Schedule spaced reviews and exam study plans from each learner's forgetting curve.

🌳 AI-Powered Skill Tree for Lifelong Human Learning. 30+ skills from K-12 to career & social intelligence, built on cognitive science. | 人类养成记：AI 驱动的终身学习技能树

ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and experiment automation. No framework, no lock-in — works with Claude Code, Codex, OpenClaw, or any LLM agent.

Rigour's MCP server: lets coding agents review their change, check for an existing helper before writing a new one, and recall the team's lessons. Works with Claude Code, Cursor and other MCP clients.

Correction-first persistent memory for AI agents. MCP server + SDK + CLI. Compounds across sessions.

Shared Single-file memory layer for all your agents, sub mili-second RAG over text, photo and video on Apple Silicon.. No Server. No API. One File. Pure Swift

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Free BaZi, I Ching, tarot, Zi Wei and bilingual learning tools with transparent methods. No API key.

Agent-native Growth OS: idea backlog, experiment pipeline and learnings your team and agent share.

🛠️ The meta-harness for AI agents — scaffold your own focused, branded agent harness with its own npx CLI, MCP server, memory, learning loop, and witness-signed releases. Works with Claude Code, Codex, pi.dev, Hermes, OpenClaw, and RVM (hardware-isolated sandbox).

Enables read-only MCP access to structured Brazilian curricular references, supporting learning-objective searches, comparisons across states, and queries tied to versioned publications.

Enables MCP clients to query and diagnose machine-learning training runs through read-only tools for listing experiments, reading configurations and final metrics, comparing runs, sampling metric curves, and detecting training failures.

Enables automation of the QingSoft U+ learning platform by patrolling courses, homework and experiments, inspecting objective questions, filling and submitting answers, and handing subjective work back to a human, while also providing a generic browser automation toolset.

Enables AI assistants to connect to a student's university learning platform through Lightpanda, supporting consultation of courses, activities, deadlines, calendar events, downloadable materials, and assignment submissions via natural-language requests.

Design and edit e-learning courses, lessons and bricks from an AI assistant.

Query xAPI and cmi5 learning statements, KPIs and learner progress from an AI assistant.

Agentic Growth OS: idea backlog, experiment pipeline and learnings your team and AI agent share.

Check lessons and courses against their learning objectives, with each fix written.