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Found 46 Skills
Launchdarkly's UI design system. Use when building interfaces inspired by Launchdarkly's aesthetic - dark mode, Inter font, 4px grid.
Use when planning A/B tests in LaunchDarkly, Optimizely, or similar platforms. Sizes the experiment (sample size, MDE, runtime), drafts hypothesis + success metrics + guardrails, and produces a launch checklist + rollback plan.
Comprehensive guide for implementing feature flags and A/B tests using the Flags SDK (the `flags` npm package). Use when: (1) Creating or declaring feature flags with `flag()`, (2) Setting up feature flag providers/adapters (Vercel, Statsig, LaunchDarkly, PostHog, GrowthBook, Hypertune, Edge Config, OpenFeature, Flagsmith, Reflag, Split, Optimizely, or custom adapters), (3) Implementing precompute patterns for static pages with feature flags, (4) Setting up evaluation context with `identify` and `dedupe`, (5) Integrating the Flags Explorer / Vercel Toolbar, (6) Working with feature flags in Next.js (App Router, Pages Router, Middleware) or SvelteKit, (7) Writing custom adapters, (8) Encrypting/decrypting flag values for the toolbar, (9) Any task involving the `flags`, `flags/next`, `flags/sveltekit`, `flags/react`, or `@flags-sdk/*` packages. Triggers on: feature flags, A/B testing, experimentation, flags SDK, flag adapters, precompute flags, Flags Explorer, feature gates, flag overrides.
Investigates a triggered observability alert and returns a structured diagnosis with likely cause, scope, and next steps.
Analyzes observability data — logs, traces, errors, sessions, and metrics — to find root cause and actionable evidence. Use when the user reports a bug, an unexpected behavior, or asks about patterns across application data.
Creates observability dashboards and graphs from logs, traces, errors, sessions, metrics, and events data by previewing charts inline and saving them to a dashboard.
Guide for setting up AI configuration in your application. Helps you choose between agent vs completion mode, select the right approach for your stack, and create AI Configs that make sense for your use case.
Configure guarded rollouts with progressive traffic increases, metric monitoring, and automatic rollback. Use when releasing features gradually with safety thresholds.
Guide for giving your AI agents capabilities through tools. Helps you identify what your AI needs to do, create tool definitions, and attach them in a way that makes sense for your framework.
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
Create, track, retrieve, update, and delete custom business metrics for AI Configs. Covers full lifecycle: define metric kinds via API, emit events via SDK, and query results.
Create a boolean first flag, add evaluation, toggle on/off for end-to-end proof. Parent onboarding Step 6; uses MCP, API, or ldcli; optional flag-create skill.