executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
serving-and-cliLaunch and operate Xinference services and model lifecycles through public CLI surfaces with safe command templates, placement notes, and troubleshooting handoffs.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when you need to start a local service, stand up a supervisor/worker cluster, or build safe command lines for model launch and lifecycle management.
xinference-local.xinference-supervisor and xinference-worker.xinference service commands for:
launchlistterminateregisterunregisterregistrationscachedremove-cacheenginecal-model-memvllm-modelsstop-clusterlogin--endpoint / -e--model-name, --model-uid, --model-type--n-worker, --n-gpu, --replica--worker-ip, --gpu-idx--enable-virtual-env, --disable-virtual-env--virtual-env-package, --env--api-key placeholdersclient-and-apimodels-and-backendsoperations-and-securityreferences/cli-reference.md for exact command shapes.scripts/render_xinference_commands.py to generate copy-paste-safe templates.references/serving-workflows.md for local, distributed, launch, and shutdown flows.references/distributed-deployment.md for host/port and worker placement rules.references/troubleshooting.md when a command fails, blocks, or returns a validation error.launch requires --model-engine for LLMs.IP:port values.--gpu-idx is a comma-separated list of integers.--key value pairs after the standard launch flags.--enable-virtual-env or --disable-virtual-env, never both.