— · MIT
Self-learning vector memory for AI agents — single-file .rvf cognitive container with HNSW search, episodic Reflexion memory, causal graph + Cypher, 9 RL algorithms, Thompson Sampling bandit, 41 MCP tools, hybrid (BM25 + dense) retrieval, GNN attention. 1
v0.13.0 · MIT
Learn as your AI coding assistant builds. You can outsource your thinking, but you can't outsource your understanding. A learning companion for the vibe coding era.
v0.11.0 · MIT
🎭 cc-best: Turn Claude Code into a full dev team — PM→Lead→Dev→QA autonomous workflow. 44 commands · 19 skills · 8 agents · 43 rules · 30 hooks · auto-learning pipeline. | 一个插件,完整开发团队。
— · Apache-2.0
Self improving agents through iterations
v2.5.0 · MIT
Complete Kaggle integration plugin/skill for AI coding agents — competition reports, dataset/model downloads, notebook execution, and badge collection. Works with Claude Code, Gemini CLI, Cursor, Codex, OpenClaw, and 35+ agents via skills.sh.
v0.2.0 · MIT
ADHD-friendly study companion agent for Claude Code: spaced repetition, small-chunk teaching, Feynman reflection
v0.1.2 · no license
Build with Claude Code without your brain going numb — a plugin that quizzes you on the code it just wrote and gates the session until you pass.
v0.5.3 · MIT
Autoharness — a self-learning skill layer for Claude Code — distills skills from your real sessions, updates them as you work, and prunes the ones that stop getting used. No daemon, no benchmark.
v2.5.0 · MIT
Self-Optimizing Neural Architecture (SONA) for Claude Flow — adaptive learning, trajectory tracking, pattern reuse, 7 RL algorithms (PPO/A2C/DQN/Q-Learning/SARSA/Decision Transformer/Curiosity), Flash Attention, MoE routing, LoRA, EWC++ for continual lear
v2.5.0 · MIT
Ruflo - Enterprise AI agent orchestration platform. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
— · MIT
Claude Code plugin for Elixir/Phoenix/LiveView — 26 specialist agents, Iron Laws enforcement, and Tidewave MCP integration. Plan features with parallel research agents, execute with automatic verification, review with 4-agent parallel audits, and capture learnings as reusable knowledge.