vllm-pr-summary

vllm-pr-summary is a skill for Claude Code, Codex from shen-shanshan/vllm-dev-skills. It costs 132 tokens per session (1,316 once invoked), scanned A, original, Apache-2.0.

A reporting tool for analyzing pull requests in the vllm-project/vllm GitHub repository. It produces a Markdown report covering the change overview, code analysis, technical ideas, discussion points, and risks, with diagrams when useful.

In plain words
What is it for?
Use it with a vLLM pull request URL or number to fetch the pull request, analyze its diff and comments, and save a structured report in the outputs folder.
Why use it?
It brings pull request metadata, discussion, and code changes into one review document. This makes the purpose, design, and possible risks easier to understand.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Not installable: its command points at a path on the author’s own machine, so it runs nowhere else. The line is /Users/shanshan-shen/.claude/skills/vllm-pr-summary/scripts/fetch_pr_data.py.

Good fit Use it with a vLLM pull request URL or number to fetch the pull request, analyze its diff and comments, and save a structured report in the outputs folder.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for vllm-pr-summary

README.md
[![agentmods](https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-pr-summary/github.svg)](https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-pr-summary)
Your own site
<a href="https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-pr-summary"><img src="https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-pr-summary/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for vllm-pr-summary

Your own site · 80×15
<a href="https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-pr-summary"><img src="https://agentmods.dev/badge/skills/shen-shanshan/vllm-dev-skills/vllm-pr-summary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 132 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,316 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00132 $0.01316
Opus 5 $0.00066 $0.00658
Sonnet 5 $0.00026 $0.00263
Haiku 4.5 $0.00013 $0.00132

Measured 11d ago against content hash 019d88fcaafa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

vllm-pr-summary scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/fetch_pr_data.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/vllm-pr-summary/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

vllm PR Summary

Fetch and analyze a vllm-project/vllm PR, then write a structured Markdown report to ./outputs/.

Take ./reference/reference.md as a reference for the report structure and style. Include Mermaid diagrams to illustrate architecture or flow when relevant.

Workflow

  1. Fetch PR data — run scripts/fetch_pr_data.py
  2. Read the JSON — load the saved file into context
  3. Analyze — synthesize PR description, comments, and diff
  4. Write report — save to ./outputs/pr-<NUMBER>-summary.md
  5. Confirm — tell the user the output path

Step 1: Fetch PR Data

python3 /Users/shanshan-shen/.claude/skills/vllm-pr-summary/scripts/fetch_pr_data.py <PR_NUMBER> \
    --output /tmp/vllm_pr_<PR_NUMBER>.json

Optional flags:

  • --token <token> — GitHub PAT (not needed if gh CLI is authenticated)
  • --max-diff-chars <N> — limit diff size (default 80 000)

Step 2: Load and Analyze

Read /tmp/vllm_pr_<PR_NUMBER>.json. The JSON contains:

Key Content
pr PR metadata: title, body, author, state, labels, additions/deletions, branch names, reviews
diff Full unified diff of all changed files
files Per-file stats: filename, status, additions, deletions, patch
issue_comments General discussion comments
review_comments Inline code review comments

For large diffs, focus on the files[].patch fields grouped by module/directory.

Step 3: Write the Report

Save to /Users/shanshan-shen/.claude/skills/vllm-pr-summary/outputs/pr-<NUMBER>-summary.md.

Report Structure

# PR #<NUMBER>: <Title>

> **Author**: @author | **State**: OPEN/MERGED/CLOSED | **Date**: YYYY-MM-DD
> **Branch**: `head` → `base` | **Labels**: label1, label2
> **Changes**: +X -Y lines across N files

---

## 1. 总结 (Summary)

2–4 sentences: what problem does this PR solve, and what is the core approach?

## 2. 背景与动机 (Background & Motivation)

Why is this change needed? Reference the PR description and any linked issues.

## 3. 代码修改分析 (Code Change Analysis)

### 3.1 修改的模块

List the changed files grouped by module/directory with a one-line description each.

### 3.2 架构 / 流程图 (Architecture / Flow Diagram)

Include at least one Mermaid diagram. Choose the most appropriate type:
- **flowchart TD** — for execution flow or decision logic
- **sequenceDiagram** — for interactions between components
- **classDiagram** — for new classes or interface changes
- **graph LR** — for data/dependency relationships

Example:
```mermaid
flowchart TD
    A[Request] --> B{Router}
    B -->|path A| C[Handler A]
    B -->|path B| D[Handler B]

3.3 关键实现细节 (Key Implementation Details)

Bullet-point the most important code changes: new classes, changed APIs, algorithm changes, config additions.

4. 涉及的技术原理 (Technical Principles)

Explain relevant background concepts a reviewer needs to understand this PR (e.g., SPMD, paged attention, tensor parallelism, CUDA graphs, chunked prefill, etc.). 2–5 short paragraphs or bullets.

5. 评论区讨论亮点 (Discussion Highlights)

Summarize notable points from issue_comments and review_comments: reviewer concerns, design debates, requested changes, approvals. Skip trivial comments (lgtm, thanks).

6. 风险与潜在问题 (Risk Analysis)

Structured risk table:

风险 严重程度 说明
Risk description High / Medium / Low Details

Categories to consider:

  • 正确性: edge cases, off-by-one errors, race conditions
  • 性能: regression in throughput/latency, memory overhead
  • 兼容性: breaking API changes, backend-specific behavior
  • 测试覆盖: missing unit/integration tests
  • 可维护性: complexity, missing documentation

7. 结论 (Conclusion)

1–2 sentences on the overall quality and readiness of the PR.

Read the full file on GitHub · 138 lines

Files

What ships with it

44 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 138 lines · 132 tokens per session scan A 019d88fcaafa

Subscribe to this mod's changes

vllm-pr-summary is a skill published in the GitHub repository shen-shanshan/vllm-dev-skills (17 stars, last pushed 2d ago), licensed Apache-2.0. It adds 132 tokens to every session and 1,316 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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