soojung-kang/ai-agent-development
skills/ai-agent-development/SKILL.md
AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
development
Updated Apr 15, 2026
$ install --global
skillsauthnpx skillsauth add soojung-kang/antigravity-awesome-skills ai-agent-developmentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Mar 31, 2026, 1:52 AM57.3s1 file scanned
SKILL.md
- name:
- ai-agent-development
- description:
- AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
- category:
- granular-workflow-bundle
- risk:
- safe
- source:
- personal
- date_added:
- 2026-02-27
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 architectureautonomous-agents- Autonomous patterns
Actions
- Define agent purpose
- Design agent capabilities
- Plan tool integration
- Design memory system
- 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 patternsautonomous-agents- Autonomous agents
Actions
- Choose agent framework
- Implement agent logic
- Add tool integration
- Configure memory
- Test agent behavior
Copy-Paste Prompts
Use @autonomous-agent-patterns to implement single agent
Phase 3: Multi-Agent System
Skills to Invoke
crewai- CrewAI frameworkmulti-agent-patterns- Multi-agent patterns
Actions
- Define agent roles
- Set up agent communication
- Configure orchestration
- Implement task delegation
- Test coordination
Copy-Paste Prompts
Use @crewai to build multi-agent system with roles
Phase 4: Agent Orchestration
Skills to Invoke
langgraph- LangGraph orchestrationworkflow-orchestration-patterns- Orchestration
Actions
- Design workflow graph
- Implement state management
- Add conditional branches
- Configure persistence
- Test workflows
Copy-Paste Prompts
Use @langgraph to create stateful agent workflows
Phase 5: Tool Integration
Skills to Invoke
agent-tool-builder- Tool buildingtool-design- Tool design
Actions
- Identify tool needs
- Design tool interfaces
- Implement tools
- Add error handling
- 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 architectureconversation-memory- Conversation memory
Actions
- Design memory structure
- Implement short-term memory
- Set up long-term memory
- Add entity memory
- Test memory retrieval
Copy-Paste Prompts
Use @agent-memory-systems to implement agent memory
Phase 7: Evaluation
Skills to Invoke
agent-evaluation- Agent evaluationevaluation- AI evaluation
Actions
- Define evaluation criteria
- Create test scenarios
- Measure agent performance
- Test edge cases
- 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 developmentrag-implementation- RAG systemsworkflow-automation- Workflow patterns
Related Skills
soojung-kang/loki-mode
development
VerifiedTrustedCommunity
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations,...
SKILL.mdUpdated Apr 26, 2026
soojung-kang/logistics-exception-management
tools
VerifiedTrustedCommunity
Codified expertise for handling freight exceptions, shipment delays, damages, losses, and carrier disputes. Informed by logistics professionals with 15+ years operational experience.
SKILL.mdUpdated Apr 25, 2026
soojung-kang/local-legal-seo-audit
testing
VerifiedTrustedCommunity
Audit and improve local SEO for law firms, attorneys, forensic experts and legal/professional services sites with local presence, focusing on GBP, directories, E-E-A-T and practice/location pages.
SKILL.mdUpdated Apr 25, 2026
soojung-kang/llm-prompt-optimizer
testing
VerifiedTrustedCommunity
Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
SKILL.mdUpdated Apr 25, 2026