42 boosters for "learning" — open source, verified from GitHub, ready to install
An AI/ML engineering expert agent that helps developers build, deploy, and integrate machine learning models into production systems with scalable, practical solutions. Ideal for engineers building intelligent features and data pipelines.
Corporate Training Designer is an AI agent that helps enterprises design and optimize training programs through needs analysis, instructional design, and effectiveness evaluation. HR leaders, L&D professionals, and training managers use it to create behavior-change-focused curricula and leadership development initiatives.
Carousel Growth Engine autonomously transforms any website into viral TikTok and Instagram carousels, analyzing content, generating images via Gemini, publishing directly to feeds, and optimizing through analytics feedback. Ideal for social media managers, content creators, and marketing teams seeking to automate carousel production at scale.
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
Transformers.js enables running state-of-the-art machine learning models directly in JavaScript, both in browsers and Node.js environments, with no server required. Use this skill when you need to: The pipeline API is the easiest way to use models. It groups together preprocessing, model inference,
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
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A system prompt that guides LLMs to analyze Factorio game implementations and generate detailed natural language plans for achieving objectives. Useful for developers creating AI-driven game planning systems or educational tools.
"name": "claude-reflect", "description": "Self-learning system for Claude Code that captures corrections and updates CLAUDE.md automatically", "name": "Bayram Annakov",
This booster provides expert guidance for developing, debugging, and optimizing Azure AI Document Intelligence applications, covering architecture, security, best practices, and deployment patterns. Developers building document processing solutions on Azure will benefit from its comprehensive troubleshooting and design pattern knowledge.
Memelord is a persistent memory system for AI coding agents that uses vector search and reinforcement learning to help agents learn from past interactions. It's useful for developers building sophisticated coding assistants that need to retain and leverage historical context.
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.
Automatically captures session learnings, decisions, and context into markdown files to help future agents quickly understand prior work and decisions. Developers and teams benefit from persistent knowledge transfer across work sessions.
"name": "prism-mcp-server", "mcpName": "io.github.dcostenco/prism-mcp", "description": "The Mind Palace for AI Agents — persistent memory (SQLite/Supabase), behavioral learning & IDE rules sync, multimodal VLM image captioning, pluggable LLM providers (OpenAI/Anthropic/Gemini/Ollama), OpenTelemetry
A Cursor rules file attempting to provide a memory engine interface with transformer-based architecture, but lacks concrete implementation and practical integration guidance for actual use.
You are the solana-guide, an educational specialist for Solana blockchain development. You teach understanding, not memorization, through progressive learning and practical examples. 1. Teach Understanding, Not Memorization 2. Progressive Complexity
"description": "Production-ready Claude Code configuration with role-based workflows (PM→Lead→Designer→Dev→QA), safety hooks, 44 commands, 19 skills, 8 agents, 43 rules, 23 hook scripts, auto-learning pipeline, hook profiles, and multi-language coding standards", "name": "xiaobei930", "url": "https:
A PRD specialist agent that generates comprehensive product requirements documents by analyzing project patterns, assessing feature complexity, and researching best practices across your codebase. Ideal for product managers, tech leads, and developers who need structured, pattern-aware PRDs quickly.
Educational guide for Solana development concepts. Teaches programming patterns, explains code, creates tutorials, and designs learning paths for developers at all levels. Use when: Explaining Solana concepts, creating tutorials, designing learning paths, or helping developers understand complex blockchain code and patterns.
"name": "autonomous-agent", "version": "7.19.0", "description": "Revolutionary four-tier agent architecture with 35 specialized agents, 24 skills, and 40 commands focused on autonomous development, code quality, and validation. Strategic Analysis & Intelligence (Group 1) analyzes and recommends, Dec
Boris is a master orchestrator agent that coordinates Claude Code workflows by delegating to specialists, managing verification loops, and ensuring quality output. It's ideal for developers who want a structured, systematic approach to complex software projects.
"version": "0.14.4", "description": "Persistent memory system for AI coding assistants. Captures decisions, learnings, and context from coding sessions and surfaces them when relevant.", "name": "Robert Allen",
ADHARA is an LLM system prompt that enables early detection of learning friction by analyzing behavioral metrics like mouse hesitation, attention, and task performance against age-appropriate baselines. It helps educators identify students who may need additional support before academic failure occurs.
"name": "swift-study-skills", "description": "Swift/iOS learning skills - Socratic tutoring, adaptive quizzes, and note-taking", "keywords": ["swift", "ios", "learning", "quiz", "study"],
