Skills

Skill 38 of 56

Launchdarkly Guarded Rollout

Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback.

3 minutes · 637 words · 17 sections

Install

$ npx skills add launchdarkly/ai-tooling --skill launchdarkly-guarded-rollout

$ npx skills add launchdarkly/ai-tooling

» /plugin marketplace add launchdarkly/ai-tooling

The first command installs just this skill, by the name in its SKILL.md; the second installs the whole repository.

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, choose which regressions roll back automatically, 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-flags – inspect the flag and its variations
get-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.)
Auto-rollback	Per metric: whether a regression rolls back automatically or only notifies

Rollout Weight Units

Rollout weights use thousandths (basis points):

1000 = 1%
10000 = 10%
50000 = 50%, the maximum for a guarded stage

Monitoring Window

The monitoring window is specified in milliseconds:

3600000 = 1 hour
86400000 = 24 hours
604800000 = 7 days

Core Principles

Start Small: Begin with a low percentage (1-5%) to catch issues early
Monitor What Matters: Choose metrics that reflect user experience
Choose Rollback Deliberately: Auto-roll back on metrics that signal real harm; notify only on noisy ones
Allow Time: Each stage needs enough monitoring time for signal to emerge
Have a Rollback Plan: Always configure at least notification on regression

Workflow

Step 1: Prepare

Before starting a guarded rollout:

Use get-flags to inspect the flag — note the variation IDs for test and control
Use get-metrics to find metrics suitable for monitoring
Ensure the flag is on in the target environment (if needed, call toggle-flag with on: true and confirm: true)
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

Each stage’s rolloutWeight is capped at 50000 (50%). Do not add a 100% stage: after the last stage passes, LaunchDarkly completes the rollout.

Step 3: Configure Metrics

Select metrics that indicate problems. Each metric entry takes exactly key (a metric or metric group key), isGroup, and autoRollback (roll back automatically on regression, or only notify):

Metric Type	Example	`autoRollback`
Error rate	`api-error-rate`	`true`
Latency	`p99-response-time`	`false` (notify only)
Conversion	`checkout-completed`	`true`

Step 4: Start the Rollout

Use start-guarded-rollout:

{
  "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}
  ],
  "metrics": [
    {"key": "api-error-rate", "isGroup": false, "autoRollback": true},
    {"key": "checkout-completed", "isGroup": false, "autoRollback": true}
  ]
}

Step 5: Verify

Use get-flags to confirm the guarded rollout is active
Check that the flag shows the rollout configuration in the environment
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, call stop-guarded-rollout:

{
  "projectKey": "my-project",
  "flagKey": "new-checkout-flow",
  "environmentKey": "production",
  "confirm": true
}

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` (`on: true`, `confirm: true`) — 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	The tool returns `requiresApproval: true` and how to submit the change with `create-approval-request`; share that request’s `approvalRequest.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 forget to configure at least one metric — a rollout without monitoring is just a regular rollout

About this skill

Trigger

Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.

The verbatim description from this skill’s front matter — the string an agent matches on to decide whether to load it.

Compatibility: Requires the remotely hosted LaunchDarkly MCP server
Licence: Apache-2.0
Source: skills/feature-flags/launchdarkly-guarded-rollout/SKILL.md

Colophon for this issue

Colophon

How this issue was made

Source

github.com/launchdarkly/ai-tooling, branch main, last pushed 7 November 2026.

Licence

Other — the text of every skill is reproduced unmodified, frontmatter included, under the upstream licence.

Discovery

56 skills found by walking the repository tree for SKILL.md, not by matching a directory convention. 10 distinct layouts observed: skills/agentcontrol/*/SKILL.md, skills/aws-devops-agent/*/SKILL.md, skills/experiments/*/SKILL.md, skills/feature-flags/*/SKILL.md, skills/metrics/*/SKILL.md, skills/observability/*/SKILL.md, skills/onboarding/*/SKILL.md, skills/onboarding/sdk-install/*/SKILL.md, skills/*/SKILL.md, skills/release-autopilot/*/SKILL.md.

Issue colours

Resolved from a deterministic hash of the owner name. Two accent tones are generated per issue and each is proven against its own ground before it ships: a single accent that passes AA on both light and dark paper is arithmetically impossible.

Heading repairs

1 repair applied to this skill so the document has one h1 and no skipped levels:

Shifted “17 headings” from h1 to h2 so the skill title is the only h1.

Spec compliance

Every skill satisfies the Agent Skills specification’s frontmatter rules.

Images

Images inside a skill come from the upstream repository. Where the author gave no alternative text we mark the image decorative rather than inventing a description — a plausible caption we made up is worse than none for the reader who depends on it.

Marketplace

A plugin manifest is published at .claude-plugin/marketplace.json by LaunchDarkly, declaring 1 plugin. It is read for editorial metadata only — never as the skill index, which is always the repository tree.

Signal

Install counts come from skills.sh. They measure downloads, not quality, and an unranked repository is not an unread one.

Agent surfaces

The whole issue is available as one markdown document at /launchdarkly/ai-tooling.md, and each skill at its own .md URL.

Publication

Set by Skills Docs from the source repository. Body text is Literata at the reader’s chosen size and measure; code is Geist Mono. Nothing on this page was written by us except this paragraph.

LaunchDarkly Guarded Rollouts

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-flags – inspect the flag and its variations
get-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?

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.)
Auto-rollback	Per metric: whether a regression rolls back automatically or only notifies

Rollout Weight Units

Rollout weights use thousandths (basis points):

1000 = 1%
10000 = 10%
50000 = 50%, the maximum for a guarded stage

Monitoring Window

The monitoring window is specified in milliseconds:

3600000 = 1 hour
86400000 = 24 hours
604800000 = 7 days

Core Principles

Start Small: Begin with a low percentage (1-5%) to catch issues early
Monitor What Matters: Choose metrics that reflect user experience
Choose Rollback Deliberately: Auto-roll back on metrics that signal real harm; notify only on noisy ones
Allow Time: Each stage needs enough monitoring time for signal to emerge
Have a Rollback Plan: Always configure at least notification on regression

Workflow

Step 1: Prepare

Before starting a guarded rollout:

Use get-flags to inspect the flag — note the variation IDs for test and control
Use get-metrics to find metrics suitable for monitoring
Ensure the flag is on in the target environment (if needed, call toggle-flag with on: true and confirm: true)
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

Each stage’s rolloutWeight is capped at 50000 (50%). Do not add a 100% stage: after the last stage passes, LaunchDarkly completes the rollout.

Step 3: Configure Metrics

Select metrics that indicate problems. Each metric entry takes exactly key (a metric or metric group key), isGroup, and autoRollback (roll back automatically on regression, or only notify):

Metric Type	Example	`autoRollback`
Error rate	`api-error-rate`	`true`
Latency	`p99-response-time`	`false` (notify only)
Conversion	`checkout-completed`	`true`

Step 4: Start the Rollout

Use start-guarded-rollout:

{
  "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}
  ],
  "metrics": [
    {"key": "api-error-rate", "isGroup": false, "autoRollback": true},
    {"key": "checkout-completed", "isGroup": false, "autoRollback": true}
  ]
}

Step 5: Verify

Use get-flags to confirm the guarded rollout is active
Check that the flag shows the rollout configuration in the environment
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, call stop-guarded-rollout:

{
  "projectKey": "my-project",
  "flagKey": "new-checkout-flow",
  "environmentKey": "production",
  "confirm": true
}

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` (`on: true`, `confirm: true`) — 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	The tool returns `requiresApproval: true` and how to submit the change with `create-approval-request`; share that request’s `approvalRequest.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 forget to configure at least one metric — a rollout without monitoring is just a regular rollout