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Agent skill

Using Model Endpoint

by JimLiu in JimLiu/science-skills

Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASEURL preloaded).

Apache-2.0Auto-check passedBackend & APIs

Install Using Model Endpoint

skills CLI
$ npx skills add JimLiu/science-skills --skill using-model-endpoint -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install JimLiu/science-skills using-model-endpoint --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/JimLiu/science-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/using-model-endpoint .claude/skills/using-model-endpoint && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
using-model-endpoint
GitHub stars
227
Used in
3 other repos
Token cost
~435 tokens
SKILL.md length
192 words
Files
4
Skills in repo
27
Repo updated
First seen
Licence
Apache-2.0

At a glance

Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASEURL preloaded).

  • Tasks that involve REST APIs
  • Runs Python scripts from its folder; needs INFER_API_KEY

What it does

Using Model Endpoint is an agent skill from JimLiu/science-skills. Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASEURL preloaded). Load once a task needs predictions from a registered model endpoint.

Its SKILL.md is about 440 tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `provider.json` and `provider.py`).

It sits in Backend & APIs, covering REST APIs. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve REST APIs

Example prompts

  • “/using-model-endpoint”

Requirements

  • Python 3
  • A credential in INFER_API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit fb309c3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • INFER_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Using Model Endpoint loads about 435 tokens when it runs. Until then it costs about 53 tokens; SKILL.md has 192 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~53
When it runs · the whole SKILL.md, loaded when a task matches
~435

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from JimLiu/science-skills at commit fb309c3, republished under its Apache-2.0 licence (© JimLiu). 192 words, ~435 tokens.

Download SKILL.mdSave it as .claude/skills/using-model-endpoint/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
using-model-endpoint
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.
license
Apache-2.0

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).

© JimLiu, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files in skills/using-model-endpoint of JimLiu/science-skills.

  • SKILL.md
  • provider.json
  • provider.py
  • requirements.lock

Open the folder on GitHubat commit fb309c3

Used in 3 other repositories

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in JimLiu/science-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Using Model Endpoint next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Using Model Endpoint compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Using Model Endpoint this skillJimLiu/science-skills2273 repos~435Automated safety check: PassApache-2.0
API DesignerJeffallan/claude-skills12k2 repos~2kAutomated safety check: PassMIT
Paperclippaperclipai/paperclip99k—~9.6kAutomated safety check: PassMIT
Nodejs Backend Patternsever-works/ever-works15818 repos~4kAutomated safety check: PassAGPL-3.0
OpenAPI to MCP Servermcp-use/mcp-use11k—~5.2kAutomated safety check: PassApache-2.0
Use Yaakmountain-loop/yaak19k—~1.9kAutomated safety check: PassMIT

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Categories

Questions about Using Model Endpoint

What does Using Model Endpoint do?

Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASEURL preloaded). Using Model Endpoint is an agent skill from JimLiu/science-skills. Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASEURL preloaded).

When should I use Using Model Endpoint?

Using Model Endpoint fits situations like: tasks that involve REST APIs.

How do I install Using Model Endpoint in Claude Code?

Run `npx skills add JimLiu/science-skills --skill using-model-endpoint -a claude-code`. Or copy the skill folder (skills/using-model-endpoint in JimLiu/science-skills) into .claude/skills/using-model-endpoint in your project. Claude Code loads it when a task matches its description.

How do I install Using Model Endpoint in Codex?

Run `npx skills add JimLiu/science-skills --skill using-model-endpoint -a codex`. Or copy the skill folder (skills/using-model-endpoint in JimLiu/science-skills) into .agents/skills/using-model-endpoint in your project. Codex loads it when a task matches its description.

Can I use Using Model Endpoint in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add JimLiu/science-skills --skill using-model-endpoint -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/using-model-endpoint, .gemini/skills/using-model-endpoint, .github/skills/using-model-endpoint and .opencode/skills/using-model-endpoint in your project.

What does Using Model Endpoint need to run?

Going by SKILL.md and its folder, Using Model Endpoint needs Python for the scripts in its folder and credentials named INFER_API_KEY. Our summary lists: Python 3; A credential in INFER_API_KEY.

Does Using Model Endpoint access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Using Model Endpoint safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Using Model Endpoint use?

Using Model Endpoint is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Using Model Endpoint use?

About 435 tokens (SKILL.md is roughly 1.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Using Model Endpoint?

Skills that share tags, products or a category with Using Model Endpoint: API Designer (Jeffallan/claude-skills, 12k stars), Paperclip (paperclipai/paperclip, 99k stars), Nodejs Backend Patterns (ever-works/ever-works, 158 stars) and OpenAPI to MCP Server (mcp-use/mcp-use, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Using Model Endpoint?

JimLiu (a GitHub user) maintains it in JimLiu/science-skills, which has 227 GitHub stars. The repository holds 27 skills in this directory. The repository was last updated on July 1, 2026.

Source: JimLiu/science-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.