vllm-omni/.claude/skills

Claude Skills for vLLM-Omni

This directory contains Claude Code skills maintained for the vllm-omni repository. These skills capture repeatable workflows for common contributor tasks such as model integration, pull request review, and release note generation.

Directory Structure

Each skill lives in its own directory under .claude/skills/. A skill may include:

• SKILL.md: the main workflow and operating instructions
• references/: focused reference material used by the skill
• scripts/: small helper scripts used by the skill

Available Skills

• add-diffusion-model: guides integration of a new diffusion model into vllm-omni
• diffusion-perf-opt: guides diffusion model performance optimization, including profiling traces, parallel strategies, stage timing analysis, and benchmark-driven tuning
• quantization: guides quantization method selection, model integration, checkpoint loading, and quality/performance validation for vLLM-Omni
• add-omni-model: covers addition of new omni-modality model support
• add-tts-model: covers integration of new TTS models and related serving workflows
• generate-release-note: helps prepare release notes for repository changes
• precheck-pr: self-check a branch before creating a PR — validates PR title format, catches dead code, verifies accuracy/perf claims, and confirms merge readiness
• vllm-omni-test: guides generation and execution of CI-aligned tests (L1–L4), pytest marker selection (core_model / advanced_model / full_model, omni / tts / diffusion), Buildkite wiring (test-ready.yml, test-merge.yml, test-nightly.yml, test-weekly.yml), and copy-paste local plus CI-like pytest commands; see references/test-routing.md for level-to-command mapping
• review-pr: provides a structured workflow for reviewing pull requests
• vllm-omni-npu-model-runner-upgrade: upgrades NPU model runners to align with the latest vllm-ascend NPUModelRunner

Maintenance Guidelines

• Keep skill names short and task-oriented.
• Prefer repository-local paths, commands, and examples.
• Avoid hardcoding fast-changing support matrices unless the skill is actively maintained alongside those changes.
• Treat skills as contributor tooling: optimize for clarity, actionability, and low maintenance overhead.