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.
Borrowing it
Nothing to install: this file belongs to pydantic/pydantic-ai. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pydantic/pydantic-ai/main/.agents/skills/complete-partial-pr/SKILL.mdgit clone --depth 1 https://github.com/pydantic/pydantic-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/pydantic/pydantic-ai/complete-partial-pr)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/complete-partial-pr"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/complete-partial-pr/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/complete-partial-pr"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/complete-partial-pr.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low Excessive Agency · line 12 Skill's behavior or capabilities extend beyond its stated purpose. Scope creep allows an agent to perform actions unrelated to its documented functionality, increasing the attack surface.Fix: Limit the skill's scope to its documented purpose. Remove instructions that enable the agent to perform actions outside its stated functionality.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.02657 |
| Opus 5.5 | $0.00022 | $0.01063 |
| Sonnet 5.5 | $0.00011 | $0.00531 |
| Haiku 4.5 | $0.00006 | $0.00266 |
Grade A, and why
complete-partial-pr scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Complete Partial PR
Use this when a PR or issue patch fixes a small visible failure but may not address the full integration contract. The goal is to turn a narrow contribution into a maintainable Pydantic AI change, or to explain precisely why it should stay narrow.
This is not limited to UI or provider integrations. Apply it to any patch that touches one variant of a broader surface: parse/dump, request/response, streaming/non-streaming, static/dynamic, sync/async, native/provider/local tools, metadata, state machines, durable execution, docs, or tests.
When To Use
- A contributor's PR addresses the immediate error but not the rest of the user's workflow.
- A fix accepts one shape of data but may not preserve roundtrip semantics.
- A provider or external protocol has more states, fields, or variants than the PR covers.
- The user asks "is this enough?", "what did the contributor miss?", "verify this against the spec", or "improve this branch".
- A reviewer suspects the patch contradicts historical decisions or creates future integration debt.
Do not use this as a replacement for /review-branch when the task is only a general code review. If the PR has no local context yet, run /adopt-pr first.
Operating Principle
Separate three things before implementing:
- The contributor's exact patch.
- The underlying user pain point.
- The integration contract Pydantic AI should support.
Only (3) determines the final shape. The submitted patch is evidence, not the boundary.
Delegation
Use subagents for independent research and review lanes. Keep the critical path local: branch selection, final synthesis, implementation, and push decisions.
Good subagent lanes:
- Spec researcher: read the linked issue/PR, provider docs, SDK types, protocol docs, and relevant project docs. Return source-backed facts only, with URLs or file paths.
- Codepath mapper: map affected code and adjacent surfaces: loaders, dumpers, stream handlers, request builders, response parsers, tool/native-tool paths, model profiles, docs, and tests.
- History researcher: inspect
git blame,git log, previous PRs, review comments, and decision logs around the touched code. Return historical decisions with source links. - Test-shape reviewer: decide what coverage proves the full contract without bloating tests. Identify where parametrization, snapshots, VCR, or direct adapter tests are appropriate.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago Changed · +36 lines 1aa131d66553
- 1mo ago First seen · 202 lines · 55 tokens per session scan A 5b65b6a32299
complete-partial-pr is a skill published in the GitHub repository pydantic/pydantic-ai (20,800 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 2,657 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
verification-strategy
Thorough verification of completed work before declaring done.
test-generator
Generate pytest test cases for Python functions and classes.
test-writer
Generate comprehensive test suites for existing code.
pydantic-evals
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)…
browser-use
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers hosted cloud browsers for AI agents: the browser-use Python library (Browser(usecloud=True)), Playwright, Puppeteer or any CDP client.
python-testing
Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization. Use when writing or improving Python tests.