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Using model endpoint

Skill BioTender-max/awesome-bio-agent-skills/skills/claude-science/using-model-endpoint

A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design.

Install
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill using-model-endpoint

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What its author says it does

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Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASE_URL preloaded). Load once a task needs predictions from a registered model endpoint.

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

1.7 KB, as published. Nobody here has run it

You are a pure HTTP client of BASE_URL. Each registered model endpoint gets its own inference kernel — a Python REPL whose network egress is scoped to exactly that endpoint — reached via compute_provider({'provider': '<slug>', 'code': '…'}) (<slug> from list_compute, without the infer: prefix).

  • BASE_URL is preloaded (as a Python variable AND as os.environ["BASE_URL"]) — build request URLs from it, never hardcode hosts/ports. Call the model's native API with httpx (preinstalled) or requests; request shapes live in the provider's own runbook skill (the registration's skillName).
  • Hosted endpoints: send Authorization: Bearer $INFER_API_KEY (always the canonical env name when a credential is delivered; the credential's own name is usually aliased too). Local endpoints need no auth header.
  • Requests ride the sandbox HTTP proxy (HTTP_PROXY/HTTPS_PROXY are set) — don't disable it (e.g. trust_env=False) or the endpoint is unreachable.
  • No job lifecycle here (no submit/harvest) — direct request/response only.

Managed endpoints (entries with managed: true / a location field in list_compute): their lifecycle — daemon-owned start/stop, registration, free_port()/register() — lives in the managed-model-endpoints skill. Cells against them are still just HTTP calls to BASE_URL; the daemon brings the model up on demand (a cold start streams its progress into your cell and can take minutes).

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