Construct evaluation loops grading AI agent output with an LLM judge.
npx skills add https://github.com/Archive228/loopkit --skill eval-harness-archive228
Please help me install this Agent Skill. Skill: eval-harness Source: https://github.com/Archive228/loopkit/tree/main/skills/eval-harness Command: npx skills add https://github.com/Archive228/loopkit --skill eval-harness-archive228
This Skill enables the creation of repeatable evaluation loops for AI agents, ensuring consistent quality control for prompt and skill changes.
Run the eval-harness skill by providing inputs and executing the loop.
High-intent search queries and answers about installing and using this skill.
Automate regression testing for AI prompts by constructing a repeatable evaluation loop that grades agent output using an LLM judge, ensuring changes are consistently scored against a baseline.
An LLM judge evaluation loop is a structured process for grading AI agent outputs across three stages: inputs, runner, and verifier, maintaining baseline consistency for quality control.
Set up model comparison testing by running a structured evaluation loop that processes inputs, executes the runner, and verifies outputs to evaluate and compare different AI models.
Yes, an AI evaluation harness is designed specifically for tuning prompts and verifying workflows by grading agent outputs against a baseline to ensure effectiveness and consistent quality.
No external dependencies are required to run AI agent evaluation loops, as the harness operates independently using its built-in scripts and references to grade outputs and maintain baseline consistency.
Using an LLM judge for AI quality control limits evaluations to the judge model's grading accuracy, requiring structured inputs and clear baselines to effectively verify workflow changes.
selcukyucelGenerate structured evaluation datasets with binary rubric scoring for AI outputs.
mvillmowEvaluate ML model performance on test datasets with classification and regression metrics.
GuidoBergmanScaffold a CI/CD quality pipeline with test execution, burn-in loops, and artifact collection.
kollaboraiCreate and iteratively improve AI skills with structured evaluation and benchmarking workflows.
