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Found 81 Skills
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants. Use when defining API request/response schemas, database models, or data validation in Python applications using Pydantic v2.
Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation in FastAPI, Django, and configuration management.
Avoid common mistakes and debug issues in PydanticAI agents. Use when encountering errors, unexpected behavior, or when reviewing agent implementations.
Create PydanticAI agents with type-safe dependencies, structured outputs, and proper configuration. Use when building AI agents, creating chat systems, or integrating LLMs with Pydantic validation.
Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
Comprehensive Pydantic data validation skill for customer support tech enablement - covering BaseModel, Field validation, custom validators, FastAPI integration, BaseSettings, serialization, and Pydantic V2 features
This skill should be used when the user asks to "validate data with pydantic", "create a pydantic model", "use pydantic best practices", "write pydantic validators", or needs guidance on pydantic v2 patterns, serialization, configuration, or performance optimization.
Implement dependency injection in PydanticAI agents using RunContext and deps_type. Use when agents need database connections, API clients, user context, or any external resources.
Test PydanticAI agents using TestModel, FunctionModel, VCR cassettes, and inline snapshots. Use when writing unit tests, mocking LLM responses, or recording API interactions.
Generate DBeaver config from Pydantic ClickHouse models. TRIGGERS - DBeaver config, ClickHouse connection, database client config.
Pydantic models and validation. Use when: (1) Defining schemas, (2) Validating input/output, (3) Generating JSON schema.