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Using Model Endpoint

CommunityPopular
HughYau
using-model-endpoint

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.

Overview

PublisherHughYau
RepositoryAcademicForge
Skill nameusing-model-endpoint
Stars
2.6K
Forks
152
Bundled files
3
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by HughYau on GitHub. Read the source before you install it.

Installation

Install the Using Model Endpoint AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HughYau/AcademicForge.git /tmp/AcademicForge
mkdir -p .claude/skills
cp -r /tmp/AcademicForge/skills/claude-science/using-model-endpoint .claude/skills/using-model-endpoint
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Model Endpoint in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Using Model Endpoint on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Using Model Endpoint is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Using Model Endpoint AI skill do?

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.

Why use Using Model Endpoint on TypingMind?

Because you install it once and use it with any model. Using Model Endpoint is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Using Model Endpoint in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HughYau/AcademicForge/tree/site-first/skills/claude-science/using-model-endpoint. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Using Model Endpoint?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Using Model Endpoint?

As many as you like. As long as a model supports skills, you can use Using Model Endpoint with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Using Model Endpoint AI skill free?

Yes. It is published on GitHub by HughYau under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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