Coordinate eval-driven development workflows for Claude Code sessions.
npx skills add https://github.com/hieuck/Pro5ChromeManager --skill eval-harness-hieuck
Please help me install this Agent Skill. Skill: eval-harness Source: https://github.com/hieuck/Pro5ChromeManager/tree/main/.agents/skills/eval-harness Command: npx skills add https://github.com/hieuck/Pro5ChromeManager --skill eval-harness-hieuck
Evaluates Claude Code sessions using a formal evaluation framework to ensure reliable model behavior and traceable results.
Define a new evaluation with /eval define feature and then run checks with /eval check feature to start the evaluation pipeline.
High-intent search queries and answers about installing and using this skill.
You can set up an evaluation pipeline by defining a new evaluation with the /eval define command and running checks with /eval check to measure session reliability.
Pass@k is a standardized metric used to measure the probability that at least one successful response is generated within k attempts, quantifying AI model reliability for regression testing.
Yes, the evaluation framework supports multiple grader types including code-based, model-based, and human graders to tailor the evaluation pipeline to your specific requirements.
You can build regression suites by applying capability and regression evaluation templates to measure and compare model performance across different versions using standardized metrics.
The best way to define pass/fail criteria is by establishing a formal evaluation environment that applies standardized metrics and workflows to ensure reliable, traceable model behavior.
You need formal eval definitions to ensure reliable model behavior and traceable results, preventing regressions and systematically measuring AI capabilities across development iterations.
yu13130122297Identify uncertainties in research framing, datasets, metrics, and baselines.
gugug168Define and run capability and regression evaluations for Claude Code sessions.
flatrickDefine, measure, and track AI behavior with pass@k metrics.
ryasrkFormalize evaluation frameworks for AI code sessions with pass@k and pass^k metrics.
