Official agent skill

Address Feedback

by pydantic in pydantic/pydantic-ai

Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.

OfficialMITAuto-check passedDevelopment

Install Address Feedback

skills CLI
$ npx skills add pydantic/pydantic-ai --skill address-feedback -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install pydantic/pydantic-ai address-feedback --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/pydantic/pydantic-ai.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/address-feedback .claude/skills/address-feedback && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
address-feedback
GitHub stars
20k
Token cost
~1.1k tokens
SKILL.md length
671 words
Files
1
Skills in repo
20
Repo updated
First seen
Licence
MIT

At a glance

Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.

  • Works in 5 steps: Gather context: Use gh to find the PR… → Triage each comment: A review comment —… → Fix the code: Make the changes for each… → …
  • Tasks that involve Pull requests
  • Calls gh

What it does

Address Feedback is an agent skill from pydantic/pydantic-ai, published by the product's own GitHub organization. Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Pull requests. It works with Pydantic AI. The repository describes itself as: How Python does AI. Agents, realtime voice, image generation, embeddings. Every model, every interface, typed end to end. The licence is MIT.

When your agent uses it

  • Tasks that involve Pull requests

Example prompts

  • “/address-feedback”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Gather context: Use gh to find the PR number from the current branch, then fetch all unresolved review comments (both PR-level and inline…
  2. Triage each comment: A review comment — bot or human — is evidence to weigh, never an acceptance criterion. This PR's acceptance criteria…
  3. Fix the code: Make the changes for each comment triage sent to fix.
  4. Continue the PR loop: Follow pushing-commits-to-the-repo from its Before you push section.
  5. Use the canonical close-out: Apply every required reply, reaction, and resolution step from that workflow. For each completed comment…

What it can do on your machine

Read from SKILL.md and the folder at commit f55bb8a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Address Feedback loads about 1.1k tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 671 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~41
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from pydantic/pydantic-ai at commit f55bb8a, republished under its MIT licence (© pydantic). 671 words, ~1,141 tokens.

Download SKILL.mdSave it as .claude/skills/address-feedback/SKILL.md (or your agent's skills folder).
name
address-feedback
description
Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.

Address PR Review Feedback

Find and address all review comments on the PR for the current branch. For each comment:

  1. Gather context: Use gh to find the PR number from the current branch, then fetch all unresolved review comments (both PR-level and inline review comments via gh 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.

  2. 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 root AGENTS.md bar 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 per pushing-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.md requirement to "leave behavior unchanged for users who aren't hitting the problem you are solving". Re-scope or escalate; never implement and document.
    • TestModel values. pydantic_ai_slim/pydantic_ai/models/AGENTS.md exempts changes to the tool-call arguments and text TestModel generates from both bars above, on the conditions it sets.
    • 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.
  3. Fix the code: Make the changes for each comment triage sent to fix.

  4. Continue the PR loop: Follow pushing-commits-to-the-repo from its Before you push section.

  5. 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.

Show full SKILL.md (85 more words)Show less

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.

© pydantic, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/address-feedback of pydantic/pydantic-ai.

Open the folder on GitHubat commit f55bb8a

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Address Feedback next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Understand Diff AnalysisEgonex-AI/Understand-Anything86k1 repos~1.4kAutomated safety check: PassMIT
PR Design DocOpenHands/OpenHands90k—~2.4kAutomated safety check: PassMIT

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Works with

Categories

Questions about Address Feedback

What does Address Feedback do?

Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow. Address Feedback is an agent skill from pydantic/pydantic-ai, published by the product's own GitHub organization. Find and address unresolved PR review comments for the current branch, then continue the canonical push, reply, reaction, and resolution workflow.

When should I use Address Feedback?

Address Feedback fits situations like: tasks that involve Pull requests.

How do I install Address Feedback in Claude Code?

Run `npx skills add pydantic/pydantic-ai --skill address-feedback -a claude-code`. Or copy the skill folder (.claude/skills/address-feedback in pydantic/pydantic-ai) into .claude/skills/address-feedback in your project. Claude Code loads it when a task matches its description.

How do I install Address Feedback in Codex?

Run `npx skills add pydantic/pydantic-ai --skill address-feedback -a codex`. Or copy the skill folder (.claude/skills/address-feedback in pydantic/pydantic-ai) into .agents/skills/address-feedback in your project. Codex loads it when a task matches its description.

Can I use Address Feedback in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add pydantic/pydantic-ai --skill address-feedback -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/address-feedback, .gemini/skills/address-feedback, .github/skills/address-feedback and .opencode/skills/address-feedback in your project.

What does Address Feedback need to run?

Going by SKILL.md and its folder, Address Feedback needs the command-line tools its instructions call (gh).

Does Address Feedback access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Address Feedback safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Address Feedback use?

Address Feedback is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Address Feedback use?

About 1.1k tokens (SKILL.md is roughly 4.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Address Feedback?

Skills that share tags, products or a category with Address Feedback: Finishing a Development Branch (obra/superpowers, 296k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars), Check PR (onyx-dot-app/onyx, 32k stars) and Understand Diff Analysis (Egonex-AI/Understand-Anything, 86k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Address Feedback?

pydantic (a GitHub organization, an official publisher) maintains it in pydantic/pydantic-ai, which has 20,476 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on October 8, 2026.

Source: pydantic/pydantic-ai on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.