pydantic-ai: Skill for Claude Code

Adding a provider API feature

Use this when exposing a new provider API capability through Pydantic AI — prompt caching, strict/structured tool calling, thinking/reasoning effort, service tier, safety settings, logprobs, cache breakpoints, and the like. The output is a change that is consistent with how sibling providers already expose the same concept, defaults deliberately, and gates support with a capability flag.

Not for: adding a new model id (that's add-new-model), a bug fix, or a refactor.

The one rule that prevents the most rework

Before designing anything, find the existing cross-provider abstraction that governs this capability and let its shape decide the API. Most "how should I expose this?" questions are already answered by an abstraction the codebase has — reaching for a new provider-specific knob when one exists is the single most common thing maintainers reject. When a feature routes through an existing abstraction, the abstraction's shape pre-decides the API surface, the opt-out, and often the default.

The tell that you skipped this: you find yourself listing 2-3 "options" for how a user controls the feature. If one of those options duplicates an existing cross-provider control, it isn't a real option — the existing abstraction wins.

Step 0 — Enumerate sibling precedent

For the capability you're adding, list how every provider that already has an analog exposes it, and name the governing existing abstraction. It is one of:

• a per-tool flag — ToolDefinition.strict: bool | None (tools.py), resolved in models/__init__.py::_customize_tool_def;
• a shared ModelSettings field — thinking, service_tier (settings.py), each with per-model resolvers mapping to native concepts;
• a provider-prefixed {Provider}ModelSettings field — anthropic_cache, openai_prompt_cache_key, groq_reasoning_effort;
• a message-stream marker — CachePoint in UserPromptPart.content (messages.py);
• a ModelProfile capability flag — openai_supports_strict_tool_definition, bedrock_supports_prompt_caching.