How to Configure SkillSpector Provider Credentials for Anthropic, NVIDIA, and OpenAI

SkillSpector obtains LLM provider credentials exclusively from environment variables, using SKILLSPECTOR_PROVIDER to select between Anthropic, OpenAI, or NVIDIA, while requiring provider-specific API keys such as ANTHROPIC_API_KEY or OPENAI_API_KEY to authenticate requests.

NVIDIA's SkillSpector is an open-source codebase analysis tool that integrates with multiple large language model providers. To configure SkillSpector provider credentials, you must export environment variables that specify your chosen provider and supply the corresponding API authentication tokens.

Supported LLM Providers and Required Credentials

SkillSpector supports three LLM providers, each requiring specific environment variables for authentication.

Anthropic Configuration

The Anthropic provider requires the ANTHROPIC_API_KEY environment variable. This key authenticates requests to api.anthropic.com.

• Required variable: ANTHROPIC_API_KEY
• Optional variables: None (base URL is fixed)
• Default models: claude-opus-4-6 for analysis tasks, claude-sonnet-4-6 for the meta-analyzer

In src/skillspector/providers/anthropic/provider.py, the credential resolution follows this pattern:

api_key = os.environ.get("ANTHROPIC_API_KEY", "").strip()
if not api_key:
    return None

OpenAI Configuration

The OpenAI provider accepts OPENAI_API_KEY for standard OpenAI endpoints, with optional support for custom base URLs via OPENAI_BASE_URL.

• Required variable: OPENAI_API_KEY
• Optional variable: OPENAI_BASE_URL (for Azure OpenAI or custom endpoints)
• Default model: gpt-4-turbo-1106

In src/skillspector/providers/openai/provider.py, the implementation reads:

api_key = os.environ.get("OPENAI_API_KEY", "").strip()
base_url = os.environ.get("OPENAI_BASE_URL")

NVIDIA Configuration

The NVIDIA provider uses internal infrastructure credentials rather than standard API keys.

• Required variable: NV_BUILD_API_KEY (used internally)
• Model source: Loaded from model_registry.yaml
• Note: No explicit key configuration is required for standard usage

Setting the Active Provider with SKILLSPECTOR_PROVIDER

Set SKILLSPECTOR_PROVIDER to specify which LLM backend to use. Valid values are anthropic, openai, or nv_build.

export SKILLSPECTOR_PROVIDER=anthropic

# or

export SKILLSPECTOR_PROVIDER=openai

# or

export SKILLSPECTOR_PROVIDER=nv_build

If the selected provider cannot locate its required API key, SkillSpector falls back to the next available provider in the order Anthropic → OpenAI, as implemented in src/skillspector/providers/__init__.py and src/skillspector/llm_utils.py. If no valid credentials are found for any provider, the library raises NO_LLM_API_KEY_MESSAGE.

Using a .env File vs Shell Environment Variables

You can configure credentials via shell exports or a .env file placed in the repository root. The CLI automatically loads variables from .env using python-dotenv.

Shell configuration:

export SKILLSPECTOR_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxx

.env file configuration:

SKILLSPECTOR_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-xxxxxxxxxxxx
OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxx
OPENAI_BASE_URL=https://my-azure-openai.openai.azure.com/

How Credential Resolution Works in the Source Code

The credential resolution follows a specific hierarchy defined in the provider initialization code.

Provider Resolution Logic

In src/skillspector/providers/__init__.py, the system implements fallback logic:

1. Check if SKILLSPECTOR_PROVIDER is set and valid
2. Attempt to load credentials for the specified provider
3. If credentials are missing, fall back to Anthropic, then OpenAI
4. Raise NO_LLM_API_KEY_MESSAGE if no credentials are found

Anthropic Implementation

Located in src/skillspector/providers/anthropic/provider.py, the code validates the API key before instantiating ChatAnthropic:

api_key = os.environ.get("ANTHROPIC_API_KEY", "").strip()
if not api_key:
    return None

# Builds ChatAnthropic instance with validated key

OpenAI Implementation

In src/skillspector/providers/openai/provider.py, the provider handles both standard and custom endpoints:

api_key = os.environ.get("OPENAI_API_KEY", "").strip()
base_url = os.environ.get("OPENAI_BASE_URL")
if not api_key:
    return None

# Uses base_url if provided, otherwise uses default OpenAI endpoint

Complete Configuration Examples

Configure SkillSpector with Anthropic

import os
os.environ["SKILLSPECTOR_PROVIDER"] = "anthropic"
os.environ["ANTHROPIC_API_KEY"] = "sk-ant-xxxxxxxxxxxx"

from skillspector.cli import main
main()

Configure SkillSpector with OpenAI and Azure

import os
os.environ.update({
    "SKILLSPECTOR_PROVIDER": "openai",
    "OPENAI_API_KEY": "sk-xxxxxxxxxxxx",
    "OPENAI_BASE_URL": "https://my-azure-openai.openai.azure.com/",
})
from skillspector.cli import main
main()

Programmatic Credential Resolution

from skillspector.providers import resolve_provider_credentials

api_key, base_url = resolve_provider_credentials()
print(f"Using API key: {api_key[:6]}…")
print(f"Base URL: {base_url or 'default'}")

Bash Setup for NVIDIA Provider

export SKILLSPECTOR_PROVIDER=nv_build
export NV_BUILD_API_KEY=your-internal-key

Summary

• SkillSpector provider credentials are configured exclusively through environment variables, with no support for hardcoded configuration files.
• Set SKILLSPECTOR_PROVIDER to anthropic, openai, or nv_build to select your LLM backend.
• Provide ANTHROPIC_API_KEY for Anthropic, OPENAI_API_KEY (and optionally OPENAI_BASE_URL) for OpenAI, or NV_BUILD_API_KEY for NVIDIA.
• The system automatically falls back from Anthropic to OpenAI if the primary provider's credentials are missing.
• Place a .env file in the repository root for automatic loading, or export variables directly in your shell.

Frequently Asked Questions

Can I use Azure OpenAI with SkillSpector?

Yes. Set SKILLSPECTOR_PROVIDER=openai and export both OPENAI_API_KEY and OPENAI_BASE_URL pointing to your Azure endpoint. In src/skillspector/providers/openai/provider.py, the code reads OPENAI_BASE_URL and passes it to the underlying client, enabling compatibility with Azure OpenAI Service deployments.

What happens if I don't set SKILLSPECTOR_PROVIDER?

If SKILLSPECTOR_PROVIDER is not set, or if the specified provider lacks valid credentials, SkillSpector attempts to resolve available providers in the fallback order Anthropic → OpenAI. According to src/skillspector/llm_utils.py, the system checks for ANTHROPIC_API_KEY first, then OPENAI_API_KEY. If neither exists, it raises NO_LLM_API_KEY_MESSAGE.

Why does SkillSpector use different default models for Anthropic?

Anthropic uses claude-opus-4-6 for analysis tasks and claude-sonnet-4-6 for the meta-analyzer, as defined in the provider configuration. OpenAI defaults to gpt-4-turbo-1106 for all operations. These defaults are baked into the respective provider classes in src/skillspector/providers/anthropic/provider.py and src/skillspector/providers/openai/provider.py.

How do I verify my credentials are loaded correctly?

You can programmatically verify credential resolution using the internal utility:

from skillspector.providers import resolve_provider_credentials
api_key, base_url = resolve_provider_credentials()

If api_key returns None or raises an exception, your environment variables are not set correctly. Ensure your .env file is in the repository root or that variables are exported in your current shell session before importing SkillSpector modules.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}