Ai Agent Development

1. Define agent purpose
2. Design agent capabilities
3. Plan tool integration
4. Design memory system
5. Define success metrics

Copy-Paste Prompts

Use @ai-agents-architect to design AI agent architecture

Phase 2: Single Agent Implementation

Skills to Invoke

• autonomous-agent-patterns - Agent patterns
• autonomous-agents - Autonomous agents

Actions

1. Choose agent framework
2. Implement agent logic
3. Add tool integration
4. Configure memory
5. Test agent behavior

Copy-Paste Prompts

Use @autonomous-agent-patterns to implement single agent

Phase 3: Multi-Agent System

Skills to Invoke

• crewai - CrewAI framework
• multi-agent-patterns - Multi-agent patterns

Actions

1. Define agent roles
2. Set up agent communication
3. Configure orchestration
4. Implement task delegation
5. Test coordination

Copy-Paste Prompts

Use @crewai to build multi-agent system with roles

Phase 4: Agent Orchestration

Skills to Invoke

• langgraph - LangGraph orchestration
• workflow-orchestration-patterns - Orchestration

Actions

1. Design workflow graph
2. Implement state management
3. Add conditional branches
4. Configure persistence
5. Test workflows

Copy-Paste Prompts

Use @langgraph to create stateful agent workflows

Phase 5: Tool Integration

Skills to Invoke

• agent-tool-builder - Tool building
• tool-design - Tool design

Actions

1. Identify tool needs
2. Design tool interfaces
3. Implement tools
4. Add error handling
5. Test tool usage

Copy-Paste Prompts

Use @agent-tool-builder to create agent tools

Phase 6: Memory Systems

Skills to Invoke

• agent-memory-systems - Memory architecture
• conversation-memory - Conversation memory

Actions

1. Design memory structure
2. Implement short-term memory
3. Set up long-term memory
4. Add entity memory
5. Test memory retrieval

Copy-Paste Prompts

Use @agent-memory-systems to implement agent memory

Phase 7: Evaluation

Skills to Invoke

• agent-evaluation - Agent evaluation
• evaluation - AI evaluation

Actions

1. Define evaluation criteria
2. Create test scenarios
3. Measure agent performance
4. Test edge cases
5. Iterate improvements

Copy-Paste Prompts

Use @agent-evaluation to evaluate agent performance

Agent Architecture

User Input -> Planner -> Agent -> Tools -> Memory -> Response
              |          |        |        |
         Decompose   LLM Core  Actions  Short/Long-term

Quality Gates

• Agent logic working
• Tools integrated
• Memory functional
• Orchestration tested
• Evaluation passing

Related Workflow Bundles

• ai-ml - AI/ML development
• rag-implementation - RAG systems
• workflow-automation - Workflow patterns

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.