gh-toskysun-sub-agents/commands/ai.md

Usage

/ai <TASK_DESCRIPTION | list | info <role> | workflow | auto>

🎯 Task Complexity Assessment

Level 0: Micro Tasks - Direct execution

• Scenario: Information queries, file reading, status checks
• Characteristics: No code modification, pure information retrieval
• Boundary: <5 minutes, no professional knowledge needed
• Triggers: "view", "check", "display", "read"
• Action: Main controller completes directly, no agent calls

Level 1: Simple Tasks - Single agent direct

• Scenario: Single file modification, basic configuration, simple functionality
• Characteristics: <50 lines of code, single technology stack, clear requirements
• Boundary: One professional domain, no cross-module impact
• Triggers: "add", "modify", "configure" single components
• Action: Direct call to 1 professional agent, bypassing director

Level 2: Medium Tasks - Single agent complex

• Scenario: Complete functional modules, multi-file coordination, requires testing
• Characteristics: 50-200 lines of code, requires planning and validation
• Boundary: Single technology stack but complex logic, may require refactoring
• Triggers: "implement", "develop", "build" complete features
• Action: 1 professional agent handles full process, main controller monitors

Level 3: Composite Tasks - Multi-agent serial

• Scenario: Cross-module functionality, frontend-backend coordination, 2-3 professional domains
• Characteristics: 200-500 lines of code, requires multi-step coordination
• Boundary: Clear dependency relationships, serial execution
• Triggers: "integrate", "connect", "full-stack" functionality
• Action: Main controller serially calls 2-3 agents

Level 4: Parallel Tasks - Multi-agent concurrent

• Scenario: Independent module parallel development, performance optimization, multi-platform
• Characteristics: 3-5 independent workflows, can execute in parallel
• Boundary: Low conflict risk, high independence
• Triggers: "simultaneously", "parallel", "multi-platform" development
• Action: Main controller calls 3-5 agents in parallel

Level 5: Enterprise Tasks - Director coordination

• Scenario: System refactoring, architecture upgrades, complex project analysis
• Characteristics: 5+ professional domains, complex dependencies, multi-phase planning
• Boundary: Requires specialized task decomposition and coordination management
• Triggers: "refactor", "architecture", "system analysis", "enterprise-level"
• Action: task-dispatch-director pure coordination, decompose into Level 1-3 tasks

⚡ Auto-Trigger Matrix

Level 0 Trigger Conditions (no agent calls):

• Keywords: "view", "check", "display", "read", "list", "status"
• Questions: "what is", "how to understand", "can you explain"
• Operations: Pure information queries, no modification requirements

Level 1 Trigger Conditions (single agent direct):

• Keywords: "add", "modify", "update", "configure", "adjust"
• Scope: Single file or component + single technology stack
• Examples: "add Vue component", "modify API endpoint", "configure database connection"

Level 2 Trigger Conditions (single agent complex):

• Keywords: "implement", "develop", "build", "create" functional modules
• Scope: Multi-file but single technology stack + requires testing
• Examples: "implement user login", "develop payment module", "build search functionality"

Level 3 Trigger Conditions (multi-agent serial):

• Keywords: "integrate", "connect", "full-stack", "end-to-end"
• Scope: 2-3 technology stacks collaboration + clear dependencies
• Examples: "frontend-backend integration", "API integration", "full-stack user system"

Level 4 Trigger Conditions (multi-agent parallel):

• Keywords: "simultaneously", "parallel", "multi-platform", "optimize"
• Scope: 3-5 independent modules + low conflict
• Examples: "multi-platform synchronized development", "comprehensive performance optimization"

Level 5 Trigger Conditions (Director coordination):

• Keywords: "refactor", "architecture", "system analysis", "enterprise-level", "complete solution"
• Scope: 5+ professional domains + complex planning
• Examples: "system architecture refactoring", "enterprise microservice design", "complex project analysis"

Mandatory Director Bypass Conditions (Level 0-2):

• Single file operations
• Clearly specified single technology stack
• User explicitly says "no team collaboration needed"
• Simple information queries and basic modifications

🚫 Direct Handling

Handle without agents:

• File reading, searching, basic analysis
• Simple code modifications or config updates
• Information queries and technical explanations

🛡️ Anti-Over-Engineering Principles

• One goal, one agent: Only call one agent unless true collaboration needed
• Minimum viable solution: Choose simplest working method
• User-oriented: Based on explicit user needs, not assumptions

🎯 Project-Specific Agents Support

Agent Discovery System

The AI system intelligently detects and integrates both:

• Global Agents: Standard agents from /agents/ directory (always available)
• Project Agents: Custom agents from .claude/agents/ directory (created by /initx)

Project Agent Features

• Auto-Detection: Automatically discovers agents in .claude/agents/ when present
• Priority System: Project-specific agents take precedence over global agents
• Smart Routing: Intelligently routes to project agents when they match the task better
• Seamless Integration: Works with the same /ai command interface

Using Project Agents

# After running /initx to create project-specific agents:
/ai "optimize checkout flow"  # Uses vue-ecommerce-developer if created
/ai "implement payment integration"  # Uses payment-integration-specialist
/ai list  # Shows both global and project agents

👥 Team Members (when using /ai list)

Note: This list shows global agents. If you have run /initx, project-specific agents from .claude/agents/ will also be available and displayed with a 🏢 icon.

🏛️ Leadership & Strategy

• 🎯 task-dispatch-director - Task coordination hub (⚠️ Never calls itself)
• 🏗️ cto - Technical strategy and architecture decisions
• 📊 product-manager - Product requirements and PRD creation

💻 Development Team

• 📋 technical-solution-architect - Technical solution design based on PRDs
• 🎨 frontend-developer - React expert, UI components, performance optimization
• 💾 backend-developer - Multi-stack API development (FastAPI/Spring Boot/Node.js)
• 🔧 infrastructure-developer - Development tools and automation scripts
• 🚀 devops-engineer - Docker containerization and deployment

🌟 Frontend Technology Stack Experts

• 🌟 vue-developer - Vue 2/3, Nuxt.js, component development, state management
• ⚛️ react-developer - React 18+, Next.js, modern Hooks patterns

🏗️ Backend Architecture Experts

• 🚀 go-architect - Go microservice architecture, distributed systems, cloud-native
• 🦀 rust-architect - Rust system programming, memory safety, high-performance computing
• ☕ java-developer - Java enterprise development, Spring Boot microservices
• 🌱 spring-architect - Spring full-stack, microservice architecture, enterprise design

🐍 Python Web Experts

• 🌶️ flask-expert - Flask framework, RESTful API, traditional web applications
• ⚡ fastapi-expert - FastAPI framework, async programming, high-performance APIs

📱 Mobile Development

• 📱 android-developer - Android native development, Kotlin/Java, Material Design
• 🎨 mobile-ui-designer - Mobile UI/UX design, cross-platform interfaces

🔐 Security & Reverse Engineering

• 🎣 android-hooking-expert - Frida/Hook technology, dynamic analysis
• 📱 xposed-developer - Xposed module development, system-level customization
• 🔍 reverse-engineer - Code deobfuscation, static analysis
• 🦠 malware-analyst - Malware analysis, threat detection

🌙 Scripting & Automation

• 🌙 lua-developer - Lua script development (game/web/automation scripts)

🎨 Design Experts

• 🎨 google-ui-designer - Material Design, user experience design

🔧 Quality & Operations

• 👀 code-review-expert - Code quality review, security checks
• 🚀 devops-engineer - Docker deployment, CI/CD, operations monitoring
• 🧪 test-expert - Testing strategy, automated testing, performance testing
• 🐛 qa-engineer - Problem diagnosis, root cause analysis
• 🔬 technical-researcher - Technical research, feasibility analysis

🎮 Command Modes

🎯 Task Execution (Default)

/ai "Add login feature"
/ai "Optimize API performance" 
/ai "Code review recent commits"

📚 Information

• /ai list - Show all team members (including project-specific agents if available)
• /ai info <role> - Get role details (works with both global and project agents)
• /ai auto - Enable maximum automation
• /initx - Initialize project and create custom AI team (see /initx command)

📊 Smart Parallel Task Execution Output

Smart Parallel Task Execution Output:

🧠 Intelligent Analysis (ultrathink mode activated)
- Intent: [Detected user goal with confidence %]
- Complexity: [Simple(1-2)/Medium(3-4)/Complex(5)] (Auto-assessed)
- Agent Selection: [Global agents / Project-specific agents if available]
- Parallel Strategy: [Why this parallel approach was chosen]
- Estimated Speedup: [Expected efficiency gain vs serial execution]

🚀 Parallel Execution Plan (Multi-Phase Concurrent)
Phase 1 (Parallel): [3 agents] → [Concurrent analysis/planning]
├── 🎯 [Agent A] → [Specific deliverable] (parallel group 1)
├── 🎯 [Agent B] → [Specific deliverable] (parallel group 1)  
└── 🎯 [Agent C] → [Specific deliverable] (parallel group 1)

Phase 2 (Parallel): [2 agents] → [Build on Phase 1 results]
├── 🔄 [Agent D] → [Integration task] (parallel group 2)
└── 🔄 [Agent E] → [Implementation task] (parallel group 2)

⚡ Launching Parallel AI Team...
├── 🚀 Phase 1: Launching 3 concurrent agents...
│   ├── ✅ [Agent A] completed: [result summary]
│   ├── ✅ [Agent B] completed: [result summary]  
│   └── 🔄 [Agent C] retrying... (attempt 2/3)
├── 🔄 Integrating Phase 1 results...
├── 🚀 Phase 2: Launching 2 concurrent agents with enhanced context...
│   ├── ✅ [Agent D] completed: [result summary]
│   └── ✅ [Agent E] completed: [result summary]

✅ Mission Complete (Parallel Execution)
- 📦 **Deliverables**: [What was produced across all parallel phases]
- ⚡ **Performance**: [Actual speedup achieved: 3.2x faster than serial]
- 🛡️ **Reliability**: [Retry success rate: 2 retries, 100% final success]
- 🧠 **Learning**: [Pattern for future similar parallel executions]

Parallel Execution Status Indicators:

🚀 Parallel Launch    # Multiple agents starting simultaneously
⚡ Partial Success   # Some agents completed, others retrying
🔄 Auto-Retry        # Intelligent retry with exponential backoff
✅ Phase Complete    # All agents in phase finished successfully
🔀 Context Merge     # Integrating parallel results for next phase
🛡️ Fallback Mode    # Serial execution after parallel retry exhaustion

Performance Metrics Display:

📊 Parallel Performance Dashboard
- Concurrent agents launched: 8 total across 3 phases
- Parallel efficiency gain: 4.1x faster than serial execution
- Auto-retry success rate: 94% (3 retries recovered, 1 fallback)
- Resource utilization: 87% (optimal parallel agent distribution)
- Total execution time: 12 minutes (vs 49 minutes serial estimate)

🚀 System Benefits

🎯 Precision Task Routing

• Level 0-2: Bypass director overhead → Direct specialist assignment
• Level 3-4: Coordinated multi-agent execution → Optimal resource allocation
• Level 5: Enterprise-level orchestration → Complex project management

⚡ Performance Optimization

• 3x faster for simple tasks (Level 0-1 direct execution)
• 2x more reliable for complex tasks (proper coordination)
• Zero agent overload (strict role boundaries)

🛡️ Anti-Deadlock Protection

• task-dispatch-director limited to pure coordination only
• Automatic fallback when agents fail (3-retry rule)
• Forced bypass for simple operations (Level 0-2)

🎯 Development Efficiency

1. Single Command - No need to remember specific roles
2. Intelligent Routing - Automatically engages right experts (including project-specific agents)
3. Full Workflow - Handles complete development cycle
4. Quality Gates - Ensures proper reviews and testing
5. Coordination - Manages team collaboration
6. Project Awareness - Prioritizes custom project agents when available