{"projects":[{"name":"OpenClaw","description":"The core platform. Personal AI assistant on your own devices. 25+ messaging channels (WhatsApp, Telegram, Slack, Discord, Signal, iMessage, Teams, Matrix, Feishu, LINE, WeChat, QQ). Voice on macOS/iOS/Android. Live Canvas rendering. Gateway control plane. 358K+ stars.","github":"https://github.com/openclaw/openclaw","repo":"openclaw/openclaw","language":"TypeScript","static_stars":"352.9K","tags":["ai-assistant","multi-platform","voice","canvas"],"category":"core","homepage":null,"paper":null},{"name":"NanoBot","description":"Ultra-lightweight personal AI agent — 99% fewer lines of code than OpenClaw. v0.1.5: Dream skill discovery, mid-turn follow-up injection, WebSocket channel, context auto-compact, notebook editing, multiple MCP servers. Supports Feishu streaming, QQ, WeCom, Kagi web search. From HKUDS (ClawTeam team). 39K+ stars.","github":"https://github.com/HKUDS/nanobot","repo":"HKUDS/nanobot","language":"Python","static_stars":"38.7K","tags":["lightweight","multi-model","MCP","sub-agents"],"category":"core","homepage":null,"paper":null},{"name":"Pi","description":"Independent AI agent harness mono repo from earendil-works (Mario Zechner / badlogic). Includes pi-ai (unified multi-provider LLM API: OpenAI, Anthropic, Google), pi-agent-core (runtime with tool calling + state), pi-coding-agent (interactive CLI), and pi-tui (terminal UI with differential rendering). Companion pi-chat for Slack/chat workflows. Strong supply-chain hardening (pinned deps, min-release-age). Foundation under feynman → Darwin.","github":"https://github.com/earendil-works/pi","repo":"earendil-works/pi","language":"TypeScript","static_stars":"57.3K","tags":["agent-harness","coding-agent","multi-provider-LLM","monorepo"],"category":"core","homepage":"https://pi.dev","paper":null},{"name":"PicoClaw","description":"Tiny, fast, and deployable anywhere. Ultra-efficient AI assistant written in Go by Sipeed. Designed for edge devices and resource-constrained environments. Inspired by NanoBot.","github":"https://github.com/sipeed/picoclaw","repo":"sipeed/picoclaw","language":"Go","static_stars":"27.9K","tags":["edge","IoT","RISC-V","tiny"],"category":"core","homepage":"https://picoclaw.io","paper":null},{"name":"MicroClaw","description":"Agentic AI assistant built with Rust. Runs on <1MB RAM targeting $2-5 MCU hardware like ESP32. The smallest member of the Claw family for sensor-level embedded AI.","github":"https://github.com/microclaw/microclaw","repo":"microclaw/microclaw","language":"Rust","static_stars":"636","tags":["embedded","MCU","ESP32","rust"],"category":"core","homepage":null,"paper":null},{"name":"Clawith","description":"OpenClaw for Teams. Multi-agent collaboration with persistent identity, long-term memory, and autonomous 'Aware' consciousness system. 6 trigger types: cron, once, interval, poll, on_message, webhook.","github":"https://github.com/dataelement/Clawith","repo":"dataelement/Clawith","language":"Python","static_stars":"3K","tags":["teams","multi-agent","awareness","FastAPI"],"category":"team","homepage":null,"paper":null},{"name":"MetaClaw","description":"Self-learning agent that evolves from every conversation. LoRA-based continual learning + reinforcement learning, no GPU cluster needed. Supports OpenClaw, NanoBot, PicoClaw backends.","github":"https://github.com/aiming-lab/MetaClaw","repo":"aiming-lab/MetaClaw","language":"Python","static_stars":"3.5K","tags":["meta-learning","LoRA","self-evolving","RL"],"category":"team","homepage":null,"paper":"https://arxiv.org/abs/2603.17187"},{"name":"EvoScientist","description":"Self-evolving multi-agent AI research system. Harness Vibe Research — 6 sub-agents (plan, research, code, debug, analyze, write) that evolve their capabilities over time. Built on DeepAgents framework. PyPI installable. Multi-platform: CLI, Telegram, Slack, WeChat. Apache 2.0 license.","github":"https://github.com/EvoScientist/EvoScientist","repo":"EvoScientist/EvoScientist","language":"Python","static_stars":"2.8K","tags":["AI4Science","multi-agent","auto-research","award-winning"],"category":"science","homepage":"https://EvoScientist.ai","paper":"https://arxiv.org/abs/2603.08127"},{"name":"MedgeClaw","description":"AI research assistant for biomedicine built on Claude Code with 140 K-Dense scientific skills. Real-time research dashboard, RStudio (:8787), JupyterLab (:8888), and Feishu rich card output.","github":"https://github.com/xjtulyc/MedgeClaw","repo":"xjtulyc/MedgeClaw","language":"TeX","static_stars":"856","tags":["biomedicine","RNA-seq","drug-discovery","clinical"],"category":"science","homepage":null,"paper":null},{"name":"OmicsClaw","description":"Local-first cross-session AI assistant for life science researchers. 63 built-in skills across 6 omics domains (spatial transcriptomics, scRNA-seq, bulk RNA-seq, genomics, proteomics, metabolomics). Multi-Provider (Anthropic/OpenAI/DeepSeek/local LLM), Multi-Channel (CLI + Feishu/WeChat/Telegram sync), MCP + dynamic GitHub skill install. Persistent memory. 100% local — no data leaves your machine.","github":"https://github.com/TianGzlab/OmicsClaw","repo":"TianGzlab/OmicsClaw","language":"Python","static_stars":"114","tags":["6-omics","63-skills","multi-provider","local-first","cross-session-memory"],"category":"science","homepage":null,"paper":null},{"name":"LabClaw","description":"Operating layer for LabOS — Stanford-Princeton AI Co-Scientists project. Automated scientific co-discovery workflows for laboratory research.","github":"https://github.com/wu-yc/LabClaw","repo":"wu-yc/LabClaw","language":"Python","static_stars":"945","tags":["Stanford","Princeton","lab-automation","co-discovery"],"category":"science","homepage":null,"paper":null},{"name":"MedClaw (zteyesreal)","description":"Early-stage medical AI project exploring agent-based approaches for clinical decision support and medical data analysis.","github":"https://github.com/zteyesreal/medclaw","repo":"zteyesreal/medclaw","language":"Python","static_stars":"60","tags":["medical","clinical","early-stage"],"category":"science","homepage":null,"paper":null},{"name":"OpenMAIC","description":"Open Multi-Agent Interactive Classroom by Tsinghua University. One-click immersive AI classroom with multi-agent learning experience. Published in JCST'26. Live demo at open.maic.chat. Built with Next.js 16 + React 19 + LangGraph. Vercel one-click deploy. OpenClaw integration. 15K+ stars.","github":"https://github.com/THU-MAIC/OpenMAIC","repo":"THU-MAIC/OpenMAIC","language":"TypeScript","static_stars":"14.7K","tags":["Tsinghua","classroom","multi-agent","PBL"],"category":"education","homepage":"https://open.maic.chat","paper":"https://doi.org/10.1007/s11390-025-6000-0"},{"name":"ZeroClaw","description":"Fast, small, and fully autonomous AI assistant infrastructure. Deploy anywhere, swap anything. Written in Rust with extreme performance focus — the infrastructure layer for self-hosted AI agents.","github":"https://github.com/zeroclaw-labs/zeroclaw","repo":"zeroclaw-labs/zeroclaw","language":"Rust","static_stars":"29.8K","tags":["infrastructure","autonomous","deploy-anywhere","Rust"],"category":"core","homepage":null,"paper":null},{"name":"NanoClaw","description":"Lightweight alternative to OpenClaw that runs in containers for security. Connects to WhatsApp, Telegram, Slack, Discord, Gmail. Built on Anthropic Agents SDK with memory and scheduled jobs.","github":"https://github.com/qwibitai/nanoclaw","repo":"qwibitai/nanoclaw","language":"TypeScript","static_stars":"27K","tags":["container","secure","Anthropic-SDK","lightweight"],"category":"core","homepage":null,"paper":null},{"name":"ClawX","description":"Desktop app providing a graphical interface for OpenClaw AI agents. Turns CLI-based AI orchestration into a visual desktop experience without using the terminal.","github":"https://github.com/ValueCell-ai/ClawX","repo":"ValueCell-ai/ClawX","language":"TypeScript","static_stars":"6.3K","tags":["desktop","GUI","agent-orchestration","visual"],"category":"team","homepage":null,"paper":null},{"name":"ClawBio","description":"The first bioinformatics-native AI agent skill library. Local-first, reproducible, built on OpenClaw. Covers genomics, population genetics, and equity-focused bioinformatics workflows.","github":"https://github.com/ClawBio/ClawBio","repo":"ClawBio/ClawBio","language":"Python","static_stars":"678","tags":["bioinformatics","genomics","local-first","reproducible"],"category":"science","homepage":null,"paper":null},{"name":"BioClaw","description":"AI-powered bioinformatics research assistant built on OpenClaw. Integrates PubMed literature search and PyMOL molecular visualization for streamlined biological research workflows.","github":"https://github.com/Runchuan-BU/BioClaw","repo":"Runchuan-BU/BioClaw","language":"TypeScript","static_stars":"323","tags":["bioinformatics","PubMed","PyMOL","research-assistant"],"category":"science","homepage":"https://bioclaw.tech","paper":null},{"name":"EdgeClaw","description":"Edge-Cloud Collaborative AI Agent built on OpenClaw — bringing the Claude Code experience to open source. v2.0 features ClawXMemory (multi-layered structured long-term memory with proactive reasoning) and ClawXRouter (LLM-as-Judge cost-saving router achieving 58% savings). Three-tier privacy (S1 Passthrough / S2 Desensitization / S3 Local), visual Dashboard, zero-config setup. By THUNLP, RUC, OpenBMB.","github":"https://github.com/Openbmb/EdgeClaw","repo":"Openbmb/EdgeClaw","language":"TypeScript","static_stars":"1.2K","tags":["edge-cloud","privacy","memory","cost-saving","Tsinghua","OpenBMB"],"category":"core","homepage":null,"paper":null},{"name":"MolClaw","description":"Light, practical, memory-enabled AI orchestrator for science. Built on NanoClaw. Adds OpenAI/OpenRouter providers, Discord/WhatsApp channels, runtime skills, durable session memory, and real-time agent execution dashboard.","github":"https://github.com/IDEA-XL/MolClaw","repo":"IDEA-XL/MolClaw","language":"TypeScript","static_stars":"19","tags":["molecular","bioinformatics","NanoClaw-fork","dashboard"],"category":"science","homepage":null,"paper":null},{"name":"PopGenAgent","description":"Tool-aware, reproducible, report-oriented AI workflows for population genomics. Template-driven execution with publication-quality figures, full provenance tracking, and cost-aware LLM routing.","github":"https://github.com/ai4nucleome/POPGENAGENT","repo":"ai4nucleome/POPGENAGENT","language":"Python","static_stars":"12","tags":["population-genetics","genomics","reproducible","report-generation"],"category":"bio-omics","homepage":null,"paper":null},{"name":"Bioinfor-Claw","description":"24/7 bioinformatics copilot — 50 specific skills across 10 application scenarios (data access, multi-omics, CRISPR, gene-list, structure, ML, plotting, literature, lab tracking). Dual nature: standalone agent with browser-based chat UI + modular SKILL.md library that plugs into OpenClaw, Claude Code, or any custom agent. Multi-LLM backend (Anthropic, OpenAI, Google, Mistral, MiniMax, OpenAI-compatible).","github":"https://github.com/MDhewei/bioinfor-claw","repo":"MDhewei/bioinfor-claw","language":"HTML","static_stars":"7","tags":["bioinformatics","copilot","skill-library","openclaw","multi-LLM"],"category":"bio-omics","homepage":null,"paper":null},{"name":"RD-Agent","description":"Microsoft R&D automation platform. AI-driven data and model research — automates hypothesis generation, experiment design, and iterative improvement for industrial and scientific R&D.","github":"https://github.com/microsoft/RD-Agent","repo":"microsoft/RD-Agent","language":"Python","static_stars":"12.4K","tags":["Microsoft","R&D-automation","data-driven","industrial"],"category":"general-research","homepage":null,"paper":null},{"name":"AI-Scientist-v2","description":"Workshop-level automated scientific discovery via agentic tree search. By Sakana AI. Autonomously generates research papers through iterative hypothesis testing and experimentation.","github":"https://github.com/SakanaAI/AI-Scientist-v2","repo":"SakanaAI/AI-Scientist-v2","language":"Python","static_stars":"5.3K","tags":["Sakana-AI","paper-generation","agentic-tree-search","autonomous"],"category":"general-research","homepage":null,"paper":null},{"name":"DATAGEN","description":"AI-driven multi-agent research assistant automating hypothesis generation, data analysis, and report writing. Full research pipeline from question to publication-ready report.","github":"https://github.com/starpig1129/DATAGEN","repo":"starpig1129/DATAGEN","language":"Python","static_stars":"1.7K","tags":["hypothesis-generation","data-analysis","report-writing","multi-agent"],"category":"general-research","homepage":null,"paper":null},{"name":"AIAgents4Pharma","description":"Multi-agent platform for pharmaceutical R&D. Covers drug discovery, drug development, and clinical pipeline automation. Built by VirtualPatientEngine.","github":"https://github.com/VirtualPatientEngine/AIAgents4Pharma","repo":"VirtualPatientEngine/AIAgents4Pharma","language":"Python","static_stars":"76","tags":["pharma","drug-discovery","LangGraph","knowledge-graph"],"category":"drug-molecular","homepage":null,"paper":null},{"name":"OpenLens AI","description":"Fully autonomous multimodal research agent. Combines vision and text understanding for scientific discovery. Chinese academic origin with bilingual support.","github":"https://github.com/jarrycyx/openlens-ai","repo":"jarrycyx/openlens-ai","language":"Python","static_stars":"258","tags":["multimodal","autonomous","vision+text","bilingual"],"category":"specialized","homepage":null,"paper":null},{"name":"BioAgents","description":"AI scientist framework for autonomous deep research in biological sciences. Multi-agent system combining literature analysis with data scientist agents for iterative scientific discovery.","github":"https://github.com/bio-xyz/BioAgents","repo":"bio-xyz/BioAgents","language":"TypeScript","static_stars":"124","tags":["biology","literature-analysis","data-science","iterative-discovery"],"category":"specialized","homepage":null,"paper":null},{"name":"ChemGraph","description":"Agentic framework for computational chemistry and materials science workflows. From Argonne National Laboratory (ALCF). Orchestrates chemistry simulation pipelines.","github":"https://github.com/argonne-lcf/ChemGraph","repo":"argonne-lcf/ChemGraph","language":"Python","static_stars":"90","tags":["Argonne","chemistry","materials-science","simulation"],"category":"drug-molecular","homepage":null,"paper":null},{"name":"Open Co-Scientist","description":"Open-source adaptation of Google DeepMind's AI Co-Scientist. 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Multi-agent bioinformatics system by OpenTechBio.","github":"https://github.com/OpenTechBio/CARIBOU","repo":"OpenTechBio/CARIBOU","language":"Jupyter Notebook","static_stars":"2","tags":["bioinformatics","omics","multi-agent","early-stage"],"category":"bio-omics","homepage":null,"paper":null},{"name":"AI-Researcher","description":"Autonomous scientific innovation system from HKUDS. NeurIPS 2025 paper. End-to-end research automation: literature review, hypothesis generation, experiment design, and paper writing. Production version at novix.science.","github":"https://github.com/HKUDS/AI-Researcher","repo":"HKUDS/AI-Researcher","language":"Python","static_stars":"5.1K","tags":["NeurIPS-2025","autonomous","paper-writing","HKUDS"],"category":"general-research","homepage":"https://novix.science/chat","paper":null},{"name":"AutoBA","description":"An AI agent for fully automated multi-omic analyses. Supports RNA-seq, scRNA-seq, spatial transcriptomics, WGS/WES, and ChIP-seq with automated code repair.","github":"https://github.com/JoshuaChou2018/AutoBA","repo":"JoshuaChou2018/AutoBA","language":"Python","static_stars":"226","tags":["multi-omics","RNA-seq","spatial-transcriptomics","self-debugging"],"category":"bio-omics","homepage":null,"paper":"https://doi.org/10.1002/advs.202407094"},{"name":"BioDiscoveryAgent","description":"LLM-based AI agent for closed-loop design of genetic perturbation experiments. Autonomous reasoning for biological discovery.","github":"https://github.com/snap-stanford/BioDiscoveryAgent","repo":"snap-stanford/BioDiscoveryAgent","language":"Python","static_stars":"102","tags":["genetic-perturbation","experiment-design","Stanford"],"category":"specialized","homepage":null,"paper":null},{"name":"Virtual Lab","description":"A virtual lab of LLM agents for science research. Multi-agent system for nanobody design with principal investigator agent coordination.","github":"https://github.com/zou-group/virtual-lab","repo":"zou-group/virtual-lab","language":"Python","static_stars":"660","tags":["multi-agent","nanobody-design","protein-engineering","Stanford"],"category":"drug-molecular","homepage":null,"paper":null},{"name":"MDCrow","description":"Molecular dynamics simulations with an LLM agent. Automates simulation setup, execution, and analysis using natural language.","github":"https://github.com/ur-whitelab/MDCrow","repo":"ur-whitelab/MDCrow","language":"Python","static_stars":"234","tags":["molecular-dynamics","simulation","computational-chemistry"],"category":"drug-molecular","homepage":null,"paper":null},{"name":"SpatialAgent","description":"An AI agent for spatial biology by Genentech. 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From the same group as POPGENAGENT.","github":"https://github.com/ai4nucleome/BioMaster","repo":"ai4nucleome/BioMaster","language":"Python","static_stars":"91","tags":["bioinformatics","multi-agent","nucleome"],"category":"bio-omics","homepage":null,"paper":null},{"name":"CellVoyager","description":"AI CompBio agent that autonomously analyzes biological data and generates new insights. From Stanford Zou Group. Published in Nature Methods. Specializes in scRNA-seq with self-debugging and hypothesis generation.","github":"https://github.com/zou-group/CellVoyager","repo":"zou-group/CellVoyager","language":"Python","static_stars":"202","tags":["Stanford","Nature Methods","scRNA-seq","autonomous-analysis"],"category":"bio-omics","homepage":null,"paper":"https://www.nature.com/articles/s41592-026-03029-6"},{"name":"GeneClaw","description":"Self-evolving AI agent framework with 5-layer safety gatekeeper. Agents observe failures, propose fixes, and safely apply them. 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Public leaderboard at pinchbench.com. Auto-grading + LLM judge. Tests what actually matters: tool usage, multi-step reasoning, and practical outcomes.","github":"https://github.com/pinchbench/skill","repo":"pinchbench/skill","language":"Python","static_stars":"965","tags":["benchmark","evaluation","coding-agent","leaderboard","OpenClaw"],"category":"benchmark","homepage":"https://pinchbench.com","paper":null},{"name":"HeurekaBench","description":"ICLR 2026 framework to create benchmarks and evaluate AI co-scientists in experimental data-driven scientific research. From EPFL Machine Learning & Bioinformatics Lab.","github":"https://github.com/mlbio-epfl/HeurekaBench","repo":"mlbio-epfl/HeurekaBench","language":"Python","static_stars":"11","tags":["benchmark","evaluation","AI-co-scientist","ICLR","EPFL"],"category":"benchmark","homepage":null,"paper":"https://arxiv.org/abs/2601.01678"},{"name":"ClawKeeper","description":"Security scanner for AI agent hosts. 42 checks across 5 phases: host hardening, network, prerequisites, installation, security audit. Auto-fix for firewalls, encryption, permissions. Credential exposure detection in configs/history/logs. Pure bash, zero dependencies. By RAD Security.","github":"https://github.com/rad-security/clawkeeper","repo":"rad-security/clawkeeper","language":"Bash","static_stars":"10","tags":["security","scanner","hardening","audit","OpenClaw"],"category":"security","homepage":"https://clawkeeper.dev","paper":null},{"name":"ClawVault","description":"OpenClaw security vault for AI agents. Sensitive data detection (API keys, PII, credit cards, 15+ pattern types), prompt injection defense, dangerous command guard (rm -rf, curl|bash), auto-sanitisation with placeholder restoration, token budget control, real-time web dashboard. Transparent proxy gateway sits between AI tools and external APIs. From Tophant.","github":"https://github.com/tophant-ai/ClawVault","repo":"tophant-ai/ClawVault","language":"Python","static_stars":"1.2K","tags":["security","prompt-injection","sensitive-data","audit","vault"],"category":"security","homepage":null,"paper":null},{"name":"NeuriCo","description":"Autonomous research framework from University of Chicago Human+AI Lab. Input a title, domain, and hypothesis — agents handle literature review, experiment design, code execution, analysis, and LaTeX paper writing. Supports Claude Code, Codex, and Gemini CLI. IdeaHub platform for community research ideas. Docker-ready, domain-agnostic.","github":"https://github.com/ChicagoHAI/NeuriCo","repo":"ChicagoHAI/NeuriCo","language":"Python","static_stars":"83","tags":["autonomous-research","multi-provider","IdeaHub","UChicago","paper-writing"],"category":"general-research","homepage":null,"paper":null},{"name":"DeepScientist","description":"Local-first AI research studio. 15-minute setup, one repo per research quest, visible progress, human takeover anytime. Published at ICLR 2026.","github":"https://github.com/ResearAI/DeepScientist","repo":"ResearAI/DeepScientist","language":"TypeScript","static_stars":"1.9K","tags":["ICLR-2026","local-first","research-studio","human-in-the-loop"],"category":"specialized","homepage":"https://deepscientist.cc","paper":"https://openreview.net/forum?id=cZFgsLq8Gs"},{"name":"autoagent","description":"Autonomous harness engineering. Self-configuring agent runtime that adapts to your workflow. 560 stars in 24 hours.","github":"https://github.com/kevinrgu/autoagent","repo":"kevinrgu/autoagent","language":"Python","static_stars":"3.9K","tags":["harness","autonomous","runtime","self-configuring"],"category":"core","homepage":null,"paper":null},{"name":"StatsClaw","description":"Multi-agent workflow for statistical software development. Information-isolated agents: builder (no ground truth), simulator (no algorithm), tester (independent validation). Cambridge + Stanford.","github":"https://github.com/statsclaw/statsclaw","repo":"statsclaw/statsclaw","language":"Python","static_stars":"55","tags":["Cambridge","Stanford","statistics","R-packages","multi-agent"],"category":"specialized","homepage":"https://statsclaw.ai","paper":"https://bit.ly/statsclaw"},{"name":"Open Multi-Agent","description":"TypeScript multi-agent framework. One runTeam() call from goal to result. Auto task decomposition, parallel execution. 3 dependencies, deploys anywhere Node.js runs. 4K stars in 4 days.","github":"https://github.com/JackChen-me/open-multi-agent","repo":"JackChen-me/open-multi-agent","language":"TypeScript","static_stars":"5.5K","tags":["multi-agent","TypeScript","lightweight","parallel-execution"],"category":"team","homepage":null,"paper":null},{"name":"TCM-Agent","description":"LLM-powered multi-agent system for Traditional Chinese Medicine network pharmacology. Compound query (PubChem), target analysis (TTD), molecular similarity (Morgan/MACCS fingerprints), enrichment analysis (GO/KEGG), and TCM-target knowledge graphs. Flask + React frontend with DeepSeek/Doubao support.","github":"https://github.com/AITCM/TCM-Agent","repo":"AITCM/TCM-Agent","language":"Python","static_stars":"11","tags":["tcm","network-pharmacology","drug-discovery","knowledge-graph","multi-agent"],"category":"drug-molecular","homepage":null,"paper":null},{"name":"ClawSafety (Benchmark)","description":"Safety benchmark for personal AI agents under realistic prompt injection. 120 adversarial test cases across 5 harm domains, 3 attack vectors, and 5 harmful action types. Tested Claude, Gemini, GPT-5.1, DeepSeek on OpenClaw/Nanobot/NemoClaw scaffolds. Key finding: chat safety ≠ agent safety.","github":"https://github.com/weibowen555/ClawSafety","repo":"weibowen555/ClawSafety","language":"Python","static_stars":"2","tags":["safety","benchmark","prompt-injection","adversarial"],"category":"benchmark","homepage":null,"paper":"https://arxiv.org/abs/2604.01438"},{"name":"ClawSafety (Scanner)","description":"Security scanner for Agent Skills — the npm audit for the Agent-Native ecosystem. Scans for shell injection, hardcoded secrets, unpinned dependencies, excessive permissions, and prompt injection risks. Rust CLI with GitHub App integration. Inspired by the ClawHavoc incident (341 malicious skills).","github":"https://github.com/relaxcloud-cn/clawsafety","repo":"relaxcloud-cn/clawsafety","language":"Rust","static_stars":"1","tags":["security","scanner","skills","vulnerability"],"category":"security","homepage":null,"paper":null},{"name":"SkillOpt","description":"Text-space optimizer from Microsoft Research that trains reusable natural-language skills for frozen LLM agents — like training a neural net but on a Markdown document. Trajectory-driven add/delete/replace edits, validation-gated updates, textual learning-rate budget. Produces a deployable best_skill.md (300-2,000 tokens). Best or tied-best on all 52 (model × benchmark × harness) cells; lifts GPT-5.5 +19.1 inside Claude Code.","github":"https://github.com/microsoft/SkillOpt","repo":"microsoft/SkillOpt","language":"Python","static_stars":"4K","tags":["Microsoft","skill-evolution","text-space-optimizer","validation-gated"],"category":"skill-evolution","homepage":"https://microsoft.github.io/SkillOpt/","paper":"https://arxiv.org/abs/2605.23904"},{"name":"TextGrad","description":"Automatic differentiation via text — uses LLMs to backpropagate textual gradients across prompts, skills, queries, and code. From Stanford Zou Group (same lab as Virtual Lab, CellVoyager). Published in Nature. The foundational primitive that newer skill-evolution frameworks (SkillOpt, SkillClaw, DSPy optimizers) compose on top of.","github":"https://github.com/zou-group/textgrad","repo":"zou-group/textgrad","language":"Python","static_stars":"3.6K","tags":["Stanford","Nature-paper","text-autograd","primitive"],"category":"skill-evolution","homepage":null,"paper":"https://www.nature.com/articles/s41586-025-08661-4"},{"name":"SkillClaw","description":"Collective skill evolution framework from Alibaba DreamX Team (AMAP-ML). Aggregates real-user trajectories across sessions, agents, devices, and users, then runs an autonomous evolver to identify recurring patterns and refine the shared skill library — improvements discovered in one context propagate to everyone. Compatible with Hermes, OpenClaw, Codex, Claude Code, QwenPaw, IronClaw, PicoClaw, ZeroClaw, NanoClaw, NemoClaw, and any OpenAI-compatible API. Validated on WildClawBench; significantly improves Qwen3-Max.","github":"https://github.com/AMAP-ML/SkillClaw","repo":"AMAP-ML/SkillClaw","language":"Python","static_stars":"1.5K","tags":["Alibaba","DreamX","collective-evolution","multi-user-traces"],"category":"skill-evolution","homepage":null,"paper":"https://arxiv.org/abs/2604.08377"},{"name":"PyMolClaw","description":"Molecular visualization skill powered by PyMOL. Natural language → publication-quality figures. 13 scripts: structure alignment, binding sites, Goodsell-style illustrations, surface rendering, mutation analysis, cryo-EM density maps, and animations.","github":"https://github.com/junior1p/PyMolClaw","repo":"junior1p/PyMolClaw","language":"Python","static_stars":"1","tags":["PyMOL","molecular-visualization","structural-biology","skill"],"category":"drug-molecular","homepage":null,"paper":null},{"name":"MathClaw","description":"Multimodal math learning assistant for middle and high school. Solving workspace, weakness diagnosis, knowledge graphs, error graphs, auto-summaries. Supports WeChat, QQ, Feishu, Telegram, Discord. From Renmin University.","github":"https://github.com/MathClaw-ruc/MathClaw","repo":"MathClaw-ruc/MathClaw","language":"Python","static_stars":"62","tags":["education","mathematics","multimodal","knowledge-graph","Renmin-University"],"category":"education","homepage":null,"paper":null},{"name":"Claw-Eval","description":"End-to-end evaluation suite for autonomous agents. 300 human-verified tasks across 9 categories (service orchestration, multimodal perception, multi-turn dialogue). Trajectory-aware grading over 2,159 rubrics. Tests Completion, Safety, and Robustness. Used by Qwen, GLM, MiniMax.","github":"https://github.com/claw-eval/claw-eval","repo":"claw-eval/claw-eval","language":"Python","static_stars":"352","tags":["benchmark","evaluation","multimodal","trajectory-aware","safety"],"category":"benchmark","homepage":"https://claw-eval.github.io","paper":null},{"name":"BloClaw","description":"Omniscient multi-modal agentic workspace for scientific discovery. Cheminformatics (RDKit), protein folding (ESMFold), molecular docking, autonomous RAG. XML-Regex dual-track routing, runtime sandbox for data visualization. From Beijing 1st Biotech + PLA General Hospital.","github":"https://github.com/qinheming/BIoClaw","repo":"qinheming/BIoClaw","language":"Python","static_stars":"1","tags":["multi-modal","cheminformatics","protein-folding","molecular-docking","RAG"],"category":"drug-molecular","homepage":null,"paper":"https://arxiv.org/abs/2604.00550"},{"name":"HealthClaw","description":"Open-source self-evolving personal health copilot. Medical consultation support, meal planning, report interpretation, chronic-care follow-up, wearable data analytics. Supports clinical data, medical imaging, and omics evidence. Streamlit + Feishu + CLI.","github":"https://github.com/HC-Guo/HealthClaw","repo":"HC-Guo/HealthClaw","language":"Python","static_stars":"255","tags":["health","medical","self-evolving","wearable","omics"],"category":"science","homepage":null,"paper":null},{"name":"BioAgent Bench","description":"AI agent evaluation suite for bioinformatics. Benchmarks LLM agents on real bioinformatics tasks. Covers sequence analysis, genomics workflows, and computational biology pipelines. arXiv paper.","github":"https://github.com/bioagent-bench/bioagent-bench","repo":"bioagent-bench/bioagent-bench","language":"Python","static_stars":"16","tags":["benchmark","bioinformatics","evaluation","LLM-agent"],"category":"benchmark","homepage":null,"paper":"https://arxiv.org/abs/2601.21800"},{"name":"BixBench","description":"Comprehensive benchmark for LLM-based agents in computational biology. 205 questions extracted from 60 real-world published Jupyter notebooks (capsules). Tests dataset exploration, multi-step analysis (Python/R/Bash code execution), and scientific hypothesis generation. Supports both agentic and zero-shot evaluation modes. From Future House (PaperQA / Aviary lab).","github":"https://github.com/Future-House/BixBench","repo":"Future-House/BixBench","language":"Python","static_stars":"94","tags":["benchmark","computational-biology","jupyter-notebook","real-world-data","Future-House","agentic-eval"],"category":"benchmark","homepage":"https://huggingface.co/datasets/futurehouse/BixBench","paper":"https://arxiv.org/abs/2503.00096"},{"name":"Multica","description":"Open-source managed agents platform. Turn coding agents into real teammates — assign tasks, track progress, compound skills. Agents show up on boards, post comments, report blockers autonomously. Works with Claude Code, Codex, OpenClaw. Self-hosted or cloud (multica.ai).","github":"https://github.com/multica-ai/multica","repo":"multica-ai/multica","language":"TypeScript","static_stars":"3874","tags":["managed-agents","task-management","multi-agent","self-hosted"],"category":"team","homepage":"https://multica.ai","paper":null},{"name":"AI-Scientist (v1)","description":"The original fully automated AI scientist from Sakana AI. Autonomously generates ideas, runs experiments, writes research papers, and conducts peer review. First open-source system to close the full research loop. 13K+ stars.","github":"https://github.com/SakanaAI/AI-Scientist","repo":"SakanaAI/AI-Scientist","language":"Jupyter Notebook","static_stars":"13219","tags":["Sakana-AI","end-to-end","autonomous","paper-generation"],"category":"general-research","homepage":null,"paper":null},{"name":"pi-autoresearch","description":"Generic version of Karpathy's autoresearch loop that works on any measurable optimization target. Modify → run → score → keep/discard → repeat. Not limited to ML — works for any codebase with a metric.","github":"https://github.com/davebcn87/pi-autoresearch","repo":"davebcn87/pi-autoresearch","language":"TypeScript","static_stars":"3585","tags":["autoresearch","optimization-loop","generic","any-metric"],"category":"general-research","homepage":null,"paper":null},{"name":"autoresearch (uditgoenka)","description":"Multi-purpose Claude Code plugin: autonomous goal-directed iteration inspired by Karpathy's autoresearch. Modify → Verify → Keep/Discard → Repeat. Also includes debug, fix, security audit, ship, and predict modes.","github":"https://github.com/uditgoenka/autoresearch","repo":"uditgoenka/autoresearch","language":"Shell","static_stars":"3659","tags":["Claude-Code","autoresearch","multi-mode","skill"],"category":"general-research","homepage":null,"paper":null},{"name":"InternAgent","description":"Unified long-horizon scientist framework from InternScience. Links deep research, executable verification, and memory-driven evolution across algorithm and empirical discovery. v1.5 with Web UI.","github":"https://github.com/InternScience/InternAgent","repo":"InternScience/InternAgent","language":"Python","static_stars":"1271","tags":["InternScience","long-horizon","end-to-end","memory-evolution"],"category":"general-research","homepage":null,"paper":null},{"name":"autoresearch-mlx","description":"Karpathy's autoresearch loop adapted for Apple Silicon Macs using MLX. No PyTorch required — runs natively on M-series chips. Overnight autonomous experiment optimization.","github":"https://github.com/trevin-creator/autoresearch-mlx","repo":"trevin-creator/autoresearch-mlx","language":"Python","static_stars":"1406","tags":["Apple-Silicon","MLX","autoresearch","Mac-native"],"category":"general-research","homepage":null,"paper":null},{"name":"Kosmos","description":"Autonomous discovery engine that tests hypotheses in sandboxed containers and tracks findings in a knowledge graph. Driven by Claude Code or API. Based on the Kosmos AI paper (arXiv:2511.02824).","github":"https://github.com/jimmc414/Kosmos","repo":"jimmc414/Kosmos","language":"Python","static_stars":"495","tags":["autonomous-discovery","knowledge-graph","sandboxed","hypothesis-testing"],"category":"general-research","homepage":null,"paper":"https://arxiv.org/abs/2511.02824"},{"name":"ClawBench","description":"Real-world web task benchmark for AI agents. 153 tasks across 15 life categories on 144 live websites. Claude Sonnet 4.6 achieves only 33.3%, GPT-5.4 only 6.5% — highlighting the gap between sandbox and real-world performance. From UBC, Vector Institute, CMU, SJTU, Tsinghua.","github":"https://github.com/reacher-z/ClawBench","repo":"reacher-z/ClawBench","language":"Python","static_stars":"61","tags":["benchmark","web-tasks","real-world","153-tasks","multi-university"],"category":"benchmark","homepage":"https://claw-bench.com","paper":null},{"name":"ClawMark","description":"Multi-day, multimodal coworker agent benchmark. 100 tasks across 13 professional domains (insurance, legal, EDA, etc). Environment changes dynamically — new emails, file updates, schedule shifts. Cross-modal multi-turn with branching logic. Best score only 55%. From NUS, Evolvent AI, HKU, MIT, UW, UC Berkeley, CUHK, HKUST (40+ scholars).","github":"https://github.com/evolvent-ai/clawmark","repo":"evolvent-ai/clawmark","language":"Python","static_stars":"51","tags":["benchmark","multi-day","multimodal","dynamic-environment","NUS-MIT-Berkeley"],"category":"benchmark","homepage":"https://claw-mark.com","paper":null}],"skills":[{"name":"skill-vetter","description":"Security vetting checklist for AI agent skills. 4-step protocol: source authentication, code review with explicit rejection criteria, permission scope evaluation, and risk classification (low → extreme). No code, no dependencies — just a battle-tested review process.","github":null,"repo":null,"tags":["Editor's Pick","security","vetting","3K-downloads","must-have"],"category":"security","install_command":"clawhub install skill-vetter"},{"name":"openalex-database","description":"Search 240M+ scholarly works across all fields. Author/institution analysis, citation tracking, open-access discovery. Completely free, no API key needed. The best all-around starting point.","github":"https://github.com/K-Dense-AI/claude-scientific-skills","repo":"K-Dense-AI/claude-scientific-skills","tags":["Editor's Pick","240M papers","no-key-needed","all-fields"],"category":"literature","install_command":"clawhub install openalex-database"},{"name":"pubmed-database","description":"Best for biomedical research. Advanced Boolean/MeSH queries, batch processing, filter by RCT/meta-analysis/systematic review. The gold standard for life sciences literature.","github":"https://github.com/K-Dense-AI/claude-scientific-skills","repo":"K-Dense-AI/claude-scientific-skills","tags":["biomedical","MeSH","no-key-needed"],"category":"literature","install_command":"clawhub install pubmed-database"},{"name":"arxiv-database","description":"Best for CS/ML/physics/math preprints. Search by keywords, authors, IDs, date ranges, and 8 category groups. No key needed.","github":"https://github.com/K-Dense-AI/claude-scientific-skills","repo":"K-Dense-AI/claude-scientific-skills","tags":["CS/ML/physics","preprints","no-key-needed"],"category":"literature","install_command":"clawhub install arxiv-database"},{"name":"aminer-data-search","description":"Best for exploring scholar networks. 28 APIs for scholar/paper/institution/journal/patent queries. Natural language Q&A like \"latest advances in Transformers\".","github":"https://github.com/CanXiangCC/aminer-open-skill","repo":"CanXiangCC/aminer-open-skill","tags":["scholar-network","patent","28-APIs","needs-key"],"category":"literature","install_command":"cp -r skills/aminer-data-search ~/.claude/skills/"},{"name":"deep-research","description":"The most comprehensive single skill for literature review. 13 agents, 7 modes: full research, quick brief, paper review, PRISMA systematic review with meta-analysis, Socratic dialogue, and fact-check. Unmatched depth.","github":"https://github.com/Imbad0202/academic-research-skills","repo":"Imbad0202/academic-research-skills","tags":["Editor's Pick","13-agents","PRISMA","meta-analysis"],"category":"review","install_command":"git clone https://github.com/Imbad0202/academic-research-skills.git && cp -r academic-research-skills/deep-research ~/.claude/skills/"},{"name":"lit-synthesizer","description":"Best for finding research gaps. PubMed MeSH + bioRxiv/medRxiv search with citation graph construction and gap analysis. Identifies understudied areas. No extra key needed.","github":"https://github.com/ClawBio/ClawBio","repo":"ClawBio/ClawBio","tags":["citation-graph","gap-analysis","no-key-needed"],"category":"review","install_command":"clawhub install lit-synthesizer"},{"name":"literature-review","description":"Solid all-rounder for systematic reviews. PRISMA-style searches, cross-database synthesis, citation verification. Outputs in APA/Nature/Vancouver format.","github":"https://github.com/K-Dense-AI/claude-scientific-skills","repo":"K-Dense-AI/claude-scientific-skills","tags":["systematic-review","PRISMA","multi-format"],"category":"review","install_command":"clawhub install literature-review"},{"name":"medical-research-toolkit","description":"14+ biomedical databases unified in one skill: ChEMBL, PubMed, ClinicalTrials.gov, OpenTargets, OpenFDA, OMIM, Reactome, KEGG, UniProt. Drug repurposing, target discovery, and more.","github":"https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills","repo":"FreedomIntelligence/OpenClaw-Medical-Skills","tags":["Editor's Pick","14-databases","drug-repurposing","needs-MCP"],"category":"data","install_command":"clawhub install medical-research-toolkit"},{"name":"AIPOCH Evidence Insights","description":"200+ medical research skills covering the full workflow. R/Python bioinformatics code generation, statistical modeling, ML pipelines, data cleaning, and visualization.","github":"https://github.com/aipoch/medical-research-skills","repo":"aipoch/medical-research-skills","tags":["200-skills","R/Python","bioinformatics","OpenClaw-native"],"category":"data","install_command":"bash <(curl -s https://raw.githubusercontent.com/aipoch/medical-research-skills/main/scientific-skills/scripts/openclaw-install.sh)"},{"name":"autoresearch","description":"End-to-end autonomous AI research pipeline: literature survey → experiment → paper. Two-loop architecture for overnight runs. Best for ML/AI research experiments.","github":"https://github.com/Orchestra-Research/AI-Research-SKILLs","repo":"Orchestra-Research/AI-Research-SKILLs","tags":["end-to-end","overnight","ML/AI-research"],"category":"data","install_command":"npx @orchestra-research/ai-research-skills"},{"name":"academic-pipeline","description":"10-stage academic writing pipeline: research → draft → peer review → revise → finalize. Multi-perspective review with 0-100 rubrics. Outputs APA 7.0, IEEE, or Chicago format.","github":"https://github.com/Imbad0202/academic-research-skills","repo":"Imbad0202/academic-research-skills","tags":["Editor's Pick","10-stage","peer-review","APA/IEEE/Chicago"],"category":"writing","install_command":"git clone https://github.com/Imbad0202/academic-research-skills.git && cp -r academic-research-skills/academic-paper ~/.claude/skills/"},{"name":"latex-paper-conversion","description":"Convert academic LaTeX papers between publisher formats: Springer → MDPI, IEEE → Nature, etc. Automates template injection and compilation debugging.","github":"https://github.com/sickn33/antigravity-awesome-skills","repo":"sickn33/antigravity-awesome-skills","tags":["LaTeX","format-conversion","no-key-needed"],"category":"writing","install_command":"clawhub install latex-paper-conversion"},{"name":"citation-management","description":"Multi-source citation tool: Google Scholar + PubMed + CrossRef + arXiv. DOI/PMID/arXiv ID to BibTeX. 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From a topic to a validated, pilot-tested research plan.","github":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep","repo":"wanshuiyin/Auto-claude-code-research-in-sleep","tags":["Editor's Pick","autonomous","idea-to-plan","overnight"],"category":"ideation","install_command":"git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git && cp -r Auto-claude-code-research-in-sleep/skills/idea-discovery ~/.claude/skills/"},{"name":"ARIS /novelty-check","description":"Verify if your research idea is truly novel. Extracts 3-5 core claims, searches each independently, then runs adversarial cross-model review to probe blind spots.","github":"https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep","repo":"wanshuiyin/Auto-claude-code-research-in-sleep","tags":["novelty-verification","adversarial-review","cross-model"],"category":"ideation","install_command":"git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git && cp -r Auto-claude-code-research-in-sleep/skills/novelty-check ~/.claude/skills/"},{"name":"scrna-orchestrator","description":"End-to-end scRNA-seq pipeline in one skill. QC → doublet removal → normalization → HVG → PCA/scVI → UMAP → Leiden → marker genes → cell type annotation → report. Generates a full reproducibility bundle.","github":"https://github.com/ClawBio/ClawBio","repo":"ClawBio/ClawBio","tags":["Editor's Pick","end-to-end","scanpy","reproducible"],"category":"scrna","install_command":"clawhub install scrna-orchestrator"},{"name":"bio-single-cell-cell-annotation","description":"Automated cell type annotation with three engines: CellTypist (deep learning, 100+ tissue models), SingleR (reference-based, R), and Azimuth (Seurat reference mapping). The most comprehensive annotation skill available.","github":"https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills","repo":"FreedomIntelligence/OpenClaw-Medical-Skills","tags":["CellTypist","SingleR","Azimuth","annotation"],"category":"scrna","install_command":"clawhub install bio-single-cell-cell-annotation"},{"name":"scvelo","description":"RNA velocity analysis: cell-state transitions, developmental trajectories, latent time estimation, and driver gene identification. Dynamical mode for maximum accuracy.","github":"https://github.com/K-Dense-AI/claude-scientific-skills","repo":"K-Dense-AI/claude-scientific-skills","tags":["RNA-velocity","trajectory","pseudotime","driver-genes"],"category":"scrna","install_command":"clawhub install scvelo"}],"skill_hubs":[{"name":"Anthropic Skills","description":"Official Agent Skills repository by Anthropic. The canonical source for Claude Code skills — community contributions, curated quality, and production-ready templates.","github":"https://github.com/anthropics/skills","repo":"anthropics/skills","tags":["Anthropic","official","Claude Code","101K stars"],"category":"registry","homepage":null},{"name":"ClawHub","description":"The official OpenClaw skill registry. Publish, version, search, and install text-based agent skills. 6,300+ skills with vector search, starring, commenting, and admin curation.","github":"https://github.com/openclaw/clawhub","repo":"openclaw/clawhub","tags":["official","registry","6300+ skills","vector-search"],"category":"registry","homepage":"https://clawhub.ai"},{"name":"ClawHub China Mirror","description":"Official ClawHub mirror for mainland China. Same 6,300+ skills, accelerated access without VPN. Full registry sync, search, and install support.","github":null,"repo":null,"tags":["official","mirror","China","accelerated"],"category":"registry","homepage":"http://mirror-cn.clawhub.com"},{"name":"Awesome OpenClaw Skills","description":"5,400+ community skills filtered and categorized from the official Skills Registry. Install via clawhub install <slug> or manual copy. Maintained by VoltAgent.","github":"https://github.com/VoltAgent/awesome-openclaw-skills","repo":"VoltAgent/awesome-openclaw-skills","tags":["awesome-list","5400+ skills","categorized","curated"],"category":"general","homepage":null},{"name":"GDM Science Skills","description":"Official Google DeepMind skill collection for agentic scientific workflows. Spans genomics, structural biology, cheminformatics, and literature search. Integrates AlphaGenome, AlphaFold DB (AFDB), UniProt, OpenAlex, ClinVar, and 30+ other databases. Designed for higher token efficiency and better grounding. Bundled with Google Antigravity; install via npx skills add or via Antigravity's Build with Google plugins. Apache-2.0 with a published technical report.","github":"https://github.com/google-deepmind/science-skills","repo":"google-deepmind/science-skills","tags":["Google-DeepMind","official","30+-databases","AlphaGenome","Antigravity"],"category":"science","homepage":"https://antigravity.google/use-cases/science"},{"name":"K-Dense Scientific Skills","description":"170+ ready-to-use skills across 17 scientific domains: bioinformatics, cheminformatics, proteomics, clinical research, materials science, physics, and more. Connected to 250+ databases.","github":"https://github.com/K-Dense-AI/claude-scientific-skills","repo":"K-Dense-AI/claude-scientific-skills","tags":["170+ skills","17 domains","250+ databases","multi-disciplinary"],"category":"science","homepage":null},{"name":"AI Research Skills","description":"86 skills across 22 categories for autonomous AI research. 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