Loading...
Loading...
Found 40 Skills
One-time setup that gathers design context for your project and saves it to your AI config file. Run once to establish persistent design guidelines.
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.
Generate app icons for your React Native Expo app with iOS 26 support
Guide for experimenting with AI configurations. Helps you test different models, prompts, and parameters to find what works best through systematic experimentation.
Configure code chunking in GrepAI. Use this skill to optimize how code is split for embedding.
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AI Configs implementation in five stages: extract prompts, wrap in the AI SDK, add tools, add tracking, add evals/judges. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini) to a managed AI Config, or stage a full hardcoded-to-LaunchDarkly migration.
Configure OpenAI as embedding provider for GrepAI. Use this skill for high-quality cloud embeddings.
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
Configure PostgreSQL with pgvector for GrepAI. Use this skill for team environments and large codebases.
Create and manage agent graphs — directed graphs of AI Configs connected by edges with handoff logic. Use when building multi-agent workflows where configs route to each other.
Create and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.
Configure search result boosting in GrepAI. Use this skill to prioritize certain paths and penalize others.