# Managed Model Endpoints

> Register or audit Feynman-managed model endpoints. Use when a research workflow needs a local or remote model service, endpoint health checks, credential refs, startup scripts, or inference routing.

- Skill: `advaitpaliwal/managed-model-endpoints` (Agent Skill)
- Install (CLI): `npx skillmds@latest add advaitpaliwal/managed-model-endpoints`
- Raw SKILL.md: https://api.skillmd.com/api/skills/advaitpaliwal/managed-model-endpoints/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: advaitpaliwal (https://skillmd.com/u/advaitpaliwal)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/advaitpaliwal/managed-model-endpoints

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# Managed Model Endpoints

Use this skill to make a model endpoint usable from Feynman.

Workflow:

1. Define the endpoint purpose, model family, input/output schema, auth method, hardware need, and expected latency.
2. Record whether the endpoint is local, remote HTTP, Modal-backed, SSH-backed, or a custom connector.
3. Add only non-secret endpoint metadata to settings. Store secret references as environment variable names or credential refs.
4. Implement start/stop/health/inference checks when Feynman owns the endpoint lifecycle.
5. Run a tiny inference smoke and save request/response shape without leaking secrets.

Expose endpoints as research infrastructure, not as permanent claims that a model is installed when health has not passed.