计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
models-and-providers【效率与协作】Guides agents through Pydantic AI model selection, provider configuration, optional extras, native tools, embeddings, wrappers, fallback, concurrency, and provider troubleshooting.
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Use this sub-skill when a task involves choosing a Pydantic AI model string, configuring a provider or profile, installing optional provider dependencies, wrapping models for fallback/concurrency/instrumentation, adding provider-native tools, using embeddings, or diagnosing provider import/auth/settings failures.
provider:model-name strings, direct model/provider/profile classes, ModelSettings, known_model_names(), FallbackModel, ConcurrencyLimitedModel, and InstrumentedModel.pydantic-ai-slim[...] extra and environment-variable checklist.WebSearchTool, CodeExecutionTool, MCPServerTool, FileSearchTool, embedding workflows, or common tools that require extras.../agent-core/ for Agent construction, run modes, dependencies, streaming, message history, and deterministic TestModel/FunctionModel testing.../tools-and-toolsets/ for Python function tools, tool schemas, toolsets, approvals, deferred tools, and ModelRetry design.../outputs-and-messages/ for NativeOutput, structured output support, message parts, multimodal content, and output/history serialization.../mcp-and-integrations/ for MCP client/server lifecycles, provider-adaptive capabilities, Logfire, UI adapters, and durable execution integrations.../cli-and-apps/ for clai, pai, web UI, and command-line model flags.openai:gpt-5.2, anthropic:claude-opus-4-6, google:gemini-3-pro-preview, or openrouter:google/gemini-3-pro-preview when portability is enough.