Merge branch 'main' into codex-contributor-agent-skills-docs

Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>

.claude/skills/readme.md

@@ -1,9 +1,9 @@
-# Claude Skills for vLLM-Omni
+# Repository Skills for vLLM-Omni
 
-This directory contains Claude Code skills maintained for the `vllm-omni`
+This directory contains repository-scale skills maintained for `vllm-omni`.
-repository. These skills capture repeatable workflows for common contributor
+They capture repeatable workflows for common contributor and maintainer tasks
-tasks such as model integration, pull request review, and release note
+such as model integration, CI-aligned testing, performance optimization, and
-generation.
+pull request review.
 
 ## Directory Structure
 
@@ -14,31 +14,41 @@ include:
 - `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
+- [`add-diffusion-model`](add-diffusion-model/SKILL.md): guides integration of
-  `vllm-omni`
+  a new diffusion model into `vllm-omni`
-- `diffusion-perf-opt`: guides diffusion model performance optimization,
+- [`add-tts-model`](add-tts-model/SKILL.md): covers integration of new TTS
-  including profiling traces, parallel strategies, stage timing analysis, and
+  models and related serving workflows
-  benchmark-driven tuning
+- [`diffusion-perf-opt`](diffusion-perf-opt/SKILL.md): guides diffusion model
-- `quantization`: guides quantization method selection, model integration,
+  performance optimization, including profiling traces, parallel strategies,
-  checkpoint loading, and quality/performance validation for vLLM-Omni
+  stage timing analysis, and benchmark-driven tuning
-- `add-omni-model`: covers addition of new omni-modality model support
+- [`precheck-pr`](precheck-pr/SKILL.md): self-checks a branch before creating a
-- `add-tts-model`: covers integration of new TTS models and related serving
+  PR by validating title format, dead code, accuracy and performance claims,
-  workflows
+  and merge readiness
-- `generate-release-note`: helps prepare release notes for repository changes
+- [`quantization`](quantization/SKILL.md): guides quantization method selection,
-- `precheck-pr`: self-check a branch before creating a PR — validates PR title
+  model integration, checkpoint loading, and quality/performance validation
-  format, catches dead code, verifies accuracy/perf claims, and confirms merge
+  for vLLM-Omni
-  readiness
+- [`review-pr`](review-pr/SKILL.md): provides a frozen-snapshot, contract-aware
-- `vllm-omni-test`: guides generation and execution of CI-aligned tests (L1–L4),
+  workflow for maintainers and reviewers; contributors should use
-  pytest marker selection (`core_model` / `advanced_model` / `full_model`,
+  `precheck-pr` for self-review
-  `omni` / `tts` / `diffusion`), Buildkite wiring (`test-ready.yml`,
+- [`vllm-omni-npu-model-runner-upgrade`](vllm-omni-npu-upgrade/SKILL.md):
-  `test-merge.yml`, `test-nightly.yml`, `test-weekly.yml`), and copy-paste
+  upgrades NPU model runners to align with the latest vllm-ascend
-  local plus CI-like `pytest` commands; see `references/test-routing.md` for
+  `NPUModelRunner`
-  level-to-command mapping
+- [`vllm-omni-test`](vllm-omni-test/SKILL.md): guides generation and execution
-- `review-pr`: provides a structured workflow for reviewing pull requests
+  of CI-aligned tests (L1–L4), pytest marker selection (`core_model` /
-- `vllm-omni-npu-model-runner-upgrade`: upgrades NPU model runners to align with the
+  `advanced_model` / `full_model`, `omni` / `tts` / `diffusion`), Buildkite
-  latest vllm-ascend NPUModelRunner
+  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
 

docs/contributing/README.md

@@ -76,6 +76,47 @@ Please refer to the [test instructions](./ci/test_execution_guide.md) for detail
 !!! warning
     Currently, not all unit tests pass when run on CPU platforms. If you don't have access to a GPU platform to run unit tests locally, rely on the continuous integration system to run the tests for now.
 
+### Using repository skills with coding agents
+
+vLLM-Omni maintains [repository-scale skills](https://github.com/vllm-project/vllm-omni/tree/main/.claude/skills) for common coding, testing, performance, and review workflows. These skills capture repository-specific structure, validation requirements, and evidence standards that a general coding agent may not know.
+
+If your coding agent discovers repository skills automatically, ask it to use the relevant skill by name. Otherwise, point it to the linked `SKILL.md` and ask it to read the file before changing code. Skills can be combined: use a domain skill for the implementation, `vllm-omni-test` for test and CI decisions, and `precheck-pr` before opening the pull request.
+
+| Task | Repository skill |
+| --- | --- |
+| Add or extend a diffusion model | [`add-diffusion-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-diffusion-model/SKILL.md) |
+| Add or extend a text-to-speech model | [`add-tts-model`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/add-tts-model/SKILL.md) |
+| Add, select, or debug quantization | [`quantization`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/quantization/SKILL.md) |
+| Diagnose or optimize diffusion performance | [`diffusion-perf-opt`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/diffusion-perf-opt/SKILL.md) |
+| Upgrade an NPU model runner | [`vllm-omni-npu-model-runner-upgrade`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-npu-upgrade/SKILL.md) |
+| Add a regression test, choose L1-L4 coverage, or wire Buildkite | [`vllm-omni-test`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/vllm-omni-test/SKILL.md) |
+| Self-check a branch before opening a pull request | [`precheck-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/precheck-pr/SKILL.md) |
+| Perform a maintainer or reviewer review of an existing pull request or local branch | [`review-pr`](https://github.com/vllm-project/vllm-omni/blob/main/.claude/skills/review-pr/SKILL.md) |
+
+Use the following workflow when collaborating with a coding agent:
+
+1. **Ground the task.** Provide the issue, RFC, or design document that defines the expected behavior. Ask the agent to state its assumptions and identify the affected module or feature contracts before editing code.
+2. **Select the narrowest applicable skills.** Start with the domain skill that owns the change. Add `vllm-omni-test` whenever production behavior or tests change. For a bug fix, ask for the smallest reproducer and a regression test tied to the original symptom.
+3. **Require executable evidence.** Ask the agent to run the narrowest relevant checks and report the exact commands and results. It must identify tests it could not run because of unavailable hardware, model weights, credentials, or dependencies; an unrun check is not a pass.
+4. **Inspect the resulting diff.** Confirm that the implementation stays within the issue or RFC, does not overwrite unrelated work, and includes the required tests, documentation, and benchmark or accuracy evidence.
+5. **Run the pre-submit workflow.** Use `precheck-pr`, then run the applicable local tests and pre-commit hooks before opening the pull request.
+
+For example:
+
+```text
+Use add-diffusion-model and vllm-omni-test to implement the linked issue.
+Read the relevant design documents first, keep the change within the issue,
+and report every test command, result, and hardware validation gap.
+```
+
+```text
+Use vllm-omni-test to turn this bug reproducer into the smallest deterministic
+regression test. Prefer L1 CPU coverage; if the failure requires real weights
+or serving, explain the required L2/L3 environment and provide the exact command.
+```
+
+Repository skills guide the agent, but they do not replace contributor judgment, the accepted issue or RFC, required test evidence, or maintainer review. Review all generated changes before submitting them.
+
 ## Issues
 
 If you encounter a bug or have a feature request, please search existing issues first to see if it has already been reported. If not, please file a new issue, providing as much relevant information as possible.