Vllm Rlt Review PR
ThinkFlowLab/vllm-rlt
Review PRs and local changes for hsliuustc0106/vllm-rlt: Ouro engine and KV correctness, serving behavior, and BF16 accuracy/speed evidence.
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.
$ npx skills add vllm-project/vllm-omni --skill review-pr -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install vllm-project/vllm-omni review-pr --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "review-pr" agent skill from https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills/review-pr into .claude/skills/review-pr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pr", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills/review-prType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add vllm-project/vllm-omni --skill review-pr -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install vllm-project/vllm-omni review-pr --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vllm-project/vllm-omni.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/review-pr .agents/skills/review-pr && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "review-pr" agent skill from https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills/review-pr into .agents/skills/review-pr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pr", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vllm-project/vllm-omni --skill review-pr -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install vllm-project/vllm-omni review-pr --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vllm-project/vllm-omni.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/review-pr .cursor/skills/review-pr && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "review-pr" agent skill from https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills/review-pr into .cursor/skills/review-pr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pr", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/vllm-project/vllm-omni.git --path .claude/skills/review-pr--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add vllm-project/vllm-omni --skill review-pr -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install vllm-project/vllm-omni review-pr --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vllm-project/vllm-omni.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/review-pr .gemini/skills/review-pr && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "review-pr" agent skill from https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills/review-pr into .gemini/skills/review-pr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pr", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install vllm-project/vllm-omni review-prInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add vllm-project/vllm-omni --skill review-pr -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/vllm-project/vllm-omni.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/review-pr .github/skills/review-pr && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "review-pr" agent skill from https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills/review-pr into .github/skills/review-pr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pr", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add vllm-project/vllm-omni --skill review-pr -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install vllm-project/vllm-omni review-pr --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/vllm-project/vllm-omni.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/review-pr .opencode/skills/review-pr && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "review-pr" agent skill from https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills/review-pr into .opencode/skills/review-pr/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-pr", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
review-prReview 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. 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.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 88a35c0. It shows what the files ask for, not the result of running them.
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.
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.
Links to these hosts (documentation or services it may open):
docs.vllm.aiFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
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.
.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.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.
Make every finding:
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.
Use vllm-project/vllm-omni as the base repository. Accept its forks and local
checkouts; use another skill for unrelated repositories.
| Input | Review surface |
|---|---|
| PR number or URL | Frozen PR metadata, full diff, and relevant threads. |
| Local branch/worktree | Frozen target-base SHA through committed, staged, unstaged, and in-scope untracked changes. |
| Pre-filled context | Reuse 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.
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.
| Reference | Read when |
|---|---|
| review-execution.md | Every review; freeze inputs, inspect safely, and deliver against the same snapshot. |
| general-checks.md | Every review; apply repository-wide correctness and evidence rules. |
| design-contracts.md | Every production review; resolve branch-local module and feature design status. |
| review-routing.md | After the diff census; select one primary module and conditional overlays. |
| Reference | Read when |
|---|---|
| entrypoints.md | Offline/CLI/API ingress, validation, rendering, streaming, or sessions change. |
| configuration.md | Config construction, deploy/stage schema, defaults, registry, or topology changes. |
| input-output-modality.md | Requests, messages, serialization, output types, accumulation, or completion change. |
| error-contracts.md | Error classification, fatality, propagation, sanitization, or rendering changes. |
| engine-orchestration.md | Cross-stage routing, request state, output ordering, RPC correlation, or terminal convergence changes. |
| stage-runtime.md | Placement, startup, readiness, replica identity, affinity, membership, or shutdown changes. |
| omni-connector.md | Cross-stage/process/device/node transport or synchronization changes. |
| model-integration.md | Registration, preprocessing, loading, runners, or model-specific execution changes. |
| ar-runtime.md | AR scheduling, request/cache state, adapters, workers, or upstream vLLM semantics change. |
| diffusion.md | Diffusion runtime, models, batching, parallelism, or offload changes. |
| execution-platforms.md | Hardware selection, capabilities, vendor workers, kernels, or patches change. |
| cache-management.md | Cache identity, reuse, validity, reset, eviction, or teardown changes. |
| quantization.md | Quantization selection, checkpoint metadata, layer mapping, precision, or constraints change. |
| observability.md | Metrics, logs, units, labels, correlation, or lifecycle changes. |
| profiling.md | Profiling instrumentation, traces, start/stop lifecycle, or overhead changes. |
| benchmarking.md | Benchmark workload, metric calculation, CLI, or result metadata changes. |
| Reference | Read when |
|---|---|
| runtime-stage-execution.md | Disaggregated inference, async chunk/output/materialization, or prefix caching changes. |
| communication.md | A concrete OmniConnector backend or its deployment contract changes. |
| diffusion-acceleration.md | Diffusion parallelism, attention, quantization, cache, batching, or offload changes. |
| infrastructure-performance.md | Metrics infrastructure or documented speech optimization stacks change. |
| Reference | Read when |
|---|---|
| model-addition-checklist.md | A model, architecture, loader, processor, registry, pipeline config, or deploy config is added. |
| perf-verification.md | The PR makes a latency, throughput, memory, or quality claim. |
| test-quality-evaluation.md | Tests change, are absent for risky code, or may not exercise production behavior. |
| tests-docs-checklist.md | Coverage, CI markers, examples, user docs, or PR evidence need review. |
| verification.md | Hardware, a server, or a runnable affected path is available for active verification. |
| examples-policy.md | The PR adds, copies, or renames Python under examples/; apply the canonical policy shared with precheck-pr. |
| find-simplifications | Every review; run a diff-scoped subtraction and simplification pass after correctness blockers. |
| Reference | Read when |
|---|---|
| maintainer-style-study.md | Findings are ready for concise maintainer-style delivery. |
| review-requests.md | The user asks to identify, suggest, request, or ping code-owner reviewers. |
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.
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.
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.
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:
public ingress -> validation/defaulting -> producer -> transformations
-> stage/worker/connector boundary -> final consumer -> terminal cleanupCover 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.
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.
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.
Stop when each changed semantic path has a supported finding or an explicit no-issue conclusion. Do not search further only to increase confidence.
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.
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
SKILL.md and 33 other files (references) in .claude/skills/review-pr of vllm-project/vllm-omni.
Open the folder on GitHubat commit 88a35c0
Review PR next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Review PR this skillvllm-project/vllm-omni | 7.1k | — | ~3.8k | Automated safety check: Pass | Apache-2.0 | |
| Vllm Rlt Review PRThinkFlowLab/vllm-rlt | 144 | — | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Perfupraullenchai/Rapid-MLX | 3.9k | — | ~1.6k | Automated safety check: Notes | Custom licence | |
| LLM Torch Profiler Trace AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS | 925 | — | ~2.8k | Automated safety check: Pass | None | |
| Dev BumpNetis/heron | 102 | — | ~983 | Automated safety check: Pass | Apache-2.0 | |
| LLM Pipeline Profiler AnalysisBBuf/AI-Infra-Auto-Driven-SKILLS | 925 | — | ~3.9k | Automated safety check: Pass | None |
ThinkFlowLab/vllm-rlt
Review PRs and local changes for hsliuustc0106/vllm-rlt: Ouro engine and KV correctness, serving behavior, and BF16 accuracy/speed evidence.
raullenchai/Rapid-MLX
Autonomous performance optimization: research, PoC, benchmark, implement, review, PR
BBuf/AI-Infra-Auto-Driven-SKILLS
Analyzes Torch Profiler traces from SGLang, vLLM and TensorRT-LLM servers into kernel attribution, overlap and fusion tables.
Netis/heron
Bump Heron version via the VERSION-file SSOT. An agent skill from Netis/heron.
BBuf/AI-Infra-Auto-Driven-SKILLS
Breaks LLM torch profiler traces down by forward pass, layer and kernel, with timing tables and Perfetto time ranges for the layers you want to inspect.
sgl-project/sglang
Unified LLM torch-profiler triage skill for sglang, vllm, TensorRT-LLM, and TokenSpeed.
vllm-project/vllm-omni
Diagnose and optimize vLLM Omni diffusion workloads, especially Wan/Qwen/Flux-style image and video generation.
vllm-project/vllm-omni
Self-check your branch before creating a PR — catch dead code, prevent new model-specific Python examples, verify accuracy/perf claims, validate PR title format, and confirm merge readiness.
vllm-project/vllm-omni
Work on vLLM-Omni quantization for diffusion, autoregressive, omni, or multi-stage models.
vllm-project/vllm-omni
Write MiniMax H3 video generation prompts for T2VA, I2VA, FL2VA, L2VA, and Ref2VA.
vllm-project/vllm-omni
Add a new diffusion model (text-to-image, text-to-video, image-to-video, text-to-audio, image editing) to vLLM-Omni, including native non-Diffusers ports, reference-parity validation, Cache-DiT…
vllm-project/vllm-omni
Add or update an in-repository vLLM-Omni model recipe with verified task, input, output, hardware, command, feature, and validation contracts.
Works with
Categories
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.
Review PR fits situations like: repeat maintainer reviews; checking correctness; model additions; distributed changes.
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.
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.
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.
SKILL.md names no scripts, command-line tools or credentials: Review PR is instructions for the agent only. Our summary lists: Python 3.
SKILL.md names 1 domain. As links in the text: docs.vllm.ai. This is read from the text; nothing was executed.
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.
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.
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.
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.
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.