Local-first AI learning workspace — ask, note, review and create around your own materials. Wiki KB, Agents, Skills, creation tools. AI learning workspace for Q&A, note-taking, review, and creative output centered around your own materials. Local-first, multi-model, with a Wiki knowledge base, AI Agents, and creation tools.
Generate educational quizzes from topics, learning goals, or provided documents. Supports multiple question types, difficulty levels, answer keys, explanations, and study-focused assessment. Use when the user wants practice questions, review quizzes, mock tests, self-checks, or exam prep.
Lightweight source-backed research and synthesis for normal Reviva agents. Use when the user asks to summarize materials, compare sources, verify claims, collect evidence, create a short research brief, prepare slide/document inputs, or produce a Markdown/HTML brief without running the full deep-researcher pipeline.
A cross-platform assistant for installing coding-agent skills on Windows, macOS, and Linux. It adapts installation commands and handles common Git, network, and file-path issues.
Guide users through learning by asking questions, giving progressive hints, and helping them reason step by step instead of immediately giving answers. Use for homework help, problem solving, concept learning, exam preparation, programming practice, and document-based learning.
Create professional technical diagrams as standalone animated HTML files with SVG graphics, flowing effects, dark/light theme toggle, and export support. Use this skill when the user asks for system architecture diagrams, infrastructure diagrams, cloud architecture visualizations, security diagrams, network topology…
An AI teacher that uses books, documents, or course materials to teach a subject step by step. It first assesses whether the learner needs a strong, medium, or light level of support and can imitate a teacher's style from supplied subtitles or a video link.
A teaching guide based on five Feynman-inspired thinking models, including checking your assumptions, separating labels from understanding, and making ideas concrete.
A guide to teaching programming through subject analysis, concept explanations, a three-step code teaching method, and core programming ideas.
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