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.

Open source implementation and extension of Google Research’s PaperBanana for automated academic figures, diagrams, and research visuals, expanded to new domains like slide generation.

freephdlabor: customizing personalized multiagent systems that researchs 24/7 on your own scientific problem

Fully Autonomous AI Research System with Self-Evolution, built natively on Claude Code

One file. Your AI agent becomes a scientist. 30+ experiments while you sleep.

Python toolkit for reproducible science. CLI commands, 371+ MCP tools, built-in skills. From raw data to manuscript — with reproducibility verification. For AI and human researchers.

Autonomous AI backend for deep research AI applications.

Research automation skills for Claude Code. Idea to submittable paper in one session.

Karpathy's autoresearch loop for non-ML domains: outreach, prediction markets, prompts

AIRAS - an open-source project for research automation

Distributed multi-agent research engine with dynamic strategy planning, durable stream-based coordination, and controlled synthesis

GitHub template for autonomous strategic research using Claude Code

MCP server for arXiv.org - Search, analyze, and export academic papers with AI assistants. Features advanced paper discovery, citation analysis, trend tracking, and multi-format exports.

arXiv MCP Server Client 🐙 enables AI assistants to search, retrieve, analyze, and summarize arXiv papers with features like author/category browsing, trends, and citation insights.

NodeBench AI is an autonomous research platform. It acts as a team of AI researchers that scour the web, analyze documents, and synthesize decision-ready briefs for founders pitching VCs or investors doing diligence—all fully backed by source evidence and receipts instead of hallucinations.

Three AI models, one synthesis — Claude, Gemini & Codex cross-verify each other for rigorous multi-perspective research

De-Anthropocentric Research Engine — AI-powered academic research automation with deep literature survey, gap analysis, idea generation, experiment design & execution. Combines iterative deep research, adversarial debate, evolutionary generation, and distributed GPU execution.

Web research automation with hierarchical citation trees. A more deterministic alternative to agent-based systems (like OpenAI Deep Research) that builds verifiable reasoning chains, works with smaller LLMs, and prioritizes transparency over black-box agents. Engineered for reliability and cost efficiency.

Autonomous AI experiment loop CLI -- run research overnight with any coding agent

Automation workflow for Google Scholar Alerts e-mails