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By launchdarkly
Manage the full lifecycle of LaunchDarkly feature flags and AI configs: create, target, experiment, and clean up flags; migrate LLM prompts to AgentControl; set up guarded rollouts with metrics and automatic rollback; instrument SDKs for custom metric tracking.
npx claudepluginhub launchdarkly/ai-toolingOnboard a project to LaunchDarkly: kickoff roadmap, resumable log, explore repo, MCP, companion flag skills, nested SDK install (detect/plan/apply), first flag. Use when adding LaunchDarkly, setting up or integrating feature flags in a project, SDK integration, or 'onboard me'.
Create and manage agent graphs — directed graphs of configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
DEPRECATED redirect — this skill was renamed to agent-graphs. Do not use this skill; invoke agent-graphs instead. Kept only so old references to aiconfig-agent-graphs still point users to the new name.
DEPRECATED redirect — this skill was renamed to built-in-metrics. Do not use this skill; invoke built-in-metrics instead. Kept only so old references to aiconfig-ai-metrics still point users to the new name.
DEPRECATED redirect — this skill was renamed to configs-create. Do not use this skill; invoke configs-create instead. Kept only so old references to aiconfig-create still point users to the new name.
External network access
Connects to servers outside your machine
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PROACTIVELY use this agent when experiments are started, modified, or when results need analysis. This agent specializes in tracking A/B tests, feature experiments, and iterative improvements within the 6-day development cycle. Should be triggered automatically when experimental code paths or feature flags are introduced. Examples:\n\n<example>\nContext: When implementing feature flags or A/B test variants\nuser: "Add a feature flag to test the new onboarding flow"\nassistant: "I've implemented the feature flag for the onboarding flow A/B test. Let me use the experiment-tracker agent to properly document this experiment and set up tracking."\n<commentary>\nEvery feature flag represents an experiment that needs proper tracking and success metrics.\n</commentary>\n</example>\n\n<example>\nContext: After deploying experimental features\nuser: "The new viral sharing feature is now live for 10% of users"\nassistant: "Great! The viral sharing feature is deployed. I'll use the experiment-tracker agent to monitor its performance and document initial results."\n<commentary>\nLive experiments need immediate tracking setup to capture valuable data from the start.\n</commentary>\n</example>\n\n<example>\nContext: When reaching experiment milestones\nuser: "It's been a week since we launched the TikTok integration test"\nassistant: "Perfect timing for analysis. Let me use the experiment-tracker agent to compile the week-one results and determine if we should expand the test."\n<commentary>\nRegular experiment checkpoints ensure data-driven decisions about feature rollouts.\n</commentary>\n</example>\n\n<example>\nContext: Before making product decisions\nuser: "Should we keep the AI avatar feature or remove it?"\nassistant: "Let me analyze the experiment data to inform this decision. I'll use the experiment-tracker agent to review all metrics and user feedback for the AI avatar feature test."\n<commentary>\nProduct decisions should be backed by experiment data, not gut feelings.\n</commentary>\n</example>
Claude Code skill pack for PostHog (24 skills)
Access PostHog analytics, feature flags, experiments, error tracking, and insights directly from Claude Code. Optionally capture Claude Code sessions to PostHog LLM Analytics.
Ship features end-to-end with launch checklists and rollout plans
Engineering process for solo founders and teams up to 50 engineers. Agents do architecture, code review, QA, and security. You make two decisions per feature.
Agent orchestration harness for Claude Code — campaign persistence, fleet coordination, intent routing
LaunchDarkly's public collection of agent skills and playbooks. These skills encode repeatable workflows for working with LaunchDarkly, so coding agents can execute common tasks safely and consistently.
Agent Skills are modular, text-based playbooks that teach an agent how to perform a workflow. This repo is designed to be a public, open-source home for LaunchDarkly skills and to align with the emerging Agent Skills Open Standard.
| Skill | Description |
|---|---|
feature-flags/launchdarkly-flag-discovery | Audit flags, find stale/launched flags, and assess removal readiness |
feature-flags/launchdarkly-flag-create | Create new feature flags in a way that fits existing codebase patterns |
feature-flags/launchdarkly-flag-targeting | Control targeting, rollouts, rules, and cross-environment config |
feature-flags/launchdarkly-flag-cleanup | Safely remove flags from code using LaunchDarkly as the source of truth |
feature-flags/launchdarkly-guarded-rollout | Configure guarded rollouts with progressive traffic, metric monitoring, and rollback |
| Skill | Description |
|---|---|
agentcontrol/configs-create | Create configs with variations for agent or completion mode |
agentcontrol/migrate | Migrate an app with hardcoded LLM prompts to AgentControl in five stages (extract, wrap, tools, tracking, evals) |
agentcontrol/configs-update | Update and delete configs, manage lifecycle |
agentcontrol/configs-variations | Manage config variations for A/B testing |
agentcontrol/tools | Create and attach tools for function calling |
agentcontrol/projects | Create and manage projects to organize configs |
agentcontrol/online-evals | Attach LLM-as-a-judge evaluators to configs |
agentcontrol/configs-targeting | Configure targeting rules for config rollouts |
agentcontrol/snippets | Create and manage reusable prompt snippets across configs |
agentcontrol/agent-graphs | Create and manage multi-agent graphs with routing and handoffs |
| Skill | Description |
|---|---|
experiments/launchdarkly-experiment-setup | Set up experiments with metrics, treatments, and data collection |
| Skill | Description |
|---|---|
metrics/launchdarkly-metric-choose | Select the right metric type for an experiment |
metrics/launchdarkly-metric-create | Create metrics and instrument tracking events |
metrics/launchdarkly-metric-instrument | Add tracking calls to code for existing metrics |
This repo is a Claude Code plugin. Installing it gives you all the skills above plus the LaunchDarkly MCP server.
/plugin install.https://github.com/launchdarkly/ai-tooling
Once installed, skills are available as /launchdarkly:<skill-name> across all your projects, and the MCP server can read and modify your flags directly.
| Skill | Description |
|---|---|
onboarding | End-to-end LaunchDarkly setup: kickoff roadmap, MCP, SDK install, first flag |
onboarding/mcp-configure | Configure the LaunchDarkly hosted MCP server (OAuth, no API keys needed) |
onboarding/sdk-install | Install and initialize the correct SDK via detect, plan, and apply sub-steps |
onboarding/first-flag | Create a boolean flag, evaluate it, toggle on/off for end-to-end proof |
This repo is a Cursor plugin. Installing it gives you all the skills above plus the LaunchDarkly MCP server, so the agent can read and modify your flags directly.
https://github.com/launchdarkly/ai-tooling
Once installed, the skills and MCP server are available across all your projects.
# Clone the repo
git clone https://github.com/launchdarkly/ai-tooling.git
cd ai-tooling
# If your agent supports skills.sh installs:
npx skills add launchdarkly/ai-tooling
# Or manually copy a skill into your agent's skills path:
cp -r skills/feature-flags/launchdarkly-flag-cleanup <your-agent-skills-dir>/
Then ask your agent something like:
Which feature flags are stale and should be cleaned up?
Create a feature flag for the new checkout flow
Roll out dark-mode to 25% of users in production
Remove the `new-checkout-flow` feature flag from this codebase