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/address-feedback/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/address-feedback)<a href="https://agentmods.dev/skills/pydantic/pydantic-ai/address-feedback"><img src="https://agentmods.dev/badge/skills/pydantic/pydantic-ai/address-feedback.svg" alt="Measured on agentmods" 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 | $0.00029 | $0.00967 |
| Opus 5 | $0.00015 | $0.00483 |
| Sonnet 5 | $0.00006 | $0.00193 |
| Haiku 4.5 | $0.00003 | $0.00097 |
Grade A, and why
address-feedback 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 4d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Address PR Review Feedback
Find and address all review comments on the PR for the current branch. For each comment:
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Gather context: Use
ghto find the PR number from the current branch, then fetch all unresolved review comments (both PR-level and inline review comments viagh api repos/{owner}/{repo}/pulls/{number}/comments). Skip already-resolved and outdated threads. Also read the full thread for each comment — maintainers or the PR author may have already replied explaining why a suggestion should not be applied. -
Triage each comment: A review comment — bot or human — is evidence to weigh, never an acceptance criterion. This PR's acceptance criteria are its linked issue, the repository instructions, and settled maintainer decisions. Sort each comment into fix, decline, escalate or file; one that clears the gates below is a fix.
- Read the thread first. If a maintainer has already weighed in, that settles it; your own earlier reply does not.
- If the finding claims a defect, reproduce it before you write the fix. Run a script, a failing test, or a snippet. A plausible reading is not a reproduction, and a severity label is the reviewer's guess. If it doesn't reproduce, reply with what you ran and why that run covers the path the finding names, then react 👎. A repro you could not run — no credentials, no worker, no cassette — is not a refutation: ask the user driving you instead. A finding about docs, tests, naming, or API shape has nothing to reproduce; weigh it against the repository instructions.
- List the production files the fix would touch. A file outside the ones this PR already changes for its issue — a shared core module especially (
pydantic_ai/_agent_graph.py,pydantic_ai/_run_context.py,pydantic_ai/messages.py,pydantic_ai/tools.py, a base class, a serialized dataclass) — means the finding has outgrown the PR's stated scope. Implement it here only on the rootAGENTS.mdbar for the case at hand — a sibling field, provider or model needs the same defect reproduced there; a shared protocol, helper or abstraction needs the narrow fix to be unavailable, or the refactor to be itself the confirmed fix — and only when including it neither explodes scope nor delays an already mergeable PR. Otherwise escalate it perpushing-commits-to-the-repo("Escalate real trade-offs, don't guess") when it needs a maintainer decision, or file it as its own issue when it is real but belongs elsewhere. Tests and documentation this change already owes are in scope, not an expansion. - Name who else moves. If the users whose observable behavior changes are a wider set than the users who hit the reported bug, the finding breaks the root
AGENTS.mdrequirement to "leave behavior unchanged for users who aren't hitting the problem you are solving". Re-scope or escalate; never implement and document. - Ask the user driving you when the call is a preference they may hold. A finding can be wrong on the merits wherever it sits: decline that one with code evidence, per
pushing-commits-to-the-repo("Invalid"), rather than filing it.
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Fix the code: Make the changes for each comment triage sent to fix.
-
Continue the PR loop: Follow
pushing-commits-to-the-repofrom itsBefore you pushsection. -
Use the canonical close-out: Apply every required reply, reaction, and resolution step from that workflow. For each completed comment, explain what changed or why no change was needed. Then resolve the thread via GraphQL
resolveReviewThread. Leave threads open only when a decision or another person's response is pending.
Always read the relevant code before making changes.
Important: Automated reviewers surface real issues and are never skipped — every finding gets a reply and a reaction. But a bot cannot approve a scope expansion, and its severity label carries no authority. A HIGH on a defect that does not reproduce is a 👎. Refuting one leaves CI Review's REQUEST_CHANGES standing; a later push re-runs the review, and the verdict clears only if that run stops finding the HIGH. Say so when you hand the PR back.
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.
- 4d ago Changed · +2 lines b420d131bdca
- 9d ago First seen · 27 lines · 29 tokens per session scan A 824a4f373d93
address-feedback is a skill published in the GitHub repository pydantic/pydantic-ai (19,780 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 967 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.
GitHub PR Workflow
Move cleanly through branch, diff, review, validation, and PR update steps without losing scope or repository-native workflow.
cflx-workflow
Legacy compatibility router for Conflux apply, rejecting, cleanup-review, accept, and archive operations. New orchestrator prompts use dedicated cflx- operation skills. CRITICAL - This skill CANNOT ask questions or request user input.
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.