Add support for a newly-released LLM model in pydantic-ai (e.g. openai:gpt-5.6, anthropic:claude-sonnet-5). Use when a provider ships a new model id and you need to wire literals, profile flags, and tests to recognize it. Handles SDK-lag, gateway list conventions, and capability probing.
Evaluate and complete an issue or PR where the submitted patch fixes only a narrow symptom of the reported pain point. Use when a contribution may miss adjacent integration surfaces, provider/spec semantics, roundtrip behavior, tests, docs, or historical maintainer decisions.
Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydanticai, or asks to build an AI agent, add tools/capabilities, defer capability loading, stream output, define agents…
Use this skill when the user wants Python data models, Pydantic models, dataclasses, TypedDicts, msgspec structs, or type-safe Python classes generated from OpenAPI, AsyncAPI, JSON Schema, GraphQL, JSON/YAML/CSV sample data, MCP tool schemas, Protocol Buffers, XML Schema, Apache Avro, or existing Python model objects.…
Use this skill first when the user wants to design or plan a dashboard, especially Vizro dashboards. Enforces a 3-step workflow (requirements, layout, visualization) before implementation. Activate when the user asks to create, design, or plan a dashboard. For implementation, use the dashboard-build skill after…
Use this skill when adding cross-filter, cross-highlight, drill-through, or data export interactions to a Vizro dashboard — both for choosing the right interaction pattern during design and for implementing actions in code. Activate when the user wants clicks on a chart or table to filter or highlight other charts…
Use this skill when writing or debugging Vizro YAML dashboard configurations — component syntax, datamanager registration, custom function wiring, filter/parameter setup, or AG Grid tables. Activate when the user is building a Vizro app, encountering YAML or runtime errors, or asking about Vizro component patterns.
Use when adding or updating documentation pages in an existing docs site. Covers matching nearby docs tone and structure, planning navigation and page content, running product services and docs previews, capturing supporting UI screenshots with Chrome DevTools, suppressing Next.js floating dev indicators before…
REQUIRED whenever the user wants to build, create, scaffold, set up, read, inspect, view, change, modify, update, edit, add a step to, fix, run, test, execute, or publish a Tracecat workflow or automation. Read this SKILL.md FIRST, before calling core.workflow.createworkflow, core.workflow.getworkflow…
Use when building or modifying CRUD endpoints with FastCRUD (the fastcrud PyPI package) in a FastAPI project — covers FastCRUD, crudrouter, EndpointCreator, FilterConfig, JoinConfig, auto-relationship detection, the filter operator syntax (gte, in, ilike, etc.), cursor pagination, soft delete, and how to avoid N+1…
A three-stage development method for Python projects built with pydantic-resolve, starting with agreed data models and ending with application interfaces. It uses an ER diagram, which shows database entities and their relationships, plus ORM models that represent database records in code.
Sync the ACP (Agent Client Protocol) schema implementation with the official reference repo by comparing Rust source types against our Python Pydantic models.
Parse RSS 2.0/0.9x, Atom 1.0, RSS 1.0 (RDF) and podcast (itunes:) feeds in Python into typed pydantic v2 models with the rss-parser package. Use this skill whenever a task involves reading, polling, validating or normalizing a feed in Python - building a feed reader or aggregator, ingesting podcast episodes…
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library.
Production-grade FastAPI development with async patterns, Pydantic v2, dependency injection, ML/AI endpoint design, and modern Python best practices for building high-performance REST APIs.
Build and debug Pydantic AI v2 agents using best practices for dependencies, instructions, tools, capabilities, hooks, and structured output validation. Use when the user wants to: (1) Create a new Pydantic AI agent, (2) Debug or fix an existing agent, (3) Add features like tools, validators, capabilities, hooks, or…
Test and evaluate AI agents and LLM outputs using code-first evaluation framework with strong typing. Use when the user wants to: (1) Create evaluation datasets with test cases for AI agents, (2) Define evaluators (deterministic, LLM-as-Judge, custom, or span-based), (3) Run evaluations and generate reports, (4)…