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.

## Core Concepts

### What Are Experiments?

Experiments in LaunchDarkly measure the impact of feature flag variations on key metrics. An experiment consists of:

- **Treatments**: the flag variations being compared (control vs. test). Each treatment has an `allocationPercent`; the values across treatments should sum to 100.
- **Metrics**: what you're measuring (conversion rate, latency, revenue, etc.). One must be the primary metric.
- **Flag config**: the `flagKey`, `ruleId`, and `flagConfigVersion` of the targeting rule that drives the experiment.
- **Iteration**: a single data-collection window. Created in `not_started` status, becomes `running` when started, transitions to `stopped` when ended.
- **Holdout** (optional): a project-level group of users excluded from the experiment for baseline measurement (`holdoutId`).

### Experiment Lifecycle

1. **Create** the experiment with its first iteration (`create-experiment`).
2. **Start the iteration** to begin data collection (`start-experiment-iteration`).
3. **Monitor** results as data accumulates (`get-experiment`).
4. **Evolve the design** mid-experiment if needed — change locked fields like `treatments`, `metrics`, or `methodology` by calling `save-and-start-experiment-iteration`, which stops the current iteration, creates a new draft with your changes, and starts it.
5. **Stop the iteration** when you have a winner or a clear call (`stop-experiment-iteration`).
6. **Ship** the winning variation.

## Core Principles

1. **Metrics first**: ensure the metrics you'll reference exist before creating the experiment.
2. **Clear hypothesis**: every iteration requires a `hypothesis` string; state what you expect to improve and by how much.
3. **Proper controls**: exactly one treatment must have `baseline: true`.
4. **Sufficient sample size**: let iterations run long enough for statistical significance.
5. **One change at a time**: test one variable per