Total 31,285 skills, AI & Machine Learning has 5066 skills
Showing 12 of 5066 skills
Dynamic plugin enumeration and capability mapping with delegation priority and explicit routing announcements
This skill should be used when the user asks to "integrate DSPy with Haystack", "optimize Haystack prompts using DSPy", "use DSPy to improve Haystack pipeline", mentions "Haystack pipeline optimization", "combining DSPy and Haystack", "extract DSPy prompt for Haystack", or wants to use DSPy's optimization capabilities to automatically improve prompts in existing Haystack pipelines.
This skill should be used when the user asks to "create a ReAct agent", "build an agent with tools", "implement tool-calling agent", "use dspy.ReAct", mentions "agent with tools", "reasoning and acting", "multi-step agent", "agent optimization with GEPA", or needs to build production agents that use tools to solve complex tasks.
This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.
Create production-ready skills from expert knowledge. Extracts domain expertise and system ontologies, uses scripts for deterministic work, loads knowledge progressively. Use when building skills that must work reliably in production.
This skill should be used when the user asks to "fine-tune a DSPy model", "distill a program into weights", "use BootstrapFinetune", "create a student model", "reduce inference costs with fine-tuning", mentions "model distillation", "teacher-student training", or wants to deploy a DSPy program as fine-tuned weights for production efficiency.
Autonomous prior art search and analysis agent. Searches multiple databases, analyzes references, creates claim charts, and assesses patentability impact.
Builds Model Context Protocol (MCP) servers for Claude with tools, resources, and prompts. Use when users request "create MCP server", "build Claude tool", "MCP integration", or "custom Claude tools".
Auto-Claude performance optimization and cost management. Use when optimizing token usage, reducing API costs, improving build speed, or tuning agent performance.
Official GitHub Model Context Protocol Server for repository management.
Compares old vs new prompts across test cases with diff summaries, stability metrics, breakage analysis, and fix suggestions. Use for "prompt testing", "A/B testing prompts", "prompt versioning", or "quality regression".
Use when writing or refactoring Ruby code that integrates Claude Code via the claude-agent-sdk gem (ClaudeAgentSDK.query, ClaudeAgentSDK::Client, streaming input, ClaudeAgentOptions configuration, tools/permissions, MCP servers, hooks, structured output, budgets, sandboxing, session resumption/rewind, and Rails patterns like jobs or ActionCable).