Implement formal AI model evaluations with code, model, and human grading.
npx skills add https://github.com/hummbl-dev/hummbl-agent --skill eval-harness-hummbl-dev
Please help me install this Agent Skill. Skill: eval-harness Source: https://github.com/hummbl-dev/hummbl-agent/tree/main/skills/eval-harness Command: npx skills add https://github.com/hummbl-dev/hummbl-agent --skill eval-harness-hummbl-dev
This Skill provides a structured approach to rigorously evaluate AI model performance, ensuring reliability and tracking regressions through defined tests and metrics.
pass@k and pass^k for reliability measurement.Use the eval-harness skill to define a new capability evaluation for user authentication.
High-intent search queries and answers about installing and using this skill.
Eval-driven development treats AI evaluations as unit tests for AI development, providing a structured approach to rigorously evaluate model performance, ensure reliability, and track regressions through defined tests and metrics.
You can run regression evaluations by defining and executing tests against new AI model versions using this formal evaluation framework, ensuring that bug fixes and updates do not introduce new problems or break existing functionalities.
Yes, the evaluation framework supports code-based, model-based, and human-in-the-loop grading mechanisms to assess AI capability evaluations, allowing you to flexibly verify model performance based on your specific testing requirements.
The framework tracks pass@k and pass^k metrics to measure AI reliability. These metrics provide quantitative tracking of model performance and pass rates across repeated evaluation sessions.
No external dependencies are required to run the formal AI evaluation framework. It independently supports capability and regression evaluations, allowing you to define expected AI generation quality without prerequisite library installations.
The best way is to use the formal evaluation framework to define expected code generation quality, run these evaluations against new model versions, and verify that new functionalities meet the defined standards without causing regressions.
bbaserdemImplement eval-driven development with capability and regression evals for AI code generation.
ryasrkFormalize evaluation frameworks for AI code sessions with pass@k and pass^k metrics.
WUIX69Define evaluation frameworks for Claude Code tasks with pass@k metrics.
Fabio29TImplement formal evaluation frameworks for AI agents with pass@k metrics.