Managed Model Endpoints Agent Skill
Explain Wisp's current managed-model endpoint boundary and plan a safe integration. Use when the user asks to register, start, stop, tunnel, authenticate, or manage a persistent inference service.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/xuzhougeng/wisp-science --skill managed-model-endpointsThe instruction itself
2 sections, as written by the authorManaged model endpoint boundary
Wisp does not currently expose an endpoint registry or a service-lifecycle
backend. The Agent cannot allocate ports, configure tunnels, read secrets,
register health checks, or start and stop a persistent inference service
through Python.
Do not model service startup as a normal run_in_context command: a Run tracks
one process lifecycle, while a managed endpoint also needs a durable endpoint
identity, health, routing, authentication, restart policy, and ownership.
Supported path
If the user already operates an endpoint outside Wisp and the selected local,
WSL, or SSH context can reach it using credentials already configured in that
execution environment, load using-model-endpoint to run a bounded inference
client. Never request or print secret values merely to make the call.
Otherwise explain that endpoint registration and service management are not
available in this Wisp build. A future implementation should add a typed service
or execution-context backend with keyring-backed secret binding, health checks,
start/stop/recovery semantics, and auditable invocation Runs.
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How to use it
Take xuzhougeng/managed-model-endpoints from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.