Which LLM Providers Are Supported by SkillSpector? OpenAI, Anthropic, and NVIDIA Build

SkillSpector supports three built-in LLM providers—OpenAI, Anthropic, and NVIDIA Build—each implementing a common interface defined in src/skillspector/providers/base.py to enable uniform credential resolution and model registry access for the LLM Analyzer node.

NVIDIA SkillSpector is an open-source framework for automated skill analysis that integrates with multiple LLM backends through a pluggable provider architecture. Understanding which LLM providers are supported by SkillSpector helps developers configure the LLM Analyzer node for evaluating code against specific criteria. The framework currently ships with production-ready implementations for OpenAI, Anthropic, and NVIDIA Build services.

Built-In SkillSpector LLM Providers

OpenAI Provider

The OpenAI Provider is implemented in src/skillspector/providers/openai/provider.py via the OpenAIProvider class. This provider connects to the OpenAI API using credentials from the OPENAI_API_KEY environment variable and defaults to the gpt-5.4 model when SKILLSPECTOR_MODEL is unspecified.

Anthropic Provider

The Anthropic Provider resides in src/skillspector/providers/anthropic/provider.py as the AnthropicProvider class. It queries the Anthropic API using ANTHROPIC_API_KEY and defaults to claude-3-sonnet-20240229 for analysis tasks.

NVIDIA Build Provider

The NVIDIA Build Provider is defined in src/skillspector/providers/nv_build/provider.py through the NVBuildProvider class. This provider interfaces with NVIDIA's Build (NV-ML) service, reading credentials from NV_BUILD_API_KEY to enable on-premise or cloud-hosted NVIDIA model inference.

How SkillSpector Discovers and Manages Providers

All providers implement the abstract base class defined in src/skillspector/providers/base.py, ensuring consistent methods for resolve_credentials(), resolve_model(), get_context_length(), and get_max_output_tokens(). The ProviderRegistry in src/skillspector/providers/__init__.py automatically discovers and registers these three providers at runtime, making them available to the LLM Analyzer node without manual configuration.

Credential Resolution and Model Registry

Each provider follows a standardized credential resolution pattern. They read environment variables—OPENAI_API_KEY, ANTHROPIC_API_KEY, or NV_BUILD_API_KEY—and return None if credentials are missing, causing the LLM Analyzer to skip that backend. For model metadata, each provider ships a model_registry.yaml file mapping model identifiers to context lengths and output token limits. The helper functions in src/skillspector/providers/registry.py cache this data for fast lookup during analysis runs.

If the user does not specify a model via the SKILLSPECTOR_MODEL environment variable, the provider falls back to hard-coded defaults specific to each service.

Code Examples: Using SkillSpector LLM Providers

The following examples demonstrate how to instantiate each provider and query their configuration:

OpenAI Provider Example

from skillspector.providers.openai.provider import OpenAIProvider

openai = OpenAIProvider()

# Resolve API key and optional custom endpoint

creds = openai.resolve_credentials()
if creds:
    api_key, base_url = creds
    print("OpenAI key:", api_key[:4] + "…" )
    print("Base URL:", base_url or "default (api.openai.com)")

# Get model information

model = openai.resolve_model()
ctx_len = openai.get_context_length(model)
max_out = openai.get_max_output_tokens(model)

print(f"Using model {model}: context={ctx_len}, max_output={max_out}")

Anthropic Provider Example

from skillspector.providers.anthropic.provider import AnthropicProvider

anthropic = AnthropicProvider()

creds = anthropic.resolve_credentials()
if creds:
    api_key, base_url = creds
    print("Anthropic key:", api_key[:4] + "…")
    print("Base URL:", base_url or "default (api.anthropic.com)")

model = anthropic.resolve_model()
ctx_len = anthropic.get_context_length(model)
max_out = anthropic.get_max_output_tokens(model)

print(f"Anthropic model {model}: context={ctx_len}, max_output={max_out}")

NVIDIA Build Provider Example

from skillspector.providers.nv_build.provider import NVBuildProvider

nv = NVBuildProvider()

creds = nv.resolve_credentials()
if creds:
    api_key, base_url = creds
    print("NV Build key:", api_key[:4] + "…")
    print("Endpoint:", base_url)

model = nv.resolve_model()
ctx_len = nv.get_context_length(model)
max_out = nv.get_max_output_tokens(model)

print(f"NV Build model {model}: context={ctx_len}, max_output={max_out}")

All three snippets follow the same pattern: instantiate the provider, pull credentials from the environment, and query the model registry for token limits.

Summary

• SkillSpector supports three LLM providers: OpenAI, Anthropic, and NVIDIA Build, each implemented in src/skillspector/providers/<name>/provider.py.
• Automatic discovery: The ProviderRegistry in src/skillspector/providers/__init__.py registers providers automatically for the LLM Analyzer node.
• Unified interface: All providers inherit from the base class in src/skillspector/providers/base.py and implement resolve_credentials(), resolve_model(), and registry lookup methods.
• Environment-based configuration: API keys are read from OPENAI_API_KEY, ANTHROPIC_API_KEY, and NV_BUILD_API_KEY respectively.

Frequently Asked Questions

How do I configure API keys for SkillSpector LLM providers?

SkillSpector reads API credentials from environment variables specific to each backend: set OPENAI_API_KEY for OpenAI, ANTHROPIC_API_KEY for Anthropic, and NV_BUILD_API_KEY for NVIDIA Build. If a required variable is missing, the provider returns None and the LLM Analyzer skips that backend.

What are the default models for each SkillSpector LLM provider?

When SKILLSPECTOR_MODEL is not specified, OpenAI defaults to gpt-5.4, Anthropic defaults to claude-3-sonnet-20240229, and NVIDIA Build uses its internal default as defined in the provider's model_registry.yaml file.

How does SkillSpector discover available LLM providers?

The framework automatically discovers providers through the ProviderRegistry class defined in src/skillspector/providers/__init__.py, which imports and registers OpenAIProvider, AnthropicProvider, and NVBuildProvider at runtime.

Can I use custom base URLs or endpoints with SkillSpector providers?

Yes, the resolve_credentials() method returns an optional base_url parameter alongside the API key, allowing you to override the default endpoint for OpenAI, Anthropic, or NVIDIA Build services through the credentials tuple.

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