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.
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.
[](https://agentmods.dev/skills/shen-shanshan/vllm-dev-skills/vllm-pr-summary)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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
- Fetch PR data — run
scripts/fetch_pr_data.py - Read the JSON — load the saved file into context
- Analyze — synthesize PR description, comments, and diff
- Write report — save to
./outputs/pr-<NUMBER>-summary.md - 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 ifghCLI 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.
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.
- outputs/pr-35963-summary.md 17 KB
- outputs/pr-38040-summary.md 12 KB
- outputs/pr-38061-summary.md 17 KB
- outputs/pr-38330-summary.md 9.5 KB
- outputs/pr-40576-summary.md 16 KB
- outputs/pr-40660-summary.md 17 KB
- outputs/pr-40695-summary.md 15 KB
- outputs/pr-40830-summary.md 11 KB
- outputs/pr-41234-summary.md 9.5 KB
- outputs/pr-41714-summary.md 19 KB
- outputs/pr-41714-vs-42224-comparison.md 10 KB
- outputs/pr-41736-summary.md 9.5 KB
- outputs/pr-41759-summary.md 11 KB
- outputs/pr-41992-summary.md 12 KB
- outputs/pr-42288-summary.md 17 KB
- outputs/pr-42785-summary.md 11 KB
- outputs/pr-43591-summary.md 7.7 KB
- outputs/pr-43718-summary.md 15 KB
- outputs/pr-45254-summary.md 9.9 KB
- outputs/pr-46451-summary.md 15 KB
- outputs/pr-47017-summary.md 11 KB
- outputs/pr-49852-summary.md 11 KB
- outputs/pr-49934-summary.md 11 KB
- outputs/pr-50727-summary.md 15 KB
- outputs/pr-51004-summary.md 12 KB
- outputs/pr-51040-summary.md 14 KB
- outputs/pr-51171-summary.md 18 KB
- outputs/pr-51633-summary.md 10 KB
- outputs/pr-52033-summary.md 15 KB
- outputs/pr-52376-summary.md 13 KB
- outputs/pr-52882-summary.md 16 KB
- outputs/pr-52921-summary.md 12 KB
- outputs/pr-53161-summary.md 15 KB
- outputs/pr-53407-summary.md 15 KB
- outputs/pr-53598-summary.md 16 KB
- outputs/pr-53664-summary.md 15 KB
- outputs/pr-53838-summary.md 16 KB
- outputs/pr-53987-summary.md 14 KB
- outputs/pr-54682-summary.md 14 KB
- outputs/pr-54787-summary.md 14 KB
- outputs/pr-54855-summary.md 15 KB
- outputs/pr-moe-refactor-combined-summary.md 12 KB
- reference/reference.md 17 KB
- scripts/fetch_pr_data.py 6.6 KB runs code
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.
- 11d ago First seen · 138 lines · 132 tokens per session scan A 019d88fcaafa
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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