Promptfoo vs SkillSpector

Side-by-side comparison of two AI agent tools

Promptfooopen-source

Test your prompts, agents, and RAGs. Red teaming/pentesting/vulnerability scanning for AI. Compare performance of GPT, Claude, Gemini, Llama, and more. Simple declarative configs with command line and

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SkillSpectoropen-source

Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, security risks, prompt injection, data exfiltration, and supply-chain risks in

Metrics

PromptfooSkillSpector
Stars25.6k18.8k
Star velocity /mo1.1k1.6k
Commits (90d)895356
Releases (6m)1010
Overall score0.80543651048097260.8057990324260016

Pros

  • +Comprehensive testing suite covering both performance evaluation and security red teaming in a single tool
  • +Multi-provider support with easy comparison between OpenAI, Anthropic, Claude, Gemini, Llama and dozens of other models
  • +Strong CI/CD integration with automated pull request scanning and code review capabilities for production deployments

    Cons

    • -Requires API keys and credits for multiple LLM providers, which can become expensive for extensive testing
    • -Command-line focused interface may have a learning curve for teams preferring GUI-based tools
    • -Limited to evaluation and testing - does not provide actual LLM application development capabilities

      Use Cases

      • •Automated testing and evaluation of prompt performance across different models before production deployment
      • •Security vulnerability scanning and red teaming of LLM applications to identify potential risks and compliance issues
      • •Systematic comparison of model performance and cost-effectiveness to optimize AI application architecture

        FAQ

        Which is more popular, Promptfoo or SkillSpector?
        Promptfoo has more GitHub stars (25,597 vs 18,781).
        Which is more actively developed, Promptfoo or SkillSpector?
        Promptfoo had more commits in the last 90 days (895 vs 356).
        Should I use Promptfoo or SkillSpector?
        Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.