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LaunchDarkly Guarded Rollout

LaunchDarkly Guarded RolloutSkill

Released
v0.1.0
Apache-2.0
Repository Docs

Summary

Design a staged LaunchDarkly rollout — traffic steps, monitoring windows, regression thresholds and automatic rollback — and start it from your coding agent.

Features

  • Designs staged rollouts with per-stage traffic weights and monitoring windows
  • Selects regression metrics and thresholds, with notify or rollback per metric
  • Starts, monitors and halts guarded rollouts through the LaunchDarkly MCP server
  • Encodes LaunchDarkly's basis-point weights and millisecond windows so the agent stops guessing units

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Skill Content

Markdown Content

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---
name: launchdarkly-guarded-rollout
description: "Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds."
license: Apache-2.0
compatibility: Requires the remotely hosted LaunchDarkly MCP server
metadata:
  author: launchdarkly
  version: "0.1.0"
---

# LaunchDarkly Guarded Rollouts

You're using a skill that will guide you through configuring guarded rollouts in LaunchDarkly. Your job is to design rollout stages, select monitoring metrics, configure regression thresholds, and start the rollout.

## Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server to be configured in your environment.

**Required MCP tools:**
- `start-guarded-rollout` -- start a progressive rollout with monitoring
- `get-flag` -- inspect the flag and its variations
- `list-metrics` -- find metrics to monitor during the rollout

**Optional MCP tools:**
- `stop-guarded-rollout` -- halt an active rollout immediately
- `toggle-flag` -- ensure the flag is turned on before starting
- `create-metric` -- create metrics if they don't exist

## Core Concepts

### What Are Guarded Rollouts?

A guarded rollout progressively increases traffic to a new feature flag variation through a series of stages. At each stage, LaunchDarkly monitors selected metrics for regressions. If a regression is detected, the rollout can automatically pause and notify the team — or even roll back.

### Key Components

| Component | Description |
|-----------|-------------|
| **Test variation** | The new variation being rolled out |
| **Control variation** | The existing/baseline variation |
| **Stages** | Steps with increasing traffic percentage and monitoring windows |
| **Metrics** | What to monitor for regressions (error rate, latency, etc.) |
| **Regression threshold** | How much a metric can degrade before triggering action |
| **On regression** | Whether to notify, rollback, or both when a threshold is breached |

### Rollout Weight Units

Rollout weights use thousandths (basis points):
- `1000` = 1%
- `10000` = 10%
- `50000` = 50%
- `100000` = 100%

### Monitoring Window

The monitoring window is specified in milliseconds:
- `3600000` = 1 hour
- `86400000` = 24 hours
- `604800000` = 7 days

## Core Principles

1. **Start Small**: Begin with a low percentage (1-5%) to catch issues early
2. **Monitor What Matters**: Choose metrics that reflect user experience
3. **Set Realistic Thresholds**: Too tight = false alarms; too loose = missed regressions
4. **Allow Time**: Each stage needs enough monitoring time for signal to emerge
5. **Have a Rollback Plan**: Always configure at least notification on regression

## Workflow

### Step 1: Prepare

Before starting a guarded rollout:

1. Use `get-flag` to inspect the flag — note the variation IDs for test and control
2. Use `list-metrics` to find metrics suitable for monitoring
3. Ensure the flag is **on** in the target environment (use `toggle-flag` if needed)
4. Confirm there's no active guarded rollout on this flag already

### Step 2: Design Stages

Plan the rollout progression. A typical pattern:

| Stage | Traffic | Monitoring Window | Purpose |
|-------|---------|-------------------|---------|
| 1 | 1% | 1 hour | Smoke test — catch obvious crashes |
| 2 | 10% | 24 hours | Early signal on metrics |
| 3 | 50% | 24 hours | Confidence building |
| 4 | 100% | 24 hours | Full rollout with monitoring |

### Step 3: Configure Metrics

Select metrics that indicate problems:

| Metric Type | Example | Threshold | Action |
|-------------|---------|-----------|--------|
| Error rate | `api-error-rate` | 0.05 (5% increase) | Rollback |
| Latency | `p99-response-time` | 0.2 (20% increase) | Notify |
| Conversion | `checkout-completed` | 0.1 (10% decrease) | Notify + Rollback |

### Step 4: Start the Rollout

Use `start-guarded-rollout`:

```json
{
  "projectKey": "my-project",
  "flagKey": "new-checkout-flow",
  "environmentKey": "production",
  "testVariationId": "variation-id-for-new-flow",
  "controlVariationId": "variation-id-for-current-flow",
  "randomizationUnit": "user",
  "stages": [
    {"rolloutWeight": 1000, "monitoringWindowMilliseconds": 3600000},
    {"rolloutWeight": 10000, "monitoringWindowMilliseconds": 86400000},
    {"rolloutWeight": 50000, "monitoringWindowMilliseconds": 86400000},
    {"rolloutWeight": 100000, "monitoringWindowMilliseconds": 86400000}
  ],
  "metrics": [
    {
      "metricKey": "api-error-rate",
      "onRegression": {"notify": true, "rollback": true},
      "regressionThreshold": 0.05
    },
    {
      "metricKey": "checkout-completed",
      "onRegression": {"notify": true, "rollback": false},
      "regressionThreshold": 0.1
    }
  ]
}
```

### Step 5: Verify

1. Use `get-flag` to confirm the guarded rollout is active
2. Check that the flag shows the rollout configuration in the environment
3. Monitor for any immediate regression notifications

**Report results:**
- Guarded rollout started with N stages
- M metrics being monitored
- First stage at X% traffic for Y hours

## Stopping a Rollout

If issues arise or you need to halt the rollout:

```json
{
  "projectKey": "my-project",
  "flagKey": "new-checkout-flow",
  "environmentKey": "production"
}
```

This immediately stops the progressive rollout and locks the flag at its current state.

## Edge Cases

| Situation | Action |
|-----------|--------|
| Flag is off | Turn it on first with `toggle-flag` — rollouts require the flag to be on |
| Active rollout exists | Stop it first with `stop-guarded-rollout` before starting a new one |
| No suitable metrics | Create metrics first with `create-metric` |
| Approval required | If the environment requires approvals, the tool will return an approval URL |

## What NOT to Do

- Don't start a guarded rollout on a flag that's turned off
- Don't skip the monitoring window design — rushing through stages defeats the purpose
- Don't set regression thresholds to 0 — small fluctuations are normal
- Don't forget to configure at least one metric — a rollout without monitoring is just a regular rollout

Usage Instructions

Learn how to use this skill with different AI agents.

Claude Desktop
/plugin marketplace add launchdarkly/ai-tooling
/plugin install launchdarkly@launchdarkly-ai-tooling

The skill requires the remotely hosted LaunchDarkly MCP server to be configured.

Example Usage

Roll out the new-checkout-flow flag to production at 1% for an hour, then 10%, 50% and 100% over the next three days, and roll back automatically if the api-error-rate metric degrades by more than 5%.

Description

Shipping behind a flag is only half a release. This official LaunchDarkly skill teaches a coding agent the other half: turning a flag on gradually, watching the metrics that would tell you it went wrong, and rolling back on its own if they move.

The skill walks the agent through a full guarded rollout:

  1. Prepare — inspect the flag and its variation IDs, list the metrics available to monitor, and confirm the flag is on in the target environment with no rollout already running.
  2. Design the stages — a progression such as 1% for an hour (a smoke test for obvious crashes), 10% for a day (early metric signal), 50%, then 100%, each with its own monitoring window.
  3. Pick the metrics and thresholds — error rate, p99 latency, conversion — and decide per metric whether a breach should notify, roll back, or both.
  4. Start it via the start-guarded-rollout MCP tool, with stop-guarded-rollout available to halt an active rollout immediately.

It also carries the unit conventions that are easy to get wrong from memory: rollout weights are thousandths (1000 = 1%, 100000 = 100%) and monitoring windows are milliseconds (3600000 = one hour, 604800000 = seven days).

Requirements

The skill drives the remotely hosted LaunchDarkly MCP server and needs start-guarded-rollout, get-flag and list-metrics available, with stop-guarded-rollout, toggle-flag and create-metric used opportunistically.

Source

Part of LaunchDarkly's official open-source agent-skills collection (Apache-2.0), which also covers flag creation, targeting, drift detection, stale-flag cleanup, experiments and AgentControl migrations. Installable as a Claude Code plugin or through the Skills CLI.

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