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Agent skill

LobeHub Model Provider Integration

by lobehub in lobehub/lobehub

Adds an AI model provider to LobeHub end to end: runtime integration, model cards, configuration, branding and documentation, or documentation-only updates.

Custom licenceAuto-check passedAI & LLM Engineering

Install LobeHub Model Provider Integration

skills CLI
$ npx skills add lobehub/lobehub --skill add-model-provider -a claude-code

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

GitHub CLI
$ gh skill install lobehub/lobehub add-model-provider --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/lobehub/lobehub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/add-model-provider .claude/skills/add-model-provider && 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
add-model-provider
GitHub stars
83k
Token cost
~2.8k tokens
SKILL.md length
1,457 words
Files
5 (incl. references)
Skills in repo
50
Repo updated
First seen
Licence
Custom licence

At a glance

Adds an AI model provider to LobeHub end to end: runtime integration, model cards, configuration, branding and documentation, or documentation-only updates.

  • Works in 6 steps: Register the provider ID, provider card,… → Set a usable static checkModel when the… → Reuse the appropriate runtime factory or… → …
  • Integrating a new AI model provider into the LobeHub codebase
  • SKILL.md covers Establish the Integration…, Implement the Provider, Branding, Ordering, and… and Validate the User Path
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The goal is a provider that users can configure, recognize and use through the supported API paths, and the skill includes the provider documentation workflow. It begins by separating a new provider from a model added to an existing one, and by recording a stable provider ID, display name, model IDs, authentication method, endpoint and supported API modes.

Research comes before code. The agent reads the official announcement, API reference, model catalog, pricing and migration notes, records source URLs and access dates, and verifies the endpoint actually being integrated, since advertised model capabilities do not prove that a compatible endpoint supports search, tools, images or structured output. A third-party endpoint is never described as the provider's official API.

Scope is kept narrow. Documentation-only requests follow references/documentation.md without adding runtime code, and a model-only request on an existing provider uses the model-bank-metadata skill for knowledgeCutoff, family and generation and skips provider registration and authentication changes. Reference files cover the integration map and local providers, and private deployment settings must not be copied into an open-source change.

When your agent uses it

  • Integrating a new AI model provider into the LobeHub codebase
  • Updating provider documentation after a configuration change
  • Adding model cards for a provider that is already registered

Example prompts

  • “Add Mistral as a new provider in LobeHub, including model cards, branding and docs.”
  • “Update the provider docs for DeepSeek to match what the config now supports.”
  • “Add the newest Gemini model to the existing Google provider, with capabilities and pricing.”

Requirements

  • A checkout of the LobeHub repository

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Register the provider ID, provider card, separate model cards, model imports/map/exports, and package subpath export. Keep chatModels: []…
  2. Set a usable static checkModel when the provider guarantees that model is available. Otherwise, omit it and let the user select and…
  3. Reuse the appropriate runtime factory or adapter. Register the runtime and its public export. If the provider needs a distinct routed API…
  4. Validate protocol selection separately for streaming chat, non-streaming chat, and structured generation. Responses-only providers may…
  5. Add authentication/configuration plumbing appropriate to the provider. For API-key providers, check both environment schema and runtime…
  6. For a new user-facing parameter, trace the model-card type, stored chat configuration, controls, request resolver, and translations…

What it can do on your machine

Read from SKILL.md and the folder at commit 35d442e. 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

    No scripts in the folder and no shell commands in SKILL.md.

    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 no API keys, tokens, secrets or passwords.

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

Context cost

LobeHub Model Provider Integration loads about 2.8k tokens when it runs, and up to ~9.4k if it reads all its reference files. Until then it costs about 71 tokens; SKILL.md has 1,457 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~9.4k

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

Its licence (Custom licence) doesn't allow us to republish the file, so here is its outline and opening line. It has 1,457 words (~2,828 tokens).

“Deliver a provider that users can configure, recognize, and use through the supported API paths. This skill includes the provider documentation workflow.”

— opening of SKILL.md by lobehub, Custom licence
name
add-model-provider
disable-model-invocation
true
argument-hint
[provider-name]

Read the full SKILL.md on GitHub

Files

SKILL.md and 4 other files (references) in .agents/skills/add-model-provider of lobehub/lobehub.

  • SKILL.md
  • agents/openai.yaml
  • references/documentation.md
  • references/integration-map.md
  • references/local-providers.md

Open the folder on GitHubat commit 35d442e

Compare with similar skills

LobeHub Model Provider Integration 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.

LobeHub Model Provider Integration compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
LobeHub Model Provider Integration this skilllobehub/lobehub83k—~2.8kAutomated safety check: PassCustom licence
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Cc GuidemikeOnBreeze/cc-crossbeam293—~668Automated safety check: PassMIT
Docstringpytorch/pytorch104k2 repos~2.6kAutomated safety check: PassCustom licence
AI Research Reproductionlllllllama/RigorPilot-Skills4971 repos~1.8kAutomated safety check: PassMIT
Cursor BYOK Prefix Stabilityleookun/cursor-byok3.2k—~1.3kAutomated safety check: PassMIT

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Categories

Questions about LobeHub Model Provider Integration

What does LobeHub Model Provider Integration do?

Adds an AI model provider to LobeHub end to end: runtime integration, model cards, configuration, branding and documentation, or documentation-only updates. The goal is a provider that users can configure, recognize and use through the supported API paths, and the skill includes the provider documentation workflow. It begins by separating a new provider from a model added to an existing one, and by recording a stable provider ID, display name, model IDs, authentication method, endpoint and supported API modes.

When should I use LobeHub Model Provider Integration?

LobeHub Model Provider Integration fits situations like: integrating a new AI model provider into the LobeHub codebase; updating provider documentation after a configuration change; adding model cards for a provider that is already registered.

How do I install LobeHub Model Provider Integration in Claude Code?

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

How do I install LobeHub Model Provider Integration in Codex?

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

Can I use LobeHub Model Provider Integration 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 lobehub/lobehub --skill add-model-provider -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/add-model-provider, .gemini/skills/add-model-provider, .github/skills/add-model-provider and .opencode/skills/add-model-provider in your project.

What does LobeHub Model Provider Integration need to run?

SKILL.md names no scripts, command-line tools or credentials: LobeHub Model Provider Integration is instructions for the agent only. Our summary lists: A checkout of the LobeHub repository.

Does LobeHub Model Provider Integration 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 LobeHub Model Provider Integration 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 LobeHub Model Provider Integration use?

LobeHub Model Provider Integration has a licence file (the repository's licence) that doesn't match a standard licence. Read it on GitHub before reusing the skill.

How many tokens does LobeHub Model Provider Integration use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.5k tokens, read only when the agent opens those files.

What are the alternatives to LobeHub Model Provider Integration?

Skills that share tags, products or a category with LobeHub Model Provider Integration: Get API Docs (sudomakes/backroad, 162 stars), Cc Guide (mikeOnBreeze/cc-crossbeam, 293 stars), Docstring (pytorch/pytorch, 104k stars) and AI Research Reproduction (lllllllama/RigorPilot-Skills, 497 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains LobeHub Model Provider Integration?

lobehub (a GitHub organization) maintains it in lobehub/lobehub, which has 83,074 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on October 9, 2026.

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