# Using Model Endpoint

Call a registered model endpoint over its native HTTP API from the endpoint's scoped...

- Repository: https://github.com/HughYau/AcademicForge (2620 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-08-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add HughYau/AcademicForge --skill using-model-endpoint -g -a claude-code -y`
Codex: `npx skills add HughYau/AcademicForge --skill using-model-endpoint -g -a codex -y`
Cursor: `npx skills add HughYau/AcademicForge --skill using-model-endpoint -g -a cursor -y`
Gemini CLI: `npx skills add HughYau/AcademicForge --skill using-model-endpoint -g -a gemini-cli -y`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

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).