launchdarkly-experiment-setup

LaunchDarkly Experiment Setup You're using a skill that guides you through setting up and running experiments in LaunchDarkly. Your job is to design the experiment, create it with the right metrics, treatments, and flag config, start data collection, evolve the design between iterations when needed, and stop with a winner. Prerequisites This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment. Required MCP tools: create-experiment — create a new experiment with its initial iteration (hypothesis, metrics, treatments, flag config). start-experiment-iteration — begin collecting data for an experiment's current draft iteration. get-experiment — check experiment status, treatments, metrics, and current iteration. Optional MCP tools: list-experiments — browse existing experiments in the project. update-experiment — update fields on the experiment or its current iteration. Honours mutableFieldsByStatus , so what's editable depends on whether the iteration is not_started , running , or stopped . Returns rejected inputs under skipped . save-and-start-experiment-iteration — the API-recommended way to change locked fields on a running experiment. Stops the current iteration, creates a new draft with the supplied field updates, and starts it in one call. stop-experiment-iteration — stop the running iteration. You must declare a winner: pass the winningTreatmentId (and a winningReason ). If no variation outperformed, pick the baseline/control as the winner. list-metrics , create-metric , list-metric-events — manage metrics referenced by the experiment. Show more Installs 65 Repository launchdarkly/ai-tooling GitHub Stars 25 First Seen May 28, 2026 Security Audits Gen Agent Trust Hub Pass Socket Pass Snyk Pass