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add-provider

Scaffold LLM provider integrations by updating registries, configuration, and tests.

Updated Aug 23, 2026
Search Tags:#configuration#environment variables#llm providers#model tiering#provider registry#pytest scaffolding#async clients
One-click install
npx skills add https://github.com/SalesTeamToolbox/frood --skill add-provider-salesteamtoolbox
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: add-provider
Source: https://github.com/SalesTeamToolbox/frood/tree/main/.claude/skills/add-provider
Command: npx skills add https://github.com/SalesTeamToolbox/frood --skill add-provider-salesteamtoolbox

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the repetitive, error-prone work of wiring a new LLM provider into an existing agent platform across registry, configuration, environment variables, and tests.

Core Features & Use Cases

  • Provider registry scaffolding: Adds the new provider to the ProviderType enum, registers the ProviderSpec, and registers one or more ModelSpec entries with tiers and token limits.
  • Configuration wiring: Updates core/config.py by adding the provider API key field to Settings and loading it via from_env().
  • Project readiness checks: Updates .env.example with the correct placeholders and generates a pytest suite to verify enum, registry, model specs, and client creation behavior for the new provider.

Quick Start

Ask the skill to scaffold a new provider integration by providing the provider name, base URL, API key env var, display name, model IDs to register (including tier and token limits), and the default model ID.

Frequently Asked Questions about add-provider

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add a new LLM provider to my agent orchestration platform?▼

Scaffolding a new LLM provider integration registers the ProviderType enum, ProviderSpec, and ModelSpec entries while wiring environment-based configuration for API keys and generating pytest validations.

What is LLM provider registry scaffolding and how does it work?▼

Provider registry scaffolding adds a new provider to the ProviderType enum, registers the ProviderSpec, and registers ModelSpec entries with tiering and token capacity constraints to formalize backend availability.

How do I configure environment variables for a new LLM backend integration?▼

Configuring environment variables involves updating the Settings class in core/config.py to load the new API key via from_env(), and extending the .env.example file with the correct placeholder values.

Can I register multiple model tiers with different token limits for a single LLM provider?▼

Yes, you can register one or more ModelSpec entries for a single provider, specifying distinct model IDs, tiering classifications, and token capacity constraints during the provider scaffolding process.

What is the best way to generate pytest scaffolding for an AsyncOpenAI client?▼

The best way to generate pytest scaffolding is to automatically create a test suite that validates the provider enum, registry, model specs, and AsyncOpenAI client creation behavior during the integration process.

Does scaffolding an LLM provider integration require manual updates to the .env.example file?▼

No, scaffolding an LLM provider integration automatically extends the .env.example file with the correct API key placeholders, ensuring project readiness checks pass without manual environment file edits.

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