Agent skill

address-feedback

Stars 16,282
Forks 1,909

Install this agent skill to your Project

npx add-skill https://github.com/pydantic/pydantic-ai/tree/main/.claude/skills/address-feedback

SKILL.md

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 Skip already-resolved and outdated threads.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:

    • If it's clear how to address (implement the suggestion, or decide it shouldn't be done with a clear reason): fix it.
    • If a maintainer or PR author has already weighed in on the thread (e.g. explaining why a suggestion doesn't apply), respect that guidance.
    • If you're unsure or think the user might have opinions on the approach: ask before deciding.
  3. Fix the code: Make the necessary changes to address each comment.

  4. Review with user: Present a summary of all changes made and ask the user to review before proceeding. Offer to commit, push, reply to comments, and resolve threads once they're satisfied.

  5. Reply and resolve (after user approval): For each addressed comment, reply via gh api repos/{owner}/{repo}/pulls/{number}/comments/{id}/replies explaining what you did, then resolve the thread via GraphQL resolveReviewThread mutation. To find thread IDs, query repository.pullRequest.reviewThreads via GraphQL.

Always read the relevant code before making changes.

Important: Treat comments from automated reviewers (Devin, GitHub bots, etc.) with the same weight as human comments. Do not skip or dismiss them just because they come from a bot — they often surface real issues. Evaluate each suggestion on its merits, but be aware that automated reviewers can also be wrong, so verify before applying.

Expand your agent's capabilities with these related and highly-rated skills.

pydantic/pydantic-ai

pre-push-review

Review the current branch against main, simulating the automated CI review from the bots workflow

16,282 1,909
Explore
petekp/claude-code-setup

ubiquitous-language

Extract a DDD-style ubiquitous language glossary from the current conversation, flagging ambiguities and proposing canonical terms. Saves to UBIQUITOUS_LANGUAGE.md. Use when user wants to define domain terms, build a glossary, harden terminology, create a ubiquitous language, or mentions "domain model" or "DDD".

20 6
Explore
petekp/claude-code-setup

every-style-editor

This skill should be used when reviewing or editing copy to ensure adherence to Every's style guide. It provides a systematic line-by-line review process for grammar, punctuation, mechanics, and style guide compliance.

20 6
Explore
petekp/claude-code-setup

manage-codex

Autonomous Codex batch orchestrator. Use for "/manage-codex", "manage codex", "use codex", "dispatch to codex", or long-running Codex work.

20 6
Explore
petekp/claude-code-setup

seo-audit

When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema-markup. For AI search optimization, see ai-seo.

20 6
Explore
petekp/claude-code-setup

capture-learning

Analyze recent conversation context and capture learnings to project knowledge files (for project-specific insights) or skills/commands/subagents (for cross-project patterns). Use when the user asks to "capture this learning", "update the docs with this", "remember this for next time", "document this issue", "add this to CLAUDE.md", "save this knowledge", or "update project knowledge". Also triggers after resolving build/setup issues, discovering non-obvious patterns, or completing debugging sessions with valuable insights.

20 6
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results