Total 44,046 skills, AI & Machine Learning has 7025 skills
Showing 12 of 7025 skills
Agent skill for automation-smart-agent - invoke with $agent-automation-smart-agent
Apply the ai-env agentic development environment template to the current repo, with intent-preserving merge for existing files
Run an interactive naming session for a project. Use when the user wants to name a project, app, package, tool, or repo. Presents names in rounds, tracks preferences, and refines suggestions based on selections.
Datumbox integration. Manage Organizations, Users, Goals, Filters. Use when the user wants to interact with Datumbox data.
Complete reference for the Galileo AI platform TypeScript/JS SDK for evaluating, observing, and protecting GenAI applications. Use when building Node.js or TypeScript applications that need LLM evaluation, production observability, tracing, or runtime guardrails with Galileo.
Three-way conversation between Hayek, Mises, and Claude. Multi-role discussion from the Austrian School of Economics perspective. Triggers: /dbs-chatroom-austrian, /chatroom-austrian, /austrian-school, "Austrian Chatroom" Austrian economics chatroom: Hayek × Mises × Claude debate. Triggers: /dbs-chatroom-austrian, /chatroom-austrian, /奥派, "Austrian chat"
Run structured multi-agent debates using argue CLI for cross-examined, high-confidence answers. Use when facing strategic decisions, ambiguous trade-offs, architecture debates, or questions where multiple perspectives improve the answer. Triggers on: argue, debate, cross-examine, second opinion, multi-agent, 'Should we X or Y?' with real stakes, consensus-building, risk analysis, or confirmation-bias mitigation.
This skill should be used when the user asks to "design agent tools", "create tool descriptions", "reduce tool complexity", "implement MCP tools", or mentions tool consolidation, architectural reduction, tool naming conventions, or agent-tool interfaces. Part of the context engineering skill suite — also activates when the user mentions "context engineering" or "context-engineering" in the context of designing tools that shape how agents receive and process context.
This skill should be used when a developer wants to autonomously execute all tasks under a fully-specified Epic or Feature — for example "go", "start building", "implement everything", "run the loop", "execute the feature", "build it all", "kick it off". Requires that the Epic/Feature/Task tree is fully written before starting. Chains implement → verify → PR for every task in dependency order, with targeted human-in-the-loop gates for contradictions and ambiguities.
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.
RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.