Can SkillSpector Analyze Skills Without an API Key? Static-Only Security Scanning Explained

Yes, SkillSpector can analyze skills without an API key by disabling LLM-based analysis with the --no-llm flag, allowing comprehensive static and behavioral security checks to run using only local source code analysis.

NVIDIA's SkillSpector is an open-source security framework designed to scan AI skills for vulnerabilities. While the tool supports LLM-powered semantic analysis, you can perform thorough security scans without OpenAI, Anthropic, or NVIDIA inference API keys by running in static-only mode.

How SkillSpector Works Without an API Key

The --no-llm Flag and Static Analysis

When you invoke SkillSpector with the --no-llm command-line flag, the CLI populates use_llm=False in the initial graph state at src/skillspector/cli.py lines 92-94. This boolean propagates through the LangGraph-based analysis pipeline, causing all LLM-driven nodes to skip execution entirely.

The framework then executes only static analyzers that perform:

• YARA pattern matching for known malicious signatures
• MCP (Model Context Protocol) validation
• Behavioral tracking and taint analysis
• Permission checks on skill manifests

These analyzers operate purely on local source code and require no external credentials.

Error Handling When API Keys Are Missing

If you attempt to run LLM analyzers without configuring an API key, the provider resolution logic in src/skillspector/providers/__init__.py invokes raise_no_llm_api_key_configured() at lines 50-53. This raises a clear ValueError with the message defined in NO_LLM_API_KEY_MESSAGE, preventing ambiguous failures or partial scans.

Running SkillSpector Without an API Key

Command-Line Interface

Run a complete static analysis scan without any API key:

skillspector scan ./my-skill/ --no-llm --format json -o report.json

The --no-llm flag disables all LLM-driven nodes. The generated report.json contains findings from YARA, MCP, and other static checks.

Programmatic Python API

You can also disable LLM analysis when using SkillSpector as a library:

from skillspector.graph import graph

# Build state with LLM analysis disabled

state = {
    "skill_path": "/path/to/skill",
    "use_llm": False,          # Disable LLM requirement

    "output_format": "json",
}

result = graph.invoke(state)
print(result["filtered_findings"])   # Static-only findings

Expected Error Without --no-llm

If you forget to disable LLM mode and have no API key configured:

from skillspector.graph import graph

# This raises ValueError with NO_LLM_API_KEY_MESSAGE

result = graph.invoke({
    "skill_path": "/tmp/skill", 
    "use_llm": True
})

The call raises ValueError as implemented in src/skillspector/providers/__init__.py lines 42-47.

Architecture Deep Dive

CLI Parsing and State Initialization

In src/skillspector/cli.py, the argument parser detects --no-llm and sets the initial state dictionary's use_llm key to False at lines 92-94. This state propagates to every node in the analysis graph, ensuring consistent behavior across the entire scanning session.

Provider Validation Logic

The src/skillspector/providers/__init__.py file contains centralized LLM provider resolution. When use_llm=True but no credentials are found for the active provider, the code raises an explicit error at lines 50-53, halting execution before any external API calls are attempted.

Analyzer Node Behavior

Individual analyzers respect the use_llm flag. For example, in src/skillspector/nodes/analyzers/semantic_developer_intent.py lines 59-61, the semantic developer intent analyzer returns an empty findings list immediately when use_llm=False, bypassing credential-requiring LLM inference while allowing other analyzers to continue.

Summary

• SkillSpector analyzes skills without an API key when you invoke it with --no-llm or set use_llm=False in the graph state.
• Static analyzers require no external credentials and detect vulnerability classes including malicious patterns, permission misconfigurations, and behavioral anomalies.
• LLM semantic checks are automatically bypassed in no-LLM mode, though they require API keys when enabled.
• Clear error handling via raise_no_llm_api_key_configured() prevents confusion when API keys are missing but LLM analysis is requested.

Frequently Asked Questions

What analysis features are lost without an API key?

Without an API key and LLM analysis disabled, SkillSpector skips the semantic developer intent analyzer and other LLM-driven nodes that interpret natural language descriptions or complex behavioral patterns. However, static pattern matching, permission validation, and behavioral taint analysis continue to function fully.

Is static analysis sufficient for security scanning?

Yes, according to the integration tests in tests/integration/test_meta_analyzer_use_llm.py lines 25-33, static-only scans successfully identify security issues without API keys. While LLM analysis adds semantic depth for subtle vulnerabilities, static checks catch the majority of common misconfigurations and malicious code patterns.

Can I use SkillSpector in CI/CD pipelines without API keys?

Absolutely. By setting --no-llm in your CI configuration, you can scan skill repositories in automated pipelines without exposing API keys or managing external service credentials. This approach is validated in the test suite at tests/integration/test_meta_analyzer_use_llm.py lines 64-73.

Which LLM providers require API keys in SkillSpector?

SkillSpector supports OpenAI, Anthropic, and NVIDIA inference providers, all of which require valid API keys when use_llm=True. The provider resolution logic in src/skillspector/providers/__init__.py validates these credentials before attempting any LLM calls.

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