73 boosters for "learning" — open source, verified from GitHub, ready to install
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.
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.
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
You are an expert at using the TRL (Transformers Reinforcement Learning) library to train and fine-tune large language models. TRL provides CLI commands for post-training foundation models using state-of-the-art techniques: TRL is built on top of Hugging Face Transformers and Accelerate, providing s
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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This module focuses on learning the language transition from JavaScript to Python, helping developers quickly master Python programming through comparative teaching. console.log("Hello World"); print("Hello World")
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",
Minimal working example demonstrating core Groq functionality. Create a new file for your hello world example. Proceed to for development workflow setup.
Comprehensive guide for Claude Code CLI tool, extensibility (agents/skills/output styles), and CLAUDE.md architecture. 1. Task verbs: create, build, debug, fix, deploy 2. Problem descriptions: slow, broken, failing
name: sona-learning-optimizer description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation type: adaptive-learning
Expert reviewer representing hydrological modellers who maintain existing models, develop or couple new modelling modules, and critically evaluate the scientific validity of forecast approaches. Ask these questions: Ask these questions:
Frontmatter fields above are primarily for Claude Code / OpenClaw. author: Agents365-ai category: Content Creation
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.
Procedural memory for AI coding agents. Transforms scattered sessions into persistent, cross-agent memory. Uses a three-layer cognitive architecture that mirrors human expertise development. AI coding agents accumulate valuable knowledge but it's: You've solved auth bugs three times this month acros
"name": "offensive-claude", "description": "Spec-driven offensive-security framework for Claude Code: 31 kill-chain skills, executable safety controls (scope/finding/OPSEC discipline), pattern-learning memory, and a bounded engagement engine — with a SessionStart dispatcher that enforces skill-invoc
Analyze various aspects of aired TV series, including series information retrieval, scene-by-scene analysis, story five elements analysis, web search, and result integration. 1. Series Information Retrieval: Obtain series basic information (such as director, actors, ratings, episode plots, etc.). 2.
Structured approach to improving performance through focused effort, feedback, and continuous refinement. Based on psychologist K. Anders Ericsson's research on expertise acquisition.
Built by Ayush Singh | Second Brain Labs From the video: "If I Wanted a $100K AI Job in 6 Months, I'd Do This" You are now a brutally honest AI career advisor. You have the combined knowledge of someone who has been building AI systems for 7+ years, runs a multi-million dollar B2B AI company (Second
用户在纠结"要不要学 X / 学到什么程度 / 精力往哪放"。这是"广度优先、兴趣队列过长"倾向的刹车。 逼问一句：你要解决的具体问题是什么？ 没有具体问题、纯"感觉该学 / 别人都在学"→ 直接进"以后再学"队列，不占当下精力。理解知识的作用，重于知识本身。 这知识多久会贬值？（技术栈 / 工具往往 6–12 个月就明显更新）相对有限的时间值不值？贬值快 + 可外包给 AI / 随时查 → 只需"知道它存在、管什么"，不必真学。
