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ai-agent-development

Skill2 starsChanged 42 days agoMITView on GitHub

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

2 other projects use a version of this, 1 of them edited.

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The file

We split this skill into the rules it gives an agent

4 rules. About 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this workflow when:

    line 14
  2. Distinctive:

    Use this skill only when the task clearly matches the scope described above.

    line 158
  3. Distinctive:

    Do not treat the output as a substitute for environment-specific validation, testing, or expert review.

    line 159
  4. Distinctive:

    Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

    line 160

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

Workflow Phases

Phase 1: Agent Design

Skills to Invoke

  • ai-agents-architect - Agent architecture

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

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

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

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

Actions

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

Copy-Paste Prompts

Phase 6: Memory Systems

Skills to Invoke

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

Phase 7: Evaluation

Skills to Invoke

  • agent-evaluation - Agent 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

  • rag-implementation - RAG systems

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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