Resolve PR review comments with severity-based prioritization, fix application, and thread replies.
Works with
Fetches inline comments and review bodies from GitHub, classifies by severity (CRITICAL > HIGH > MEDIUM > LOW), and displays a structured summary table before processing
Parses CodeRabbit review sections (outside diff, duplicate, nitpick) and uses embedded \"Prompt for AI Agents\" context to understand issues and suggested fixes
Applies fixes with user confirmation, commits functionall
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongithub-pr-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches github-pr-review from fvadicamo/dev-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate github-pr-review. Access via /github-pr-review in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Resolves Pull Request review comments with severity-based prioritization, fix application, and thread replies.
!gh pr view --json number,title,state,milestone -q '"PR #\(.number): \(.title) (\(.state)) | Milestone: \(.milestone.title // "none")"' 2>/dev/null
REPO=$(gh repo view --json nameWithOwner -q '.nameWithOwner')
PR=$(gh pr view --json number -q '.number')
LAST_PUSH=$(git log -1 --format=%cI HEAD)
# Inline review comments - filter out replies (keep only originals)
gh api repos/$REPO/pulls/$PR/comments?per_page=100 --jq '
[.[] | select(.in_reply_to_id == null) |
{id, path, user: .user.login, created_at, body: .body[0:200]}]
'
# PR-level reviews with non-empty body (CodeRabbit sections, Gemini, etc.)
gh api repos/$REPO/pulls/$PR/reviews?per_page=100 --jq '
[.[] | select(.body | length > 0) |
{id, user: .user.login, state, submitted_at, body: .body[0:500]}]
'
Cross-check review-attached comments: CodeRabbit's review body states "Actionable comments posted: N". If the general pulls/$PR/comments endpoint returns fewer than N new originals from that reviewer, some comments are only available via the review-specific endpoint. Fetch them and merge by comment ID:
# $REVIEW_ID from the reviews fetch above; $EXPECTED from parsing "Actionable comments posted: N"
gh api repos/$REPO/pulls/$PR/reviews/$REVIEW_ID/comments?per_page=100 --jq '
[.[] | select(.in_reply_to_id == null) |
{id, path, user: .user.login, created_at, body: .body[0:200]}]
'
Deduplicate by id before continuing. Comments found only via the review-specific endpoint are valid inline comments and should be treated identically (same classification, same in_reply_to reply mechanism).
Filter new vs already-seen: compare created_at/submitted_at with $LAST_PUSH. Comments posted after the last push are new. Mark older comments as "previous round" in the summary table.
Parse CodeRabbit review bodies: the initial fetch truncates bodies for classification. For reviews from CodeRabbit (user.login starts with coderabbitai), fetch the full body separately:
gh api repos/$REPO/pulls/$PR/reviews?per_page=100 --jq '
[.[] | select(.user.login | startswith("coderabbitai")) |
{id, submitted_at, body}]
'
CodeRabbit posts structured <details> blocks containing outside-diff, duplicate, and nitpick comments. Each block includes file path, line range, severity, and optionally a "Prompt for AI Agents" with pre-built context. See references/coderabbit_parsing.md for full parsing guide.
Use CodeRabbit AI prompts when available: if a comment (or the review body) contains a "Prompt for AI Agents" <details> block, use it to understand the issue and suggested approach. Always read the actual code before proposing a fix. If the review body contains a "Prompt for all review comments with AI agents" block, read it first for cross-comment context before processing individual comments.
Classify all comments by severity and process in order: CRITICAL > HIGH > MEDIUM > LOW.
| Severity | Indicators | Action |
|---|---|---|
| CRITICAL | critical.svg, _🔒 Security_, _🚨 Critical_, _🔴 Critical_, "security", "vulnerability" |
Must fix |
| HIGH | high-priority.svg, _⚠️ Potential issue_, _🐛 Bug_, _⚡ Performance_, _🟠 Major_, "High Severity" |
Should fix |
| MEDIUM | medium-priority.svg, _🛠️ Refactor suggestion_, _💡 Suggestion_, "Medium Severity" |
Recommended |
| LOW | low-priority.svg, _🧹 Nitpick_, _🔧 Optional_, _🟡 Minor_, _🔵 Trivial_, _⚪ Info_, "style", "nit" |
Optional |
When a comment has both a type label and a secondary color badge (e.g., _💡 Suggestion_ | _🟠 Major_), the color badge is the binding severity and overrides the type-based default.
See references/severity_guide.md for full detection patterns (Gemini badges, CodeRabbit emoji, Cursor comments, keyword fallback, related comments heuristics).
Before processing, display a structured overview of all comments:
| # | ID | Severity | File:Line | Type | Status | Summary |
|---|------------|----------|--------------------|----------|----------|--------------------|
| 1 | 123456789 | CRITICAL | src/auth.py:45 | inline | new | SQL injection risk |
| 2 | 987654321 | HIGH | src/db.py:346-350 | outside | new | Missing join cond |
| 3 | 555555555 | HIGH | src/chunk.py:188 | duplicate| previous | Stale metadata |
| 4 | 444444444 | LOW | tests/test_q.py:12 | nitpick | previous | Naming convention |
inline, outside (outside diff), duplicate, minor, nitpick (from CodeRabbit sections), or review (generic PR-level)new (posted after last push) or previous (from earlier rounds)If there are more than 10 comments, suggest saving a review summary to Claude's memory for tracking across sessions. The summary should include: PR number, comment IDs, severity, status (new/addressed/deferred/won't fix), and brief description. This helps maintain continuity when new comments arrive after subsequent pushes.
For each comment, in severity order:
Use git-commit skill format. Functional fixes get separate commits, cosmetic fixes are batched:
| Change type | Strategy |
|---|---|
| Functional (CRITICAL/HIGH) | Separate commit per fix |
| Cosmetic (MEDIUM/LOW) | Single batch style: commit |
Reference the comment ID in the commit body.
Important: use --input - with JSON. The -f in_reply_to=... syntax does NOT work.
COMMIT=$(git rev-parse --short HEAD)
gh api repos/$REPO/pulls/$PR/comments \
--input - <<< '{"body": "Fixed in '"$COMMIT"'. Brief explanation.", "in_reply_to": 123456789}'
Comments embedded in the review body (outside diff, duplicate, nitpick) do not have inline threads. The GitHub API does not support replying to a review body directly. Post a general PR comment referencing the specific issue:
gh pr comment $PR --body "Fixed in $COMMIT. Addresses outside-diff comment on file/path.py:346-350."
Reply templates (no emojis, minimal and professional):
| Situation | Template |
|---|---|
| Fixed | Fixed in [hash]. [brief description of fix] |
| Won't fix | Won't fix: [reason] |
| By design | By design: [explanation] |
| Deferred | Deferred to [issue/task]. Will address in future iteration. |
| Acknowledged | Acknowledged. [brief note] |
Run the project test suite. All tests must pass before pushing. Push all fixes together to minimize review loops.
After addressing all comments, formally submit a review:
gh pr review $PR --approve --body "..." - all comments addressed, PR is readygh pr review $PR --request-changes --body "..." - critical issues remaingh pr review $PR --comment --body "..." - progress update, no decision yetgh pr view $PR --json milestone -q '.milestone.title // "none"'
If the PR has no milestone, check for open milestones:
REPO=$(gh repo view --json nameWithOwner -q '.nameWithOwner')
gh api repos/$REPO/milestones --jq '[.[] | select(.state=="open")] | .[] | "\(.number): \(.title)"'
If open milestones exist, inform the user and suggest assigning:
gh pr edit $PR --milestone "[milestone-title]"
Do not assign automatically. This is a reminder only.
When bots (Gemini, Codex, etc.) review every push:
[skip ci] or [skip review]pulls/$PR/comments) and review bodies (pulls/$PR/reviews)pulls/$PR/reviews/$REVIEW_ID/comments when count mismatchesgh pr review) after addressing all commentsstyle: commitreferences/severity_guide.md - Severity detection patterns (Gemini badges, CodeRabbit emoji, Cursor comments, keyword fallback, related comments heuristics)references/coderabbit_parsing.md - CodeRabbit review body structure, section parsing, "Prompt for AI Agents" usage, duplicate and "also applies to" handlingMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Useful defaults in github-pr-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Solid pick for teams standardizing on skills: github-pr-review is focused, and the summary matches what you get after install.
Registry listing for github-pr-review matched our evaluation — installs cleanly and behaves as described in the markdown.
github-pr-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: github-pr-review is focused, and the summary matches what you get after install.
github-pr-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
github-pr-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added github-pr-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend github-pr-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: github-pr-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
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