Explore AI Agent Skills & Claude Prompts

Contribute to vLLM-Omni by adding new model support, fixing bugs, or improving features. Use when integrating a new model into vllm-omni, setting up a development environment, writing tests, or submitting pull requests to the vllm-omni project.

vllm-omni-review

Review PRs on vllm-project/vllm-omni by routing to the right domain skills, checking critical evidence, and focusing comments on blocking issues. Use when reviewing pull requests or local branches, triaging review depth, running detailed or default review, or checking tests, benchmarks, and breaking changes in vllm-omni.

schedule Updated 29 days ago

vllm-omni-release-note-writer

Use when drafting or editing release notes for vllm-project/vllm-omni, especially when summarizing changes between tags, organizing highlights, and matching the style of recent vLLM-Omni releases

schedule Updated 2 months ago

vllm-omni-pre-check

Use before submitting a PR to vllm-project/vllm-omni — self-check the branch against project conventions, catch dead code, verify accuracy/performance claims, and confirm merge readiness. Use when the user says "pre-check", "self review", "pre-submit check", or "check my PR before I open it."

schedule Updated 29 days ago

vllm-omni-recipe

Use when adding a recipe for omnimodal models (text-to-image, text-to-video, text-to-audio, image-to-video, any-to-any, diffusion transformers) to the vLLM recipes repository, or documenting vLLM-Omni deployment

schedule Updated 3 months ago

vllm-omni-diffusion-perf-optim

Guide for achieving optimal inference performance with vLLM-Omni diffusion models. Covers all lossless and lossy optimization methods (parallelism, torch.compile, CPU offload, quantization, cache acceleration), per-model support tables, and ready-to-use recipes. Use when asked to speed up diffusion inference, reduce latency, lower VRAM usage, or tune a diffusion pipeline.

schedule Updated 3 months ago

vllm-omni-quantization

Use when working on vLLM-Omni quantization for autoregressive, diffusion, or multi-stage omni models, choosing methods such as `awq`, `gptq`, `fp8`, `int8`, `gguf`, or ModelOpt checkpoints, adding quantized model support, or debugging memory, loader, quality, or performance issues.

vllm-omni-cicd

Set up CI/CD pipelines for vLLM-Omni model deployments including Docker builds, automated testing, rolling updates, and deployment validation. Use when creating deployment pipelines, automating model serving updates, setting up Docker workflows, or configuring GitHub Actions for vllm-omni.

schedule Updated 3 months ago

vllm-omni-audio-tts

Generate audio and speech with vLLM-Omni using Qwen3-TTS, Fish Speech S2 Pro, CosyVoice3, MiMo-Audio, and Stable-Audio models. Use when synthesizing speech from text, generating audio effects or music, configuring TTS parameters, cloning voices, adding new TTS models, or working with text-to-speech models.

vllm-omni-api

Integrate with vLLM-Omni using the OpenAI-compatible API for text, image, video, and audio generation. Use when building client applications, calling vllm-omni endpoints, sending requests to the API server, or integrating vllm-omni into an application.

vllm-omni-video-gen

Generate videos with vLLM-Omni using Wan2.2 and other video generation models. Use when generating videos from text, creating videos from images, configuring video generation parameters, or working with text-to-video or image-to-video models.

vllm-omni-perf

Optimize vLLM-Omni performance through benchmarking, TeaCache, Cache-DiT, quantization, CPU offloading, and parallelism tuning. Use when improving inference speed, reducing latency, lowering memory usage, running benchmarks, or enabling diffusion acceleration.