Ai Agent Development
Follow a phased workflow to design, implement, orchestrate, and tool-enable autonomous or multi-agent systems with CrewAI, LangGraph, or custom stacks.
Overview
ai-agent-development is an agent skill most often used in Build agent-tooling (also Validate scope and Ship testing) that guides autonomous and multi-agent implementation with orchestration and tool integration.
Install
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill ai-agent-developmentWhat is this skill?
- Multi-phase workflow: Agent Design → Single Agent → multi-agent paths in one bundle
- Explicit invoke hooks for ai-agents-architect and autonomous-agent-patterns skills
- Covers tool integration, memory design, and human-in-the-loop placement
- Copy-paste @skill prompts to chain architecture into implementation
- Success metrics and capability planning before writing orchestration code
- Workflow bundle organized in numbered phases starting with Agent Design and Single Agent Implementation
Adoption & trust: 466 installs on skills.sh; 40.1k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You want an autonomous agent but lack an ordered path from architecture to tools, memory, and testable behavior.
Who is it for?
Indie builders creating tool-using agents, multi-agent workflows, or LangGraph/CrewAI orchestration who want a skill-chained playbook.
Skip if: Simple one-shot LLM prompts with no tools, or teams that only need a static RAG chat widget without agent autonomy.
When should I use this skill?
Building autonomous AI agents, multi-agent systems, agent orchestration, tool integration, or agent memory setups.
What do I get? / Deliverables
You complete phased agent design and implementation with invoked architect/pattern skills and a framework-backed agent ready for further testing hardening.
- Agent architecture and capability plan
- Implemented agent or multi-agent scaffold with tools and memory configuration
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Agent construction is core Build agent-tooling work, though the bundled workflow spans design choices that start in validation and testing that continues into Ship. Framework selection, tool wiring, memory, and orchestration are agent-tooling concerns—not generic frontend or DevOps unless you extend the workflow.
Where it fits
Define agent purpose, success metrics, and tool boundaries before committing to a framework.
Implement single-agent logic with tools and memory using autonomous-agent-patterns.
Wire external APIs as agent tools after architecture from ai-agents-architect.
Exercise agent behavior and orchestration paths before exposing automation to users.
How it compares
A workflow skill package that chains design and implementation steps—not a hosted agent runtime or single MCP connector.
Common Questions / FAQ
Who is ai-agent-development for?
Solo developers and small teams building autonomous or multi-agent products who want phased guidance and explicit handoffs to architecture and pattern skills.
When should I use ai-agent-development?
When starting agent architecture after MVP scope is clear, during Build agent-tooling implementation, and when extending to orchestration; also when validating agent capabilities before wider launch.
Is ai-agent-development safe to install?
It is documentation-forward workflow guidance; any live agent will need API keys and tool permissions you control—review the Security Audits panel on this page.
SKILL.md
READMESKILL.md - Ai Agent Development
# 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 - `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,