vllm-omni/.claude/skills

Repository Skills for vLLM-Omni

This directory contains repository-scale skills maintained for vllm-omni. They capture repeatable workflows for common contributor and maintainer tasks such as model integration, CI-aligned testing, performance optimization, and pull request review.

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

Using the Skills

Coding agents that discover repository skills can invoke the relevant skill by name. For other agents, point them to the skill's SKILL.md and ask them to read it before changing code. Combine a domain skill with vllm-omni-test when tests or CI coverage change, then use precheck-pr for the contributor's final self-check.

Available Skills

• add-diffusion-model: guides integration of a new diffusion model into vllm-omni
• add-tts-model: covers integration of new TTS models and related serving workflows
• diffusion-perf-opt: guides diffusion model performance optimization, including profiling traces, parallel strategies, stage timing analysis, and benchmark-driven tuning
• find-simplifications: finds evidence-backed opportunities to remove or merge dead, duplicated, speculative, over-generalized, or unnecessarily defensive vLLM-Omni code
• precheck-pr: self-checks a branch before creating a PR by validating title format, dead code, simplification opportunities, accuracy and performance claims, and merge readiness
• quantization: guides quantization method selection, model integration, checkpoint loading, and quality/performance validation for vLLM-Omni
• review-pr: provides a frozen-snapshot, contract-aware workflow for maintainers and reviewers; contributors should use precheck-pr for self-review
• vllm-omni-npu-model-runner-upgrade: upgrades NPU model runners to align with the latest vllm-ascend NPUModelRunner
• 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

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.