How to Configure SkillSpector to Use Local LLMs Like Ollama: A Complete Guide

Configure SkillSpector to use local LLMs like Ollama by setting SKILLSPECTOR_PROVIDER=openai, pointing OPENAI_BASE_URL to your local server endpoint (e.g., http://localhost:11434/v1), and providing any non-empty string for OPENAI_API_KEY while optionally selecting the specific model via SKILLSPECTOR_MODEL.

NVIDIA's SkillSpector analyzes AI skills through LLM-powered semantic analysis. While the tool defaults to cloud-based providers, you can configure SkillSpector to use local LLMs like Ollama through its OpenAI-compatible interface, requiring no code modifications—only environment variable adjustments.

How SkillSpector Routes LLM Requests

SkillSpector communicates with language models through the skillspector.llm_utils helper module. According to the source code in src/skillspector/llm_utils.py, the system first queries the active provider for credentials, then falls back to the generic OpenAI-compatible variables OPENAI_API_KEY and OPENAI_BASE_URL.

The resolver in src/skillspector/providers/__init__.py handles provider selection via the SKILLSPECTOR_PROVIDER environment variable. When configured, it passes these credentials to ChatOpenAI (via llm_utils.get_chat_model()), which routes all LLM requests—including per-file meta-analysis calls—to your specified endpoint. This architecture means any server implementing the OpenAI chat-completion API, including Ollama, works seamlessly.

Step-by-Step Local LLM Configuration

Select the OpenAI-Compatible Provider

Set the provider to openai to enable the OpenAI-compatible wrapper. This provider is a thin abstraction that supplies only the API key and base URL, making it compatible with any local server that mimics the OpenAI API format.

export SKILLSPECTOR_PROVIDER=openai

Configure the API Key and Base URL

Point SkillSpector at your local Ollama instance by setting two critical environment variables. In src/skillspector/providers/openai/provider.py, the system expects OPENAI_API_KEY to contain any non-empty string (Ollama ignores this value but requires it for request validation), and OPENAI_BASE_URL to specify your local endpoint.

export OPENAI_API_KEY=ollama
export OPENAI_BASE_URL=http://localhost:11434/v1

Specify the Local Model Name

Optionally select which model Ollama should use for processing requests. The SKILLSPECTOR_MODEL variable tells SkillSpector which specific model to request from your local server.

export SKILLSPECTOR_MODEL=llama3.1:8b

Complete Configuration Examples

Standard Configuration with OpenAI Provider

This configuration uses the dedicated openai provider for maximum clarity and compatibility:

# 1. Choose the OpenAI-compatible provider

export SKILLSPECTOR_PROVIDER=openai

# 2. Provide a dummy API key (required but ignored by Ollama)

export OPENAI_API_KEY=ollama

# 3. Point to your local Ollama server

export OPENAI_BASE_URL=http://localhost:11434/v1

# 4. Select the model you have pulled in Ollama

export SKILLSPECTOR_MODEL=llama3.1:8b

# 5. Run a scan - LLM requests now hit your local server

skillspector scan ./my-skill/

Using Default Provider with Endpoint Overrides

If you prefer to keep the default nv_build provider but redirect it to a local endpoint, override the environment variables directly:

export SKILLSPECTOR_PROVIDER=nv_build
export OPENAI_API_KEY=ollama
export OPENAI_BASE_URL=http://localhost:11434/v1
export SKILLSPECTOR_MODEL=llama3.1:8b
skillspector scan ./my-skill/

As documented in docs/DEVELOPMENT.md, the OPENAI_BASE_URL variable specifically exists to "Override the OpenAI endpoint (e.g., point at Ollama)."

Disabling LLM Analysis When Needed

If you need to run static analysis only without any LLM calls, use the --no-llm flag to bypass all language model requests:

skillspector scan ./my-skill/ --no-llm

Key Source Files for Reference

Understanding these files helps troubleshoot configuration issues:

• src/skillspector/llm_utils.py – Resolves credentials, builds the ChatOpenAI instance, and routes all LLM calls.
• src/skillspector/providers/openai/provider.py – Demonstrates how OPENAI_API_KEY and OPENAI_BASE_URL transform into credential pairs for any OpenAI-compatible endpoint.
• src/skillspector/providers/__init__.py – Handles provider selection logic via SKILLSPECTOR_PROVIDER.
• README.md – Contains the quick-start example and the complete table of environment variables.
• docs/DEVELOPMENT.md – Explains the role of OPENAI_BASE_URL and lists supported configuration variables.

Summary

• SkillSpector uses an OpenAI-compatible abstraction layer in llm_utils.py to connect to language models.
• Set SKILLSPECTOR_PROVIDER=openai to enable local LLM support.
• Configure OPENAI_BASE_URL to point at your Ollama instance (e.g., http://localhost:11434/v1).
• Provide any non-empty value for OPENAI_API_KEY (required by the client but ignored by Ollama).
• Optionally specify the model using SKILLSPECTOR_MODEL (e.g., llama3.1:8b).
• Use --no-llm to disable LLM calls entirely for static-only analysis.

Frequently Asked Questions

Do I need to modify SkillSpector code to use Ollama?

No. SkillSpector's provider abstraction in src/skillspector/providers/__init__.py is designed to be generic. You only need to set environment variables before invoking the CLI. The system reads OPENAI_BASE_URL and OPENAI_API_KEY at runtime and passes them to ChatOpenAI, requiring zero code changes.

Why does Ollama require an OpenAI API key if it doesn't use it?

The OPENAI_API_KEY variable is required by the underlying ChatOpenAI client class that SkillSpector instantiates in llm_utils.py. While Ollama ignores the actual key value, the client library requires a non-empty string to initialize the connection. Any placeholder value like "ollama" or "dummy" works.

Can I use other local LLM servers besides Ollama?

Yes. Any server implementing the OpenAI chat-completion API specification works with this configuration. Examples include LM Studio, text-generation-webui, or llama-cpp-python with the OpenAI-compatible server flag. Simply set OPENAI_BASE_URL to the appropriate local endpoint (e.g., http://localhost:1234/v1).

How do I verify SkillSpector is actually connecting to my local server?

Check your Ollama server logs for incoming HTTP requests to the /v1/chat/completions endpoint when running skillspector scan. Additionally, you can set SKILLSPECTOR_PROVIDER=openai and temporarily misconfigure OPENAI_BASE_URL—SkillSpector will fail with a connection error, confirming it attempts to reach your specified local endpoint rather than cloud APIs.

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