detected Agent:** $detected_agent **Mode:** $RALPH_AUTO_MODE **Max Iterations:** $RALPH_MAX_ITERATIONS **Timestamp:** $(date -Iseconds) ## Context This state file was automatically generated by the Ralph auto-trigger hook. Ralph CLI will read this file and autonomously execute the request. ## Auto-Trigger Details - Triggered by: Claude Code UserPromptSubmit hook - Trigger mode: $RALPH_AUTO_MODE - Background execution: Yes (non-blocking) - Log file: $RALPH_LOG_FILE ## Usage Ralph is running autonomously in the background. Monitor progress: bash # View Ralph output in real-time tail -f ~/.claude/ralph-output.log # Check if Ralph is still running ps aux | grep ralph # Stop Ralph manually kill $(cat ~/.claude/ralph.pid) rm ~/.claude/ralph.lock EOF # Spawn Ralph in background (NON-BLOCKING) if command -v ralph &> /dev/null; then # Create log file touch "$RALPH_LOG_FILE" # Start Ralph in background with nohup (survives terminal close) echo "[$(date -u +"%Y-%m-%d %H:%M:%S UTC")] Starting Ralph in background..." >> "$RALPH_LOG_FILE" echo "Mode: $RALPH_AUTO_MODE" >> "$RALPH_LOG_FILE" echo "Agent: $detected_agent" >> "$RALPH_LOG_FILE" echo "Max iterations: $RALPH_MAX_ITERATIONS" >> "$RALPH_LOG_FILE" echo "---" >> "$RALPH_LOG_FILE" # Start Ralph in background nohup ralph build "$RALPH_MAX_ITERATIONS" >> "$RALPH_LOG_FILE" 2>&1 & RALPH_PID=$! # Save PID for tracking echo "$RALPH_PID" > "$RALPH_PID_FILE" echo "$RALPH_PID" > "$RALPH_LOCK_FILE" # Log the trigger { echo "[$(date -u +"%Y-%m-%d %H:%M:%S UTC")] Ralph auto-triggered" echo " Mode: $RALPH_AUTO_MODE" echo " Agent: $detected_agent" echo " PID: $RALPH_PID" echo " Log: $RALPH_LOG_FILE" } >> "$CLAUDE_DIR/ralph-trigger.log" 2>/dev/null || true # Notify user via stderr (visible in Claude Code) echo "🔄 Ralph CLI auto-started in background" >&2 echo " PID: $RALPH_PID" >&2 echo " Agent: $detected_agent" >&2 echo " Monitor: tail -f ~/.claude/ralph-output.log" >&2 echo " Stop: kill \$(cat ~/.claude/ralph.pid)" >&2 else # Ralph not installed, just create state file echo "⚠️ Ralph CLI not installed. State file created for manual use." >&2 echo " Install: npm install -g @iannuttall/ralph" >&2 fi fi # Exit immediately (NON-BLOCKING - Claude Code continues) exit 0 EOF chmod +x ~/.claude/hooks/ralph-auto-trigger.sh ### Configure Hooks: Create hooks.json to enable Ralph auto-trigger: cat > ~/.claude/hooks.json << 'EOF' { "description": "User hooks for Ralph auto-trigger", "hooks": { "UserPromptSubmit": [ { "hooks": [ { "type": "command", "command": "/home/uroma/.claude/hooks/ralph-auto-trigger.sh", "timeout": 5 } ] } ] } } EOF Note: Replace "/home/uroma" with your actual home directory if different. ### What This Provides: **Background Autonomous Looping:** - Ralph runs as background process (NON-BLOCKING) - Claude Code continues immediately - Ralph output logged to: ~/.claude/ralph-output.log - Ralph PID tracked in: ~/.claude/ralph.pid - State preserved in `.claude/ralph-loop.local.md` - Can handle complex multi-step workflows **Control Modes:** # Mode 1: Auto-loop only for agent requests (default) export RALPH_AUTO_MODE="agents" # Mode 2: Auto-loop for everything export RALPH_AUTO_MODE="always" # Mode 3: Disable auto-loop export RALPH_AUTO_MODE="off" # Set max iterations (default: 50) export RALPH_MAX_ITERATIONS="100" ### How It Works (Background Execution): 1. You request in Claude Code: "Build a TikTok app in 2 weeks" 2. Hook detects agent language 3. Hook spawns Ralph in BACKGROUND (non-blocking) 4. Claude Code immediately continues (you can keep working!) 5. Ralph autonomously iterates in background: - Iteration 1: rapid-prototyper builds MVP - Iteration 2: tiktok-strategist plans viral features - Iteration 3: frontend-developer builds UI - Continues until Ralph completes max iterations 6. Monitor Ralph: tail -f ~/.claude/ralph-output.log 7. Stop Ralph anytime: kill $(cat ~/.claude/ralph.pid) ### Monitoring Ralph: # View Ralph output in real-time tail -f ~/.claude/ralph-output.log # Check if Ralph is still running ps aux | grep ralph # Or check the PID file cat ~/.claude/ralph.pid # View Ralph trigger log cat ~/.claude/ralph-trigger.log ### Canceling Ralph: If Ralph is running in background and you want to stop: # Method 1: Kill by PID (recommended) kill $(cat ~/.claude/ralph.pid) rm ~/.claude/ralph.lock # Method 2: Kill by process name pkill -f "ralph build" # Method 3: Disable auto-mode (prevents future triggers) export RALPH_AUTO_MODE="off" # Method 4: Delete state file (Ralph will stop on next iteration) rm ~/.claude/ralph-loop.local.md ### Ralph vs PROACTIVELY Agents: | Feature | Ralph CLI (Background) | PROACTIVELY Agents | |---------|------------------------|-------------------| | **Execution** | Multi-loop, background process | Single interaction | | **Blocking** | NON-BLOCKING (runs in bg) | Blocking (waits for response) | | **State** | Persisted in file + log | Context only | | **Control** | Manual stop via PID | Automatic completion | | **Complexity** | Handles complex workflows | Quick coordination | | **Setup** | Requires hook + script | Built-in to agents | | **Best For** | Large projects, overnight coding | Quick tasks, human collaboration | **You can use BOTH together:** - Use Ralph for complex, multi-step projects (autonomous) - Use PROACTIVELY agents for quick tasks (interactive) - Set RALPH_AUTO_MODE="agents" for hybrid approach - Ralph runs in background while you continue working in Claude Code --- ## Step 7: Verify Complete Installation Run comprehensive checks: # 1. Verify agents (should be 38+ files) ls -la ~/.claude/agents/ find ~/.claude/agents -name "*.md" | wc -l # 2. Verify MCP tools npx @z_ai/mcp-server --help npx @z_ai/coding-helper --help tldr --help # 3. Verify TLDR indexing tldr status . # 4. Verify Ralph CLI (if installed) ralph --version # 5. Verify Ralph hook ls -la ~/.claude/hooks/ralph-auto-trigger.sh # 6. Test Claude Code claude --version Expected results: - ✅ 39+ agent files (38 agents + README) - ✅ All 8 departments present - ✅ MCP tools accessible - ✅ TLDR indexed current directory - ✅ Settings configured - ⚠️ Ralph CLI (optional - verify if installed in Step 6) --- ## Summary: What You Now Have ### 40+ AI Agents Organized across 8 departments with PROACTIVELY auto-triggering ### 15+ MCP Tools - 8 vision/analysis tools (@z_ai/mcp-server) - 3 web/GitHub tools (@z_ai/coding-helper) - 18 code analysis tools (llm-tldr) ### Three Auto-Triggering Systems - **PROACTIVELY keyword** (context-aware, built into agents) - **Ralph CLI** (autonomous looping, optional advanced setup) - **Hooks-based** (event-driven, for Ralph integration) ### Complete Integration Benefits **Token Efficiency:** - TLDR reduces code context by 95% - $100/month savings on API costs for large codebases **Code Understanding:** - 5-layer analysis (AST → PDG) - Semantic search by behavior - Impact analysis for safe refactoring **Visual Capabilities:** - Screenshot-to-code conversion - Error diagnosis from images - Video and diagram understanding **Automation:** - Auto-test writing after code changes - Auto-UI enhancement after design changes - Auto-coordination for complex tasks **Up-to-Date Information:** - Real-time web search - Repository analysis - Current documentation Please execute this complete integration step by step and confirm when each component is installed. ═══════════════════════════════════════════════════════════════════════════════ ``` **✂️ COPY ENDS HERE** --- --- # ═══════════════════════════════════════════════════════════════════════════════ # 📖 REAL-LIFE EXAMPLES: HOW AUTO-TRIGGERING WORKS # ═══════════════════════════════════════════════════════════════════════════════ > ⚠️ **IMPORTANT: These are ILLUSTRATIVE EXAMPLES ONLY** > > The examples below demonstrate the conceptual difference between working **without** auto-triggering agents versus **with** auto-triggering agents. > These are simplified scenarios to help you understand how the PROACTIVELY system improves your workflow. > > **These are not actual output promises** - actual results will vary based on your specific use case, context, and model capabilities. ## Example 1: Adding OAuth Authentication ### ❌ WITHOUT Auto-Triggering Agents You: Help me add OAuth to my app Claude: [Writes code, but no tests] You: [Manually write tests later] Claude: [Fixes bugs] ### ✅ WITH Auto-Triggering Agents You: Help me add OAuth to my app Claude: [Writes code] [test-writer-fixer PROACTIVELY triggers] test-writer-fixer: I've automatically created comprehensive tests for your OAuth implementation Result: Code + tests complete in one interaction ✓ --- ## Example 2: Creating a Loading Spinner ### ❌ WITHOUT Auto-Triggering Agents You: Create a loading spinner Claude: [Creates basic spinner] You: [Manually request improvements later] Claude: [Adds animation] ### ✅ WITH Auto-Triggering Agents You: Create a loading spinner Claude: [Creates spinner] [whimsy-injector PROACTIVELY triggers] whimsy-injector: I've enhanced your spinner with bounce animation and encouraging messages Result: Delightful spinner created automatically ✓ --- ## Example 3: Launching a New Feature ### ❌ WITHOUT Auto-Triggering Agents You: We're launching next week Claude: [Doesn't know what to prepare] You: [Manually create launch checklist] Claude: [Helps with specific items] ### ✅ WITH Auto-Triggering Agents You: We're launching next week [project-shipper PROACTIVELY triggers] project-shipper: I've created a complete launch plan with: - Pre-launch readiness checklist - Go-to-market strategy - Stakeholder communication plan - Success metrics and monitoring Result: Launch-ready plan created automatically ✓ --- ## Example 4: Adding a Feature Flag ### ❌ WITHOUT Auto-Triggering Agents You: Add feature flag for new checkout Claude: [Adds flag, but no tracking] You: [Manually set up analytics later] ### ✅ WITH Auto-Triggering Agents You: Add feature flag for new checkout Claude: [Adds flag] [experiment-tracker PROACTIVELY triggers] experiment-tracker: I've configured tracking for this experiment: - Success metrics defined - Analytics events set up - Rollout plan created - Sample size calculated Result: A/B test ready to run ✓ --- ## Example 5: Complex Multi-Agent Task ### ❌ WITHOUT Auto-Triggering Agents You: Build viral TikTok app in 2 weeks Claude: [Overwhelmed, doesn't know where to start] You: [Manually coordinate frontend, backend, marketing teams] ### ✅ WITH Auto-Triggering Agents You: Build viral TikTok app in 2 weeks [studio-coach PROACTIVELY triggers] studio-coach: I'll coordinate this complex project: → Calling rapid-prototyper for MVP → Calling tiktok-strategist for viral features → Calling frontend-developer for UI → Calling project-hipper for launch planning [All agents work together seamlessly] Result: Complete app, launch-ready, on time ✓ --- ## Key Differences Summary | Aspect | Without Auto-Trigger | With Auto-Trigger | |--------|---------------------|-------------------| | **Test Coverage** | Manual, forgettable | Automatic, comprehensive | | **UI Polish** | Basic, functional | Delightful, memorable | | **Launch Prep** | Last-minute, chaotic | Planned, organized | | **Experiment Tracking** | Missing, incomplete | Defined, measurable | | **Complex Projects** | Overwhelming, fragmented | Coordinated, smooth | | **Follow-up Required** | Always needed | Built-in | --- ## Why This Matters **Time Savings:** - Without: Multiple back-and-forth interactions - With: Complete in one interaction **Quality:** - Without: Inconsistent, forgets steps - With: Comprehensive, best practices applied **Peace of Mind:** - Without: Did I remember everything? - With: Agents handle it automatically **Scalability:** - Without: Manual coordination doesn't scale - With: Automated coordination handles complexity --- ## ⚡ PROACTIVELY vs Hooks-Based ### PROACTIVELY System (Context-Aware) **How it works:** - Claude analyzes the context of your request - Automatically determines which coordinator to trigger - Coordinator calls specialist agents as needed **Examples:** - You write code → test-writer-fixer triggers - You design UI → whimsy-injector triggers - You mention launch → project-shipper triggers **Benefits:** - No configuration needed - Works based on natural language context - Can be overridden by manual requests ### Hooks-Based System (Event-Driven) **How it works:** - Configured in `~/.claude/hooks.json` - Triggers on specific events (prompt submit, tool output, etc.) **Example Configuration:** ```json { "userPromptSubmitHook": "test-writer-fixer@agent", "toolOutputHook": "whimsy-injector@agent" } ``` **Benefits:** - Predictable triggers - Fine-grained control - Works alongside PROACTIVELY system ### Combined Power You can use BOTH systems together: - PROACTIVELY: Automatic context-aware triggers - Hooks: Additional event-driven triggers - Result: Comprehensive automation coverage --- # 📚 AGENT DEPARTMENTS AND SPECIALTIES ## Engineering Department (7 agents) 1. **ai-engineer** - ML/LLM feature integration 2. **backend-architect** - API design, database optimization 3. **devops-automator** - CI/CD, cloud infrastructure 4. **frontend-developer** - React/Vue/Angular UI development 5. **mobile-app-builder** - iOS/Android native apps 6. **rapid-prototyper** - Quick MVPs and prototypes 7. **test-writer-fixer** - Auto test writing (PROACTIVELY) ## Marketing Department (7 agents) 1. **tiktok-strategist** - Viral TikTok marketing 2. **growth-hacker** - Viral growth loops 3. **content-creator** - Multi-platform content 4. **instagram-curator** - Visual content strategy 5. **reddit-community-builder** - Reddit engagement 6. **twitter-engager** - Twitter growth 7. **app-store-optimizer** - ASO and rankings ## Design Department (6 agents) 1. **brand-guardian** - Brand consistency 2. **ui-designer** - Interface design 3. **ux-researcher** - User research 4. **visual-storyteller** - Visual narratives 5. **whimsy-injector** - Delight injection (PROACTIVELY) 6. **ui-ux-pro-max** - Professional UI/UX (PROACTIVELY) ## Product Department (3 agents) 1. **feedback-synthesizer** - User feedback analysis 2. **sprint-prioritizer** - Feature prioritization 3. **trend-researcher** - Market trend identification ## Project Management Department (3 agents) 1. **experiment-tracker** - A/B test tracking (PROACTIVELY) 2. **project-shipper** - Launch coordination (PROACTIVELY) 3. **studio-producer** - Team coordination (PROACTIVELY) ## Studio Operations Department (5 agents) 1. **analytics-reporter** - Data insights 2. **finance-tracker** - Financial management 3. **infrastructure-maintainer** - System maintenance 4. **legal-compliance-checker** - Legal compliance 5. **support-responder** - Customer support ## Testing Department (5 agents) 1. **api-tester** - API testing 2. **performance-benchmarker** - Performance optimization 3. **test-results-analyzer** - Test analysis 4. **tool-evaluator** - Tool assessment 5. **workflow-optimizer** - Process improvement ## Bonus Department (3 agents) 1. **studio-coach** - Agent coordination (PROACTIVELY) 2. **agent-updater** - Agent updates (on-demand) 3. **joker** - Humor injection --- # 🔧 CONFIGURATION FILES ## ~/.claude/settings.json Main Claude Code configuration file: ```json { "env": { "ANTHROPIC_AUTH_TOKEN": "your-api-key", "ANTHROPIC_BASE_URL": "https://api.anthropic.com", "API_TIMEOUT_MS": "3000000" } } ``` ### For Z.AI / GLM: ```json { "env": { "ANTHROPIC_AUTH_TOKEN": "your-zai-key", "ANTHROPIC_BASE_URL": "https://api.z.ai/api/anthropic", "ANTHROPIC_DEFAULT_HAIKU_MODEL": "glm-4.5-air", "ANTHROPIC_DEFAULT_SONNET_MODEL": "glm-4.7", "ANTHROPIC_DEFAULT_OPUS_MODEL": "glm-4.7" } } ``` ## ~/.claude/claude_desktop_config.json MCP server configuration: ```json { "mcpServers": { "tldr": { "command": "tldr-mcp", "args": ["--project", "."] }, "zai-vision": { "command": "npx", "args": ["@z_ai/mcp-server"] }, "zai-web": { "command": "npx", "args": ["@z_ai/coding-helper"] } } } ``` ## ~/.claude/hooks.json Auto-triggering hooks: ```json { "userPromptSubmitHook": "test-writer-fixer@agent", "toolOutputHook": "whimsy-injector@agent" } ``` --- # 🎯 QUICK REFERENCE ## Available Commands ### Sync Agents ```bash ~/.claude/sync-agents.sh ``` ### Verify Installation ```bash ./verify-claude-setup.sh ``` ### Check TLDR Status ```bash tldr status . ``` ### Test Claude Code ```bash claude --version ``` ## Environment Variables ```bash # Auto-push to Gitea after sync export GITEA_REPO_URL="https://github.rommark.dev/admin/claude-code-glm-suite.git" # Update repository agents during sync export REPO_AGENTS_DIR="/path/to/repo/agents" ``` ## Troubleshooting ### Agents not triggering? - Check `~/.claude/agents/` directory - Verify agent files exist and have content - Restart Claude Code ### MCP tools not available? - Check `~/.claude/claude_desktop_config.json` - Verify MCP servers are installed - Restart Claude Code ### TLDR not working? - Run `tldr warm .` in project directory - Check Python installation - Verify `tldr-mcp` is in PATH --- # 📖 ADDITIONAL RESOURCES ## Official Documentation - **Claude Code**: https://docs.anthropic.com/en/docs/claude-code - **Z.AI / GLM**: https://docs.z.ai/devpack/tool/claude - **contains-studio/agents**: https://github.com/contains-studio/agents - **@z_ai/mcp-server**: https://github.com/zai-ai/mcp-server - **llm-tldr**: https://github.com/parcadei/llm-tldr ## Community & Support - **Claude Code Sub-Agents**: https://docs.anthropic.com/en/docs/claude-code/sub-agents - **Gitea Repository**: https://github.rommark.dev/admin/claude-code-glm-suite ## Changelog & Updates - Check the repository regularly for updates - Use `~/.claude/sync-agents.sh` to keep agents current - Review MASTER-PROMPT.md for latest features --- **Last Updated**: 2025-01-16 **Version**: 2.0 **Maintained By**: rommark.dev