vllm-omni-review-miner

name	vllm-omni-review-miner
description	Scan pull requests of a specific type (e.g., new model support) from vLLM and vLLM-Omni repos, extract code review patterns and suggestions, then generate a specialized review automation skill. Use when learning from historical pull request reviews, building domain-specific code review expertise, or automating review pattern extraction.

vLLM Review Pattern Miner

Overview

This skill mines PR review comments from vllm-project/vllm and vllm-project/vllm-omni to extract recurring review patterns, common issues, and best practices for a specific PR category. It then generates a new specialized review skill under .claude/skills/.

Usage

Invoke this skill with a PR category. Examples:

• /vllm-omni-review-miner new model support — mine reviews on PRs that add new model architectures
• /vllm-omni-review-miner bugfix — mine reviews on bug fix PRs
• /vllm-omni-review-miner performance — mine reviews on performance optimization PRs
• /vllm-omni-review-miner diffusion — mine reviews on diffusion/image generation PRs

Core Workflow

Step 1: Define Search Scope

Map the user-provided category to search queries. Common mappings:

• new model: label:new-model or title contains [Model], add support for
• bugfix: label:bug or title contains [Bugfix], fix
• performance: label:performance or title contains [Perf], optimize
• diffusion: title contains [Image], diffusion, DiT
• tts/audio: title contains [TTS], [Audio], speech
• quantization: title contains [Quantization], quant, awq, gptq, fp8
• distributed: title contains [Distributed], tensor parallel

For unrecognized categories, construct a best-effort search query from the user's keywords.

Step 2: Fetch PRs from Both Repos

Use gh CLI to search for merged PRs in both repos. Prioritize PRs with substantive reviews (multiple review comments).

# Search vllm-project/vllm for merged PRs matching the category
gh pr list --repo vllm-project/vllm \
  --state merged \
  --search "<search_query>" \
  --limit 30 \
  --json number,title,url,reviewDecision,comments

# Search vllm-project/vllm-omni for merged PRs matching the category
gh pr list --repo vllm-project/vllm-omni \
  --state merged \
  --search "<search_query>" \
  --limit 30 \
  --json number,title,url,reviewDecision,comments

Filter to keep only PRs with review comments (skip auto-merged or rubber-stamped PRs). Aim for 10-20 high-quality reviewed PRs total across both repos.

Validation checkpoint: Verify at least 5 PRs with review comments were found before proceeding. If fewer than 5, broaden the search query or warn the user that the generated skill may lack coverage.

Step 3: Extract Review Comments

For each selected PR, fetch all review comments:

# Fetch review comments (inline code review comments)
gh api repos/<owner>/<repo>/pulls/<pr_number>/comments \
  --jq '.[] | {body, path, diff_hunk, created_at, user: .user.login}'

# Fetch review-level comments (top-level review body)
gh api repos/<owner>/<repo>/pulls/<pr_number>/reviews \
  --jq '.[] | {body, state, user: .user.login}'

# Fetch issue-style comments (discussion)
gh api repos/<owner>/<repo>/issues/<pr_number>/comments \
  --jq '.[] | {body, user: .user.login}'

Step 4: Analyze and Categorize Patterns

Process all collected review comments and classify them into:

4.1 Recurring Issues (things reviewers flag repeatedly)

Look for patterns such as:

• Missing tests or insufficient test coverage
• Missing error handling or validation
• Performance concerns (memory, latency, batch size)
• API compatibility issues
• Documentation gaps
• Code style or architecture violations
• Security concerns
• Race conditions or concurrency issues

4.2 Common Suggestions (advice reviewers give)

Extract actionable recommendations:

• "You should add a test for..."
• "Consider using X instead of Y..."
• "This needs to handle the case where..."
• "Please add type annotations for..."

4.3 Approval Criteria (what makes a PR approvable)

Identify what reviewers look for before approving:

• Required tests present and passing
• Benchmark results provided
• Documentation updated
• No breaking changes (or migration path provided)
• Code follows existing patterns

4.4 Domain-Specific Knowledge

Extract technical knowledge unique to this PR category:

• Architecture constraints
• Required integration points
• Common pitfalls
• Performance expectations

Validation checkpoint: Review extracted patterns with the user before generating the skill. Confirm the top 5 patterns are accurate and relevant to the category.

Step 5: Generate the Specialized Review Skill

Create a new skill directory and files:

.claude/skills/vllm-omni-review-<category>/
├── SKILL.md                    # Main skill with review checklist
└── references/
    ├── review-checklist.md     # Detailed checklist derived from patterns
    ├── common-issues.md        # Recurring issues with examples
    └── review-examples.md      # Real review comment examples (anonymized)

SKILL.md Template

The generated SKILL.md should follow this structure:

---
name: vllm-omni-review-<category>
description: Specialized review skill for <category> PRs in vLLM/vLLM-Omni, derived from analysis of N historical PR reviews. Use when reviewing <category>-related pull requests.
---

# <Category> PR Review Guide

## Overview

This review skill was generated by analyzing N reviewed PRs from vLLM and vLLM-Omni
repositories. It encodes the most common review patterns, issues, and approval criteria
specific to <category> PRs.

## Quick Checklist

[Generated checklist of the top 10-15 most important review points, ordered by frequency]

## Detailed Review Areas

### [Area 1: Most Common Issue Category]
[Description, what to look for, examples from real reviews]

### [Area 2: Second Most Common]
...

## Common Pitfalls

[List of domain-specific pitfalls extracted from review comments]

## Approval Criteria

[What reviewers expect before approving this type of PR]

## Anti-Patterns

[Things that reviewers consistently reject or request changes for]

## References

- [Review Checklist](references/review-checklist.md)
- [Common Issues](references/common-issues.md)
- [Review Examples](references/review-examples.md)

Step 6: Update Review Router (Optional)

If the generated skill should be integrated into vllm-omni-review, suggest adding a routing entry in vllm-omni-review/references/review-routing.md.

Step 7: Present Summary to User

After generating the skill, present:

1. How many PRs were analyzed from each repo
2. Top 5 most common review patterns found
3. The generated skill location and structure
4. Suggestion to review and refine the generated skill

Important Guidelines

• Anonymize when appropriate: Don't attribute specific criticism to specific reviewers in the generated skill. Focus on the patterns, not the people.
• Prioritize by frequency: Issues that appear in 5+ PRs are more valuable than one-off comments.
• Include real examples: Concrete code snippets from reviews are more useful than abstract rules.
• Cross-repo patterns: Note when a pattern appears in both vLLM and vLLM-Omni — these are likely the most important.
• Keep it actionable: Every item in the generated skill should be something a reviewer can check in under 2 minutes.
• Rate limit awareness: Space out API calls to avoid GitHub rate limiting. Use --paginate judiciously.

Error Handling

• If gh is not authenticated, prompt the user to run gh auth login
• If a repo returns no results, try broadening the search query
• If rate-limited, inform the user and suggest retrying later
• If fewer than 5 PRs are found, warn that the generated skill may lack coverage

References

• See references/search-strategies.md for advanced search query patterns
• See references/analysis-prompts.md for review comment classification guidance