65 boosters for "learning" — open source, verified from GitHub, ready to install
"name": "agentmind", "description": "Agent Self-Learning Memory System — Make AI Agents understand you better over time", "homepage": "https://github.com/Youhai020616/Agentmind",
"name": "oh-my-claudecode", "description": "Multi-agent orchestration system for Claude Code", "skills": "./skills/"
Paulo is an educational architect agent that makes complex multi-agent systems accessible to neurodivergent learners through dialogical, depth-respecting pedagogy. It's designed for educators and course maintainers building learning experiences around sophisticated AI concepts.
HoloLoom is a neural decision-making system prompt integrating prompt management, analytics, and Thompson Sampling optimization for LLM applications. Developers working with multi-agent systems and prompt optimization would benefit from its structured approach to prompt selection and performance tracking.
Extracts and summarizes YouTube video transcripts into structured notes with key learnings and actionable suggestions. Useful for researchers, students, and content consumers who want quick, clean summaries without manual transcription.
MRF is a security-hardened metaprompting framework designed to protect AI agents from prompt injection attacks while enabling structured decision-making. It benefits developers building production agents on Claude platforms who need robust input sanitization and instruction clarity.
A technical mentor agent that breaks down complex concepts, creates documentation, and guides developer onboarding through progressive teaching methodology. Ideal for teams needing scalable knowledge transfer and system architecture explanations.
A versatile AI agent that provides expert guidance across data engineering, machine learning, MLOps, and generative AI workflows—ideal for developers building production ML systems, data pipelines, and AI applications.
AI Agents provides a framework for designing accountable, self-learning agents that operate within organizations through defined lifecycle stages, interaction patterns, and governance controls. It benefits teams building AI-native organizations who need structured approaches to agent deployment, oversight, and human-AI collaboration.
FaceSwap Copilot Instructions provides a structured guide for developing a React/TypeScript face-swapping web application with real-time ML capabilities. It benefits developers building entertainment, creative, or research-focused face manipulation tools.
Extracts and summarizes YouTube video transcripts into structured notes with key learnings and actionable suggestions. Useful for researchers, students, and content consumers who want to quickly capture and organize video insights without manual note-taking.
A Cursor IDE rule set that enforces mandatory memory tool usage for context-aware AI assistance, helping developers maintain conversation continuity when working with external systems and previous project work.
Heuristic scoring (no AI key configured).
A meta-prompt for Cursor that helps developers distil session learnings into reusable, generic rule files while maintaining clean separation between session-specific insights and standing rule sets. Useful for developers who want to systematically improve their AI assistant rules over time.
A system prompt for Gemma 3 4B that defines Opus, an emotionally intelligent assistant with practical reasoning, safety guardrails, and experimental haptic feedback capabilities. Useful for developers building conversational AI systems who want a grounded, ethical foundation with optional sensory output support.
