# Managed Model Endpoints

Register a model service in the managed family, a local model server container the...

- Repository: https://github.com/HughYau/AcademicForge (2620 stars)
- Category: Engineering
- License: Apache-2.0
- Last updated: 2026-08-30
- Runs scripts: no, instructions only

## Install

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

## Description

Register a model service in the managed family, a local model server container the daemon starts/stops on demand, or a remote upstream model API (https). Read the runbook, allocate a port (local only), compose idempotent start/stop scripts (local only), register once. Load when the user wants a model service available for inference, or when list_compute shows managed endpoints.

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

# Managed model endpoints

A **managed model endpoint** is a model service the **daemon** owns: you
register it **once**, then every `compute_provider` cell against it just
works, the daemon swaps the resident model off the device (one model at a
time, via the resident's own approved stop), runs your approved start
script, waits for the readiness route, then runs your cell, streaming its
lifecycle progress into the cell as it goes. You never run the container
runtime yourself, never poll readiness in cells, and never see the
credential value. Two verbs: `register()` (asks the user once) and ordinary
inference cells.

Container specifics, image, registry login, internal port, cache mount
target, readiness route, come from the **model's own runbook skill**; this
skill is the translation contract.

## Calling a registered endpoint, inference cells

Calling a registered endpoint, use the `using-model-endpoint` skill
(this skill is the REGISTRATION contract; that one documents the call
side in full).

The ONLY dispatch form is the `compute_provider` tool with the endpoint's
registered name (`list_compute` shows them):

```
compute_provider(provider="boltz2-service", code="""
import requests
r = requests.post(BASE_URL + "/v1/infer", json=payload)
""")
```

The daemon brings the model up on demand (a first cold start downloads
image + weights, minutes; let it run) and preloads `BASE_URL` into the
cell, both as a Python variable (use it directly, as above) and as
`os.environ["BASE_URL"]` (plus `INFER_API_KEY` for remote endpoints). Endpoints are
**not kernel environments**: `environment="boltz2-service"` on a plain
python cell fails, plain cells get no `BASE_URL`.

## Enablement, once per machine

The rest: https://github.com/HughYau/AcademicForge/blob/HEAD/skills/claude-science/managed-model-endpoints/SKILL.md