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Found 12 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.
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, 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 and configure configs in LaunchDarkly. Helps you choose between agent vs completion mode, create the config, add variations with models and prompts, and verify the setup.
Sets up or repairs the AGENTS.md source-of-truth pattern for any project. Creates a well-structured AGENTS.md with real stack info auto-detected from the project, then wires all AI config satellites (.claude/CLAUDE.md, .github/copilot-instructions.md, .agents/rules/, MEMORY.md) to point to it. Eliminates duplication. Always runs in plan mode — asks before acting. Use this skill whenever the user mentions AGENTS.md, agent config, source of truth for AI rules, setting up Claude/Copilot/Cursor for a project, fixing duplicate AI instructions, or wants to consolidate AI configuration files. Trigger even if the user just says "set up agents" or "fix my AI config".
Create and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.
Update, archive, and delete LaunchDarkly AI Configs and their variations. Use when you need to modify config properties, change model parameters, update instructions or messages, archive unused configs, or permanently remove them.
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.
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.
Create and manage prompt snippets — reusable text blocks referenced inside AI Config variation prompts. Keeps common instructions, personas, and guardrails consistent across multiple configs.
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
Give your AI agents capabilities through tools (function calling). Helps you identify what your AI needs to do, create tool definitions, and attach them to AI Config variations.