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ai-agent-development

ai-agent-development

github.com/sickn33Agent workflows48.7k686 installsUpdated today
Repository

About ai-agent-development

A workflow guide for developers building AI agents, covering single agents, multi-agent systems, orchestration, tools, and memory. Its phased index points to related skills and checks so teams can select steps that fit the task.

Key features

  1. 01Phased agent development workflow
  2. 02Single- and multi-agent design
  3. 03Orchestration and tool guidance
  4. 04Short- and long-term memory planning
  5. 05Testing and evaluation checks

Use cases

  1. 01Planning an autonomous agent architecture
  2. 02Building a multi-agent system
  3. 03Adding tools and memory to an agent
  4. 04Defining agent evaluation criteria

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sickn33/agentic-awesome-skills --skill ai-agent-development

After installing, type/ai-agent-developmentin Claude Code to confirm it loaded.

Details

From the project README

AI Agent Development Workflow

Overview

Specialized workflow for building AI agents including single autonomous agents, multi-agent systems, agent orchestration, tool integration, and human-in-the-loop patterns.

When to Use This Workflow

Use this workflow when:

  • Building autonomous AI agents
  • Creating multi-agent systems
  • Implementing agent orchestration
  • Adding tool integration to agents
  • Setting up agent memory

Scope and execution

Choose the phases that match the user’s task; this document is an index, not an instruction to run every listed skill. Read a selected skill before using it. Respect repository and user constraints. Cloud calls, host configuration, publishing, destructive operations and spending require the applicable authorization; a workflow checklist does not grant it.

Multi-agent phases describe the product being built. They do not authorize the assistant to spawn or delegate to agents during this task. Use an existing single-agent architecture when appropriate and record evaluation inputs, observed results and limitations.

Workflow Phases

Phase 1: Agent Design
Skills to Invoke
  • ai-agents-architect - Agent architecture
  • autonomous-agents - Autonomous patterns
Actions
  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.

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