address-pr-comments

Address GitHub pull request review comments using gh CLI. Use when asked to process PR feedback, especially mixed human + AI bot reviews (for example CodeRabbit prompts), triage validity, apply fixes, and prepare concise responses with evidence.

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Install skill "address-pr-comments" with this command: npx skills add sepantajoshani/agents-skills/sepantajoshani-agents-skills-address-pr-comments

Address PR Comments

Overview

Collect PR feedback with gh, classify AI/bot vs human comments, validate each comment against current code, implement only valid changes, and summarize what was addressed vs rejected.

Workflow

Address PR comments in this order:

  1. Resolve target PR.
  2. Verify gh availability/auth.
  3. Collect comments (top-level, reviews, inline).
  4. Classify source (AI/bot vs human).
  5. Validate each comment before changing code.
  6. Apply fixes and run targeted checks.
  7. Commit each resolved comment locally (no push).
  8. Summarize addressed/rejected items with rationale.

1. Resolve PR

If user did not provide a PR number, infer from current branch:

gh pr view --json number,title,url

If that fails, ask the user for PR number or URL.

2. Verify GH CLI

gh --version
gh auth status

If missing or unauthenticated, stop and report the blocker clearly.

3. Collect Feedback

Use the helper script for normalized output:

python3 ./scripts/list_comments.py --pr <number> --json

The script aggregates:

  • Top-level comments
  • Review submissions
  • Inline review comments from unresolved threads by default (including outdated unresolved threads)
  • AI-prompt snippets when present in bot comments

To include resolved inline threads too:

python3 ./scripts/list_comments.py --pr <number> --json --include-resolved

4. Classify + Prioritize

Prioritize:

  1. Human reviewer blocking concerns
  2. High-confidence AI comments with concrete evidence
  3. Lint/style/nit comments

Treat bots as advisory. Do not apply suggestions blindly.

5. Validate Before Fixing

Use the checklist in references/validation-checklist.md.

Mark each comment as one of:

  • valid
  • invalid
  • already_fixed
  • out_of_scope
  • needs_clarification

For AI bot comments containing a "Prompt for AI Agents" block, parse and verify each requested change against current files and repo conventions before editing.

6. Implement + Verify

Apply fixes one validated comment at a time.

Run checks relevant to changed files only, scoped to the modified paths (e.g., type checks, linting, tests).

7. Commit Each Resolved Comment (No Push)

For every valid comment you resolve:

  1. Stage only the files needed for that single comment.
  2. Examine recent commits (git log --oneline -10) to understand the repository's commit message style. If a commit message generation skill is installed, use it. Otherwise, write a concise commit message from the staged diff that follows the same conventions.
  3. Create exactly one local commit for that resolved comment.
  4. Do not push.

Rules:

  • Do not combine multiple resolved comments into one commit unless technically inseparable.
  • If inseparable, note all linked comment URLs in your final report for that commit.
  • Do not commit comments marked invalid, already_fixed, out_of_scope, or needs_clarification.

8. Report Back

Provide:

  • Addressed comments (with file references and commit hashes)
  • Rejected comments (with reason)
  • Any unclear comments that need reviewer clarification
  • What checks were run and results

Quick Commands

# Current branch PR
gh pr view --json number,title,url

# Structured comment dump
python3 ./scripts/list_comments.py --json

# Structured comment dump for a specific PR
python3 ./scripts/list_comments.py --pr <number> --json

# Include resolved inline threads
python3 ./scripts/list_comments.py --pr <number> --json --include-resolved

# Per-comment local commit workflow (no push)
git add <files-for-one-comment>
git diff --staged --stat
git commit -m "<conventional-commit-message>"
# DO NOT: git push

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

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