Configure and interpret LaunchDarkly AI Config online evaluations with judge attachments
Attach judges to LaunchDarkly AI Config variations, create custom judges, set sampling rates, and interpret production quality signals from online evaluations.
npx skills add agentskillexchange/skills --skill configure-and-interpret-launchdarkly-ai-config-online-evaluations-with-judge-attachments
This skill helps an agent set up and reason about LaunchDarkly AI Config online evaluations. The agent can create or attach judge configs, configure sampling rates, enable fallthrough correctly, and interpret quality scores for production variations using LaunchDarkly’s online evaluation model. The workflow is specifically about evaluation plumbing and rollout-quality measurement for AI Configs, not generic prompt experimentation.
What this skill actually does
Use this when a user is shipping or monitoring LaunchDarkly AI Config changes and wants the agent to wire up online judges, compare evaluation behavior, and catch regressions in production-facing traffic. This is more appropriate than using LaunchDarkly normally when the need is to orchestrate the evaluation setup and explain what those scores mean operationally.
The scope boundary is clear: this is not a plain LaunchDarkly or SDK listing. It is a narrow workflow around online evaluation setup, judge configuration, sampling strategy, and result interpretation for AI Config variations.