Agent skill

Review PR

by vllm-project in vllm-project/vllm-omni

Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings.

Apache-2.0Auto-check passedAI & LLM Engineering

Install Review PR

skills CLI
$ npx skills add vllm-project/vllm-omni --skill review-pr -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vllm-project/vllm-omni review-pr --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vllm-project/vllm-omni.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/review-pr .claude/skills/review-pr && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-pr
GitHub stars
7.1k
Token cost
~3.8k tokens
SKILL.md length
1,619 words
Files
34 (incl. references)
Skills in repo
20
Repo updated
First seen
Licence
Apache-2.0

At a glance

Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings.

  • Works in 8 steps: Freeze and report the snapshot → Build the diff census → Route from the live behavior → …
  • Repeat maintainer reviews
  • SKILL.md covers Quality contract, Select the input and depth, Reference guide and Workflow
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review PR is an agent skill from vllm-project/vllm-omni. Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings. Use for default, detailed, or repeat maintainer reviews; checking correctness, compatibility, tests, benchmarks, model additions, distributed changes, or breaking behavior; and identifying or explicitly requesting the most relevant code-owner reviewers. Use precheck-pr instead for an author's pre-submit self-check.

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 39 other files, including reference files (for example `agents/openai.yaml`, `references/checks/model-addition-checklist.md` and `references/checks/perf-verification.md`).

It sits in AI & LLM Engineering, covering Pull requests, LLM inference and serving and Verification before completion. It works with vLLM. The repository describes itself as: A framework for efficient model inference with omni-modality models. The licence is Apache-2.0.

When your agent uses it

  • Repeat maintainer reviews
  • Checking correctness
  • Model additions
  • Distributed changes

Example prompts

  • “/review-pr”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Freeze and report the snapshot
  2. Build the diff census
  3. Route from the live behavior
  4. Run the blocker scan
  5. Apply module and feature contracts
  6. Verify the changed path
  7. Consolidate and deliver
  8. Optionally request focused owner reviews

What it can do on your machine

Read from SKILL.md and the folder at commit 88a35c0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.vllm.ai

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review PR loads about 3.8k tokens when it runs, and up to ~25k if it reads all its reference files. Until then it costs about 132 tokens; SKILL.md has 1,619 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~132
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~25k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from vllm-project/vllm-omni at commit 88a35c0, republished under its Apache-2.0 licence (© vllm-project). 1,619 words, ~3,763 tokens.

Download SKILL.mdSave it as .claude/skills/review-pr/SKILL.md (or your agent's skills folder). This skill also uses 33 other files; get the full folder from GitHub.
name
review-pr
description
Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings. Use for default, detailed, or repeat maintainer reviews; checking correctness, compatibility, tests, benchmarks, model additions, distributed changes, or breaking behavior; and identifying or explicitly requesting the most relevant code-owner reviewers. Use precheck-pr instead for an author's pre-submit self-check.

Review vLLM-Omni Pull Requests

Review like a maintainer: direct, selective, and focused on issues that CI does not prove. Prefer a few high-confidence findings over exhaustive commentary. Zero findings is a valid result.

Quality contract

Make every finding:

  • Correct: prove a reachable failure, not a suspicion.
  • Prioritized: lead with merge blockers and high-impact defects.
  • Actionable: identify the smallest safe fix direction.
  • Evidence-based: cite code, tests, docs, CI, or measurements.
  • Concise: avoid review templates and repeated summaries.
  • Calibrated: match severity to user and maintainer impact.

Do not report unrelated backlog, style already enforced by pre-commit, or a missing test that would not protect changed behavior. Do report new allowlist or budget entries in check_forbidden_imports.py / check_torch_cuda.py / check_tts_adapter.py / check_buildkite.py unless the PR justifies them: those are policy changes, not lint noise.

Select the input and depth

Use vllm-project/vllm-omni as the base repository. Accept its forks and local checkouts; use another skill for unrelated repositories.

InputReview surface
PR number or URLFrozen PR metadata, full diff, and relevant threads.
Local branch/worktreeFrozen target-base SHA through committed, staged, unstaged, and in-scope untracked changes.
Pre-filled contextReuse supplied metadata; fetch only missing facts and the full diff.

Default to maintainer brevity. A detailed or audit request expands coverage and lists path:line findings, but keeps the same confidence and severity bar.

Reference guide

Load references in review order: process, one primary module contract, matching feature designs, evidence checks, then delivery. Every file is linked directly below; do not load unrelated module references.

Each concise reference links to the maintained vLLM-Omni documentation. For branch-specific behavior, inspect the matching docs/ file in the reviewed checkout first; use the published latest docs for current guidance and discovery. If docs and live code disagree, verify the code/tests and report the drift.

Review process
ReferenceRead when
review-execution.mdEvery review; freeze inputs, inspect safely, and deliver against the same snapshot.
general-checks.mdEvery review; apply repository-wide correctness and evidence rules.
design-contracts.mdEvery production review; resolve branch-local module and feature design status.
review-routing.mdAfter the diff census; select one primary module and conditional overlays.
Primary module contract
ReferenceRead when
entrypoints.mdOffline/CLI/API ingress, validation, rendering, streaming, or sessions change.
configuration.mdConfig construction, deploy/stage schema, defaults, registry, or topology changes.
input-output-modality.mdRequests, messages, serialization, output types, accumulation, or completion change.
error-contracts.mdError classification, fatality, propagation, sanitization, or rendering changes.
engine-orchestration.mdCross-stage routing, request state, output ordering, RPC correlation, or terminal convergence changes.
stage-runtime.mdPlacement, startup, readiness, replica identity, affinity, membership, or shutdown changes.
omni-connector.mdCross-stage/process/device/node transport or synchronization changes.
model-integration.mdRegistration, preprocessing, loading, runners, or model-specific execution changes.
ar-runtime.mdAR scheduling, request/cache state, adapters, workers, or upstream vLLM semantics change.
diffusion.mdDiffusion runtime, models, batching, parallelism, or offload changes.
execution-platforms.mdHardware selection, capabilities, vendor workers, kernels, or patches change.
cache-management.mdCache identity, reuse, validity, reset, eviction, or teardown changes.
quantization.mdQuantization selection, checkpoint metadata, layer mapping, precision, or constraints change.
observability.mdMetrics, logs, units, labels, correlation, or lifecycle changes.
profiling.mdProfiling instrumentation, traces, start/stop lifecycle, or overhead changes.
benchmarking.mdBenchmark workload, metric calculation, CLI, or result metadata changes.
Feature-design overlays
ReferenceRead when
runtime-stage-execution.mdDisaggregated inference, async chunk/output/materialization, or prefix caching changes.
communication.mdA concrete OmniConnector backend or its deployment contract changes.
diffusion-acceleration.mdDiffusion parallelism, attention, quantization, cache, batching, or offload changes.
infrastructure-performance.mdMetrics infrastructure or documented speech optimization stacks change.
Evidence and quality checks
ReferenceRead when
model-addition-checklist.mdA model, architecture, loader, processor, registry, pipeline config, or deploy config is added.
perf-verification.mdThe PR makes a latency, throughput, memory, or quality claim.
test-quality-evaluation.mdTests change, are absent for risky code, or may not exercise production behavior.
tests-docs-checklist.mdCoverage, CI markers, examples, user docs, or PR evidence need review.
verification.mdHardware, a server, or a runnable affected path is available for active verification.
examples-policy.mdThe PR adds, copies, or renames Python under examples/; apply the canonical policy shared with precheck-pr.
find-simplificationsEvery review; run a diff-scoped subtraction and simplification pass after correctness blockers.
Delivery and reviewer coordination
ReferenceRead when
maintainer-style-study.mdFindings are ready for concise maintainer-style delivery.
review-requests.mdThe user asks to identify, suggest, request, or ping code-owner reviewers.

Workflow

1. Freeze and report the snapshot

Pin the base and head before reading source or running validation. Within 60 seconds, report the pinned head, CI, mergeability, and preliminary findings in the host conversation. Do not wait for CI or post this update to GitHub.

If the target changes while fetching, discard the evidence and retry once. If it changes again, report the churn and wait for a stable target.

For a trusted PR head, materialize the pinned head in an isolated detached worktree. A worktree freezes identity but is not a security sandbox. Treat fork heads as untrusted until the user and environment policy explicitly establish trust: execute their code only after that trust is recorded for this host, with the trusted SHA noted in the review state and secrets kept out of the execution scope; without recorded trust, use static SHA-addressed reads and CI evidence only. For a local review, freeze the committed, index, worktree, and NUL-safe in-scope untracked contents. Follow review-execution.md for trust gates, state fingerprints, and byte-for-byte staleness checks.

2. Build the diff census

Group files into production code, tests, docs, configuration, build/CI, and generated artifacts. Map each changed production file and test group to the PR goal. Compare the title/body claims with the actual diff; use linked issues only when they define the contract or reproduction.

Mark unrelated scope and unexplained generated artifacts. Do not infer behavior from the PR description without tracing the live code.

3. Route from the live behavior

Trace each claimed behavior through the changed producer to its live consumer, then use design-contracts.md and review-routing.md to select one primary module contract, a second only for a real documented cross-boundary call path, and every matching feature-design and evidence overlay. Treat titles and paths as hints; live behavior and the frozen head's current design metadata are authoritative. For docs-, tests-, or CI-only changes, route to the production contract they protect or use only the applicable evidence checks.

Show full SKILL.md (619 more words)Show less
4. Run the blocker scan

Apply every category in general-checks.md before lower-priority comments.

If the diff census contains an added, copied, or renamed Python path under examples/, read and apply the canonical examples policy. Treat a new model-specific Python example as blocking. Do not flag model-specific example debt that the PR only modifies or removes, and do not run the rest of the author-oriented precheck-pr workflow.

For each changed value or behavior, trace:

text
public ingress -> validation/defaulting -> producer -> transformations
  -> stage/worker/connector boundary -> final consumer -> terminal cleanup

Cover every applicable offline/online, streaming/non-streaming, sync/async, feature-on/off, topology, and compatibility path. Search bounded callers and sibling implementations rather than assuming the changed hunk is the only path.

5. Apply module and feature contracts

Apply the reference set selected in step 3 and any matching repo-local skill. Read the exact module and feature pages in the frozen head, including status, ownership boundary, dependencies, candidate invariants, safe-change guide, and promotion gate. Candidate or draft rules are questions, not blockers, unless current code, tests, or policy enforce them. Inspect both sides of any config, registry, serialization, connector, cache, or stage boundary.

Read and apply find-simplifications on every review. Constrain it to the diff and the adjacent ownership, callers, or consumers needed to prove a candidate. Check whether added or expanded helpers, classes, state, fallback and compatibility branches, data movement, or public behavior can be deleted, merged, moved, or inlined. Zero candidates is a valid result. Do not widen the review into repository backlog or report speculative style preferences as simplification findings.

6. Verify the changed path

Before each validation group, verify the frozen SHA plus the tracked, index, untracked, and ignored-file fingerprint, or recreate a pristine snapshot. On a head the user explicitly trusted for this host, run an import/version preflight, then the narrowest relevant tests and low-cost static checks. Bind every result to the head SHA, snapshot fingerprint, and environment fingerprint. Never run imports, tests, builds, hooks, or repo-configurable tooling from an untrusted head on the reviewer host.

  • Treat CI as status evidence; inspect only the first overlapping failure.
  • For docs-only changes, use diff hygiene, links/build checks, and bounded live contract verification instead of dependency setup or pytest.
  • For hardware-dependent paths, run available static/CPU checks and name the exact GPU/NPU gap. Never simulate device evidence.
  • For performance or accuracy claims, require comparable base/head runs with the same environment, workload, warmup, repetitions, and quality criteria.

Stop when each changed semantic path has a supported finding or an explicit no-issue conclusion. Do not search further only to increase confidence.

7. Consolidate and deliver

Verify each finding against the current diff, deduplicate by root cause, and order by severity.

Re-read the remote head and reverify or recreate the pristine validation snapshot immediately before delivery. If either changed, mark the review stale and restart from the new snapshot.

Return findings first. Use maintainer-style-study.md to keep them direct and brief. Each finding must include an exact path:line, trigger or call path, current behavior, impact, and smallest fix direction. If there are no findings, say so briefly and name material validation gaps.

Keep the review read-only unless the user explicitly authorizes posting. Do not submit APPROVE, COMMENT, or REQUEST_CHANGES, add labels, edit code, or push commits as an implied part of review.

8. Optionally request focused owner reviews

Only when the user asks to identify or request reviewers, read review-requests.md. Rank path-matched CODEOWNERS with the frozen module page's owners or required reviewers and documented governance expertise; propose one to three focused reviewers with an explicit contract rationale.

Identifying or suggesting reviewers is read-only. Requesting reviewers or posting @mention comments changes external state and requires explicit user authorization. When authorized, recheck the head, deduplicate existing requests, and post at most one consolidated comment. Do not infer this permission from a request to review the code.

© vllm-project, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 33 other files (references) in .claude/skills/review-pr of vllm-project/vllm-omni.

  • SKILL.md
  • agents/openai.yaml
  • references/checks/model-addition-checklist.md
  • references/checks/perf-verification.md
  • references/checks/test-quality-evaluation.md
  • references/checks/tests-docs-checklist.md
  • references/checks/verification.md
  • references/delivery/maintainer-style-study.md
  • references/delivery/review-requests.md
  • references/features/communication.md
  • references/features/diffusion-acceleration.md
  • references/features/infrastructure-performance.md
  • references/features/runtime-stage-execution.md
  • references/modules/ar-runtime.md
  • references/modules/benchmarking.md
  • … and 19 more

Open the folder on GitHubat commit 88a35c0

Compare with similar skills

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LLM Torch Profiler Trace AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS925—~2.8kAutomated safety check: PassNone
Dev BumpNetis/heron102—~983Automated safety check: PassApache-2.0
LLM Pipeline Profiler AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS925—~3.9kAutomated safety check: PassNone

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Works with

Questions about Review PR

What does Review PR do?

Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings. Review PR is an agent skill from vllm-project/vllm-omni. Review pull requests and local branches for vllm-project/vllm-omni with a frozen snapshot, module-design ownership, feature-design overlays, targeted validation, and concise evidence-backed findings.

When should I use Review PR?

Review PR fits situations like: repeat maintainer reviews; checking correctness; model additions; distributed changes.

How do I install Review PR in Claude Code?

Run `npx skills add vllm-project/vllm-omni --skill review-pr -a claude-code`. Or copy the skill folder (.claude/skills/review-pr in vllm-project/vllm-omni) into .claude/skills/review-pr in your project. Claude Code loads it when a task matches its description.

How do I install Review PR in Codex?

Run `npx skills add vllm-project/vllm-omni --skill review-pr -a codex`. Or copy the skill folder (.claude/skills/review-pr in vllm-project/vllm-omni) into .agents/skills/review-pr in your project. Codex loads it when a task matches its description.

Can I use Review PR in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vllm-project/vllm-omni --skill review-pr -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-pr, .gemini/skills/review-pr, .github/skills/review-pr and .opencode/skills/review-pr in your project.

What does Review PR need to run?

SKILL.md names no scripts, command-line tools or credentials: Review PR is instructions for the agent only. Our summary lists: Python 3.

Does Review PR access the network?

SKILL.md names 1 domain. As links in the text: docs.vllm.ai. This is read from the text; nothing was executed.

Is Review PR safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review PR use?

Review PR is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review PR use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 21k tokens, read only when the agent opens those files.

What are the alternatives to Review PR?

Skills that share tags, products or a category with Review PR: Vllm Rlt Review PR (ThinkFlowLab/vllm-rlt, 144 stars), Perfup (raullenchai/Rapid-MLX, 3.9k stars), LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 925 stars) and Dev Bump (Netis/heron, 102 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review PR?

vllm-project (a GitHub organization) maintains it in vllm-project/vllm-omni, which has 7,097 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on October 9, 2026.

Source: vllm-project/vllm-omni on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.