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/.claude/skills/pre-push-review/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/pre-push-review)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/pre-push-review"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/pre-push-review/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/pre-push-review"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/pre-push-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00024 | $0.00627 |
| Opus 5 | $0.00012 | $0.00313 |
| Sonnet 5 | $0.00005 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
pre-push-review 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 12d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-push Review
Use the strongest locally available reviewer to catch problems while they are still cheap to fix. Run this before the first push and again before every later push to an existing PR.
This is the local counterpart to douwebot: a high-judgment standards review paid for by
the developer's model subscription. It is independent of the automatic CI Review, which
runs on GitHub after CI passes.
Read the review rubric
Read the prompt: of the douwebot review job in .github/workflows/bots.yml. It is the
source of truth for what to look for, how to prioritise concerns, and what makes feedback
useful. Apply its review judgment and comment-quality rules, but ignore its hosted workflow
mechanics: triggers, checkout, model selection, pre-gathered file paths, and GitHub comment
tools.
Read the root AGENTS.md, agent_docs/index.md and its relevant topic guides, plus every
directory-specific AGENTS.md governing a changed file.
Gather local and PR context
First run gh pr view for the current branch.
- If a PR exists, read its title, body, base branch, linked issue, comments and reviews. Review the entire branch diff against that base, not just the latest commit. Use the existing discussion to avoid duplicate findings and to detect concerns that remain unresolved after an iteration.
- If no PR exists, use
mainas the base and review against the task context available locally. Skip only PR metadata that does not exist; scope and readiness are still valid review concerns.
Gather the corresponding local state:
git status --short
git merge-base <base> HEAD
git diff <base>...HEAD --stat
git diff <base>...HEAD -W
git diff HEAD
The last command includes staged and unstaged work that has not reached HEAD. Read a large
diff in chunks, core implementation before tests, and skip generated files (uv.lock,
cassettes).
Return the review locally
Do not post comments, submit a GitHub review, or modify the branch. Return only actionable
findings as text: file:line, the problem, and the concrete fix. Put higher-level concerns
before lower-level ones, following the ordering in the douwebot rubric. Say plainly when
there are no findings.
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.
- 12d ago First seen · 71 lines · 24 tokens per session scan A 6da2a4396cdf
pre-push-review is a skill published in the GitHub repository pydantic/pydantic-ai (19,869 stars, last pushed today), licensed MIT. It adds 24 tokens to every session and 627 once invoked, about $0.0001 per session on Opus 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
parallel-pr-review
Use when asked to "review the open PRs", review a batch or stack of pull requests, or run a recurring PR-review pass on a repo — especially with many PRs, stacked branches, conflicts, or security-sensitive changes. Covers grouping, fan-out to review subagents, verdict synthesis, and posting.
review-readiness
PR readiness dashboard — tracks which reviews have been completed per branch and gates merge decisions. Shows code review, tests, security, QA, and linting status.
agent-swarm-pr
Agent skill for swarm-pr - invoke with $agent-swarm-pr.
agent-pr-manager
Agent skill for pr-manager - invoke with $agent-pr-manager.
agent-github-pr-manager
Agent skill for github-pr-manager - invoke with $agent-github-pr-manager.
github-automation
GitHub workflow automation, PR management, issue tracking, and code review coordination. Integrates with GitHub Actions and repository management. Use when: PR creation, code review, issue management, release automation, workflow setup. Skip when: local-only changes, non-GitHub repositories.