Total 51,074 skills, AI & Machine Learning has 8555 skills
Showing 12 of 8555 skills
AI text humanization: reduce AI-detection patterns, natural phrasing, tone adjustment
This skill should be used when the user asks to "build an MCP server", "create an MCP", "make an MCP integration", "wrap an API for Claude", "expose tools to Claude", "make an MCP app", or discusses building something with the Model Context Protocol. It is the entry point for MCP server development — it interrogates the user about their use case, determines the right deployment model (remote HTTP, MCPB, local stdio), picks a tool-design pattern, and hands off to specialized skills.
Agent skill for data-ml-model - invoke with $agent-data-ml-model
Neural pattern training with SONA (Self-Optimizing Neural Architecture), MoE (Mixture of Experts), and EWC++ for knowledge consolidation. Use when: pattern learning, model optimization, knowledge transfer, adaptive routing. Skip when: simple tasks, no learning required, one-off operations.
Guide for implementing Syncfusion Windows Forms AI AssistView (SfAIAssistView) for building conversational AI interfaces in desktop applications. Use this when creating chat interfaces, AI assistants, or chatbots with Windows Forms. Supports OpenAI and Azure OpenAI integration, typing indicators, chat suggestions, message bubbles, and custom views for interactive messaging experiences.
Attach judges to AI Config variations for automatic LLM-as-a-judge evaluation. Create custom judges, configure sampling rates, and monitor quality scores.
One sentence — what this skill does and when Claude should use it.
Update AGENTS.md and agent_docs/ following best practices. Use when modifying agent guidelines, adding new documentation, or restructuring agent instructions.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Auto-activates when working with implementation plans. Triggers on "continue the plan", "next task", "what's the plan status", "run task 2.1", or when user references plans/*.plan.md files. Not for creating plans - use /superplan command for that.
Hamilton Helmer's 7 Powers framework applied to a business. Spawns a team of specialist agents — Power Cartographer, Lifecycle Timer, Counter-Positioning Scout, and Moat Devil's Advocate — who each apply a distinct lens from Helmer's taxonomy. The lead synthesizes into a Power Inventory (what you have), Power Pipeline (what's achievable given your stage), and the honest Helmer Verdict. Use when the user says "helmer this", "apply 7 powers", "what power does this company have", "is this a moat", "diagnose my competitive position", or proposes a business and wants strategic analysis. Works standalone or after /thiel (which confirms you need a monopoly) or /munger (which asks if the economics are durable).