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Build and run a model server locally via Docker Compose, then test it with curl. Use when the engineer wants to test a model server locally before committing.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/wikimedia/machinelearning-liftwing-inference-services --skill local-test命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Triage Wikimedia ML inference-services incidents at a time T (now or past). TRIGGER on "investigate / triage / debug / post-mortem / what happened" when the subject is a LiftWing alert, ML-serve namespace, error spike, latency regression, or pod crash — including generic phrasing ("investigate this issue") when context (Phabricator task, pasted alert, log snippet) points at LiftWing, even with no alert string. Always trigger on: LiftWingServiceErrorRate, KubernetesDeploymentUnavailableReplicas, KubernetesPodCrashLooping; any namespace under revscoring*, revertrisk*, articletopic-outlink, or other ML-serve; clusters ml-serve-eqiad, ml-serve-codfw, ml-staging-codfw. SKIP only when modifying code without diagnosing a runtime incident. Pulls Prometheus via Grafana's anonymous datasource proxy; correlates with Gerrit merges in `inference-services` and `deployment-charts` (Phab IDs from `Bug:` footers). Read-only.
Analyze a model server's Python code and its deployment chart to identify performance bottlenecks and optimization opportunities for CPU and GPU (AMD MI300X, ROCm, vLLM) inference services on KServe. Use when you want to improve inference throughput or latency for a model.
Scaffold a new LiftWing ML service in operations/deployment-charts. Handles both adding to an existing namespace (append inference_services entry) and creating a brand-new namespace (full helmfile scaffold). Use when an engineer wants to deploy a new inference service for the first time.
基于 SOC 职业分类
| name | local-test |
| description | Build and run a model server locally via Docker Compose, then test it with curl. Use when the engineer wants to test a model server locally before committing. |
| argument-hint | [service-name] |
| allowed-tools | ["Bash(docker *)","Bash(curl *)","Read"] |
Build, run, and test a model server locally.
.env file exists in the repo root with PATH_TO_<MODEL>_MODEL=/local/path/to/model.docker compose build <service>.docker compose up <service>.POST /v1/models/<model-name>:predict where <model-name> is the MODEL_NAME env var set for that service in docker-compose.yml. Check src/models/<service>/model_server/model.py for the expected input schema. Example for revert-risk language agnostic model: curl localhost:8080/v1/models/revertrisk-language-agnostic:predict -X POST -d '{"lang":"en","rev_id":12345}' -H "Content-type: application/json".Ctrl+C or docker compose down.platform: linux/amd64 to the service in docker-compose.yml.localhost:8080 by default. Check docker-compose.yml if the port is mapped differently. If :8080 is already in use, stop the other service or remap the port.https://analytics.wikimedia.org/published/wmf-ml-models/ and placed at the path referenced in .env.docker compose up -d <service>. Logs: docker compose logs <service>.$ARGUMENTS — the docker-compose service name (e.g., revertrisk-language-agnostic, articlequality, reference-need).