How Python does AI. Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end.
Pydantic AI is a typed Python SDK for building AI agents that can use different language models and interfaces, including voice, image generation, and embeddings. Developers use it for applications ranging from structured data extraction to long-running multi-agent work. The catalogue entries provide workflows and configuration for its coding-agent and harness features.
▶ Pydantic AI 2.0: The New Best Way to Build AI Agents is Composing Capabilities Cole Medin · about pydantic/pydantic-ai · on YouTube → Skill Claude CodeCodex
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.
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Add a new provider API capability (prompt caching, strict/structured tool calling, thinking/reasoning effort, service tier, safety settings, logprobs, etc.) to Pydantic AI. Use when wiring a provider feature through the library — it enforces reasoning from the existing cross-provider abstraction before designing…
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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.
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Shape every response for an ADHD reader who reviews the work but does not do the coding — the agent does. Use whenever responding to ANY message — coding, debugging, planning, casual. Lead with the result or the decision. Surface anything needing the user's input as a structured question, never buried in prose they'll…
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Independent power-user audit of a big new-feature PR. Research the feature domain from external sources before reading any implementation code, design the ideal test suite from a power-user's perspective, then gap-compare it against the PR to produce evidence-backed, precedent-linked review items. Use when a large…
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Open and advance a PR — write a current title and body, label it, review before every push, watch CI, and triage every comment. Use whenever you open a PR or push a commit to one.
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Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.
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Run a high-judgment local review of the current branch before pushing, both before a PR exists and between PR iterations.
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Record, rewrite, and debug VCR cassettes for HTTP recordings. Use when running tests with --record-mode, verifying cassette playback, or inspecting request/response bodies in YAML cassettes.
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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…
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Migrate Python LangChain or LangGraph applications to Pydantic AI. Use for LangChain agents, chains, LCEL, or direct LangGraph graphs, persistence, interrupts, and streaming. Do not use for migrations centered on createdeepagent or Deep Agents harness features.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: