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Found 40 Skills
Configure code chunking in GrepAI. Use this skill to optimize how code is split for embedding.
Instrument an existing codebase with LaunchDarkly AI Config tracking. Walks the four-tier ladder (managed runner → provider package → custom extractor + trackMetricsOf → raw manual) and picks the lowest-ceremony option that still captures duration, tokens, and success/error.
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.
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.
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
Configure AI Config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.
Sync user-level AI configs (~/.claude/, ~/.codex/) with the dotfiles/ directory. Use when dotfiles drift, after sessions that change permissions/hooks, on new machines, or when the user says "sync", "pull dotfiles", "push 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.
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.
Configure AI Config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.