An AI agent skill designed to resolve complex support queries about LLM availability, regional hosting, and capability differences across major cloud providers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install model-provider-support
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install model-provider-support using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Model Provider Support?
The Model Provider Support skill is a specialized utility designed for AI assistants to accurately answer team questions regarding model availability across different hosting platforms. Whether navigating Claude on AWS Bedrock, GPT models on Azure, or Gemini on Google Cloud Vertex AI, this skill ensures that answers are grounded strictly in official, current documentation. By preventing hallucinated cross-provider features, developers can maintain an accurate matrix of hosted model endpoints.
By integrating this tool into your Openclaw Skills workspace, you eliminate the guesswork associated with multi-cloud deployments of large language models. The system relies on local structured registries and taxonomies to validate capabilities, providing a reliable source of truth for technical and architecture teams.
Model Provider Support Use Cases
- Verifying if a specific Claude model is available in AWS Bedrock's us-east-1 region.
- Answering developer questions about Azure OpenAI feature parity versus direct OpenAI API capabilities.
- Validating Vertex AI regional support for Gemini model families.
- Reviewing official API support documentation during cloud migration planning.
How Model Provider Support Works
- Identify the specific model family, hosting provider, target region, and API capability required.
- Load the local references located in references/source-registry.json along with any provider-specific notes.
- Query the references/capability-taxonomy.md file to determine the correct status labels for the requested capability.
- Apply provider-specific overrides and nuances found in references/provider-differences.md before returning the answer.
- Fall back to a conservative, unverified state (unknown_needs_live_check) if live documentation is unreachable, citing the fallback registry URL.
Model Provider Support Setup
To initialize and run this workspace skill, ensure you have Node.js installed to perform registry validation. Follow these steps to set up the environment:
# Navigate to your workspace directory
cd tools/model-provider-support
# Validate the source registry and check reference sources
node scripts/check-sources.mjs
Add this skill directory to your active AI environment configuration to load it among your configured Openclaw Skills.
Model Provider Support Data Schema & Taxonomy
The skill relies on a structured repository schema to map provider capabilities accurately. Below is the configuration structure:
| File Path | Purpose | Format |
|---|---|---|
references/source-registry.json |
Stores the primary URLs, provider metadata, and official reference endpoints. | JSON |
references/capability-taxonomy.md |
Defines the status labels used to classify model features and regional readiness. | Markdown |
references/provider-differences.md |
Documents differences in feature parity, rate limits, and endpoint behaviors. | Markdown |
scripts/check-sources.mjs |
Validation script to verify that all registry sources remain active and correctly formatted. | ESM JavaScript |
All modifications to the registry must be validated using the provided check script.
Model Provider Support Advanced Features
- Strict separation of first-party model capabilities from hosting provider features to avoid inaccurate support answers.
- Built-in verification script using Node.js to automatically check and validate all online documentation sources.
- Conservative fallback handling that labels uncertain capabilities as unknown_needs_live_check rather than risking hallucinated data.
- Easy extensibility for other Openclaw Skills tracking custom internal endpoints and private model deployments.
SKILL.md
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