AI Agent Development Workflow workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
AI Agent Development Workflow workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
This public intake copy packages plugins/antigravity-awesome-skills/skills/ai-agent-development from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.
Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.
This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.
AI Agent Development Workflow
Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Agent Architecture, Quality Gates, Limitations.
When to Use This Skill
Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.
Building autonomous AI agents
Creating multi-agent systems
Implementing agent orchestration
Adding tool integration to agents
Setting up agent memory
Use when the request clearly matches the imported source intent: AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.
Operating Table
Situation
Start here
Why it matters
First-time use
metadata.json
Confirms repository, branch, commit, and imported path through the external_source block before touching the copied workflow
Provenance review
ORIGIN.md
Gives reviewers a plain-language audit trail for the imported source
Workflow execution
SKILL.md
Starts with the smallest copied file that materially changes execution
Supporting context
SKILL.md
Adds the next most relevant copied source file without loading the entire package
Handoff decision
## Related Skills
Helps the operator switch to a stronger native skill when the task drifts
Workflow
This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.
ai-agents-architect - Agent architecture
autonomous-agents - Autonomous patterns
Define agent purpose
Design agent capabilities
Plan tool integration
Design memory system
Define success metrics
Imported Workflow Notes
Imported: Workflow Phases
Phase 1: Agent Design
Skills to Invoke
ai-agents-architect - Agent architecture
autonomous-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 patterns
autonomous-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 framework
multi-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 orchestration
workflow-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 building
tool-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 architecture
conversation-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 evaluation
evaluation - 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
Imported: Related Workflow Bundles
ai-ml - AI/ML development
rag-implementation - RAG systems
workflow-automation - Workflow patterns
Imported: Overview
Specialized workflow for building AI agents including single autonomous agents, multi-agent systems, agent orchestration, tool integration, and human-in-the-loop patterns.
Use @ai-agent-development-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.
Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.
Example 2: Ask for a provenance-grounded review
Review @ai-agent-development-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.
Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.
Example 3: Narrow the copied support files before execution
Use @ai-agent-development-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.
Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.
Example 4: Build a reviewer packet
Review @ai-agent-development-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.
Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.
Best Practices
Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.
Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
Treat generated examples as scaffolding; adapt them to the concrete task before execution.
Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.
Troubleshooting
Problem: The operator skipped the imported context and answered too generically
Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/ai-agent-development, fails to mention provenance, or does not use any copied source files at all.
Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.
Problem: The imported workflow feels incomplete during review
Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task.
Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.
Problem: The task drifted into a different specialization
Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better.
Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.
Related Skills
@00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
@00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
@10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
Additional Resources
Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.
Resource family
What it gives the reviewer
Example path
references
copied reference notes, guides, or background material from upstream
references/n/a
examples
worked examples or reusable prompts copied from upstream
examples/n/a
scripts
upstream helper scripts that change execution or validation
scripts/n/a
agents
routing or delegation notes that are genuinely part of the imported package
agents/n/a
assets
supporting assets or schemas copied from the source package
assets/n/a
Imported Reference Notes
Imported: Quality Gates
Agent logic working
Tools integrated
Memory functional
Orchestration tested
Evaluation passing
Imported: 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.
