Using model endpoint

Skill xuzhougeng/wisp-science/skills/using-model-endpoint

Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models.

Install

npx -y skills add xuzhougeng/wisp-science --skill using-model-endpoint

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What its author says it does

Copied from the file, not written here

Invoke an already configured model endpoint from a supported Wisp execution context and capture the bounded inference as a Run. Use only when the endpoint URL and authentication are already available inside that context; this skill does not register or manage services.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.4 KB, as published. Nobody here has run it

Use an existing model endpoint

Wisp can record a bounded client invocation as a Run, but it does not register or manage the endpoint. Require all of the following:

a selected local, wsl:<distro>, or ssh:<alias> context;
a concrete endpoint URL reachable from that context;
authentication already configured by the user in that execution environment or the endpoint client's own external configuration;
a documented request and response schema;
a finite request timeout and a concrete output path.

Do not ask the user to paste secrets into the command, project files, or chat. Wisp exposes no credential accessor to the Agent and does not inject keyring values into run_in_context commands.

Invocation workflow

Write a small deterministic client such as runs/call_endpoint.py. Read the URL and credential variable names at runtime; never embed secret values.
Validate its request against the endpoint's documented schema.
For SSH, stage the client and small inputs with input_paths. Keep large inputs at an existing absolute remote path.
Submit one invocation with run_in_context and register the response with output_specs:

{
  "context_id": "ssh:gpu-box",
  "title": "Existing endpoint inference",
  "command": "source ~/miniforge3/etc/profile.d/conda.sh && conda activate endpoint-client && python call_endpoint.py --input request.json --output /home/me/wisp-results/endpoint/response.json",
  "timeout_secs": 300,
  "input_paths": ["runs/call_endpoint.py", "data/request.json"],
  "output_specs": [
    {
      "glob": "ssh://gpu-box/home/me/wisp-results/endpoint/response.json",
      "kind": "json",
      "residency": "remote"
    }
  ]
}

Replace all example context and paths. Call monitor_run once when waiting is useful, get_run once for a snapshot, or cancel_run to stop.

Local and WSL Runs are capped at 300 seconds and do not accept input_paths. Keep their client and outputs in host-visible project paths. If endpoint setup, tunnelling, health management, or deployment is required, stop and load managed-model-endpoints for the explicit current boundary.

What its author says it does

SKILL.md

Use an existing model endpoint

Invocation workflow

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