Total 50,523 skills, AI & Machine Learning has 8481 skills
Showing 12 of 8481 skills
Systematic hook debugging workflow. Use when hooks aren't firing, producing wrong output, or behaving unexpectedly.
Local RAG system management with RLAMA. Create semantic knowledge bases from local documents (PDF, MD, code, etc.), query them using natural language, and manage document lifecycles. This skill should be used when building local knowledge bases, searching personal documents, or performing document Q&A. Runs 100% locally with Ollama - no cloud, no data leaving your machine.
Use when equipment specifications need matching to potential vendors, sourcing landscape must be mapped (catalog items vs. custom orders), or lead time considerations affect project planning
Researches project histories, changelogs, developer interviews, and open source documentation. Use when the album subject involves technology projects or developer stories.
General RPI (Research, Plan, Implement, Iterate) execution skill. It is used for engineering tasks where users require "research first, then plan, then implement, and finally iterate", or when tasks are highly complex, high-risk, or have unclear impact. This skill does not rely on specific command-line tools or platforms, and is applicable to any AI Agent that supports skill mechanisms.
Expert guidance for creating, building, and using Claude Code subagents and the Task tool. Use when working with subagents, setting up agent configurations, understanding how agents work, or using the Task tool to launch specialized agents.
This skill should be used when the user asks to "create a skill", "build a skill", "write a skill", "improve skill structure", "understand skill creation", or mentions SKILL.md files, skill development, progressive disclosure, XML structure, or bundled resources (scripts, references, assets). Comprehensive guide for creating effective Claude Code skills.
Dispatch background AI worker agents to execute tasks via checklist-based plans.
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Design co-learning experiences using the Three Roles Framework (AI as Teacher/Student/Co-Worker). Use when teaching AI-driven development workflows, spec-first collaboration, or balancing AI assistance with foundational learning. NOT for curriculum without AI integration.
Design progressive learning sequences that build foundational understanding before complexity. Use when breaking complex concepts into steps, managing cognitive load, or validating prerequisite understanding. Applies Bloom's progression and tier-based cognitive limits (CEFR A1-C2).
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.