openai-agents
Workflow patterns and gotchas for OpenAI Agents SDK. Directs to RAG for implementation.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Workflow patterns and gotchas for OpenAI Agents SDK. Directs to RAG for implementation.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
This skill should be used when the user asks about "visual builder", "no-code agent builder", "drag and drop", "ThinkingConfig", "extended thinking", "chain of thought", "reasoning", or needs guidance on using ADK's visual development tools or configuring advanced reasoning capabilities.
This skill should be used when the user asks about "callbacks", "lifecycle hooks", "before_model_call", "after_tool_call", "plugins", "session state", "state management", "artifacts", "file uploads", "events", "EventActions", "human-in-the-loop", "confirmation", "memory", "MemoryService", "long-term memory", "remember across sessions", "RAG", "retrieval augmented generation", "grounding", "knowledge base", "vector search", or needs guidance on customizing agent behavior, intercepting execution, managing state across turns, implementing approval workflows, or implementing persistent memory or grounding agent responses in external knowledge.
This skill should be used when the user asks about "creating a new ADK project", "initializing ADK", "setting up Google ADK", "adk create command", "ADK project structure", "YAML agent configuration", "creating an agent", "LlmAgent", "BaseAgent", "custom agent", "agent with different model", "Claude with ADK", "OpenAI with ADK", "LiteLLM", "multi-model agent", or needs guidance on bootstrapping an ADK development environment, authentication setup, choosing between Python code and YAML-based agent definitions, agent configuration, model selection, system instructions, or extending the base agent class for non-LLM logic.
This skill should be used when the user asks about "multi-agent systems", "sub-agents", "delegation", "agent routing", "orchestration", "SequentialAgent", "ParallelAgent", "LoopAgent", "agent-to-agent", "A2A protocol", "agent hierarchy", "streaming", "real-time responses", "SSE", "server-sent events", "websocket", "bidirectional", "Live API", "voice", "audio", "video", "multimodal streaming", or needs guidance on building systems with multiple specialized agents working together or implementing real-time communication patterns.
This skill should be used when the user asks about "deploying", "production", "Agent Engine", "Vertex AI", "Cloud Run", "GKE", "Kubernetes", "hosting", "scaling", "guardrails", "safety", "content filtering", "input validation", "output validation", "authentication", "OAuth", "API keys", "credentials", "security plugins", "testing agents", "evaluation", "evals", "benchmarks", "tracing", "Cloud Trace", "logging", "observability", "AgentOps", "LangSmith", "user simulation", or needs guidance on deploying ADK agents to production environments, implementing safety measures, access control, secure authentication, testing, debugging, monitoring, or evaluating ADK agent quality.
This skill should be used when the user asks about "adding a tool", "FunctionTool", "creating tools", "MCP integration", "OpenAPI tools", "built-in tools", "google_search tool", "code_execution tool", "long-running tools", "async tools", "third-party tools", "LangChain tools", "computer use", or needs guidance on extending agent capabilities with custom functions, API integrations, or external tool frameworks.
基于 SOC 职业分类
| name | OpenAI Agents |
| description | Workflow patterns and gotchas for OpenAI Agents SDK. Directs to RAG for implementation. |
| Need | Pattern | RAG Query |
|---|---|---|
| Single specialist | Basic Agent | "Agent basic example" |
| Route to specialists | Handoffs | "agent handoff routing" |
| Sequential steps | Agent chains | "agent chain pattern" |
| Persistent context | Memory | "agent memory conversation" |
| Background processing | Async runner | "async agent runner" |
Query RAG: mcp__agentic-rag__query_sdk("pattern name", sdk="openai", mode="build")
These cause silent failures or confusion:
@tool decorator - Functions without it won't be recognizedgpt-5.2, gpt-5.2-codex (not gpt5 or GPT-5.2)RAG Query: mcp__agentic-rag__query_sdk("openai agents installation", sdk="openai", mode="explain")
RAG Query: mcp__agentic-rag__query_sdk("Agent class definition", sdk="openai", mode="build")
RAG Query: mcp__agentic-rag__query_sdk("@tool decorator example", sdk="openai", mode="build")
Type hints + docstring = working tool.
RAG Query: mcp__agentic-rag__query_sdk("Runner execution", sdk="openai", mode="build")
RAG Query: mcp__agentic-rag__query_sdk("handoff between agents", sdk="openai", mode="build")
RAG Query: mcp__agentic-rag__query_sdk("agent memory persistence", sdk="openai", mode="build")
| Symptom | Likely Cause | RAG Query |
|---|---|---|
| Tool not found | Missing @tool decorator | "tool decorator usage" |
| Schema error | Missing type hints | "tool type annotations" |
| API error | Invalid API key | "openai authentication" |
| Model error | Wrong model name | "supported models" |
| Handoff fails | Bad description | "handoff description" |
Query RAG when you need:
"streaming agent output""parallel tool execution""custom Runner implementation""agent guardrails safety""agent tracing debugging"