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Source channel @githubtrending · Post #15534 · Mar 1

#python#agent_skills#ai_scientist#bioinformatics#chemoinformatics#claude#claude_skills#claudecode#clinical_research#computational_biology#data_analysis#drug_discovery#genomics#materials_science#metabolomics#proteomics#scientific_computing#scientific_visualization Claude Scientific Skills offers 148+ ready-to-use tools for AI agents like Cursor or Claude Code, covering biology, chemistry, drug discovery, clinical research, ML, and 250+ databases (PubMed, ChEMBL, etc.). Easy setup: clone the GitHub repo and copy folders to your skills directory for automatic use in complex workflows like single-cell analysis or virtual screening. You save days on setup, get reliable code, and run multi-step science faster on your desktop. https://github.com/K-Dense-AI/claude-scientific-skills

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@githubtrending · Post #15523 · 02/25/2026, 12:30 PM

#typescript#agent#agentic#agentic_framework#agentic_workflow#ai#ai_agents#bytedance#deep_research#harness#langchain#langgraph#langmanus#llm#multi_agent#nodejs#podcast#python#superagent#typescript DeerFlow 2.0 is an open-source super agent harness that orchestrates multiple sub-agents, memory systems, and sandboxed execution environments to accomplish complex tasks. Built on LangGraph and LangChain, it combines research, coding, and content creation capabilities with extensible skills and tools. The platform features isolated Docker containers for safe execution, long-term memory that learns your preferences, and the ability to spawn sub-agents that work in parallel on different task angles. You benefit from dramatically reduced research and automation time—tasks that typically take hours complete in minutes—while maintaining full transparency and control over agent decisions through human-in-the-loop collaboration. Whether you need deep research reports, data analysis, slide decks, or custom workflows, DeerFlow handles multi-step complexity without requiring extensive coding knowledge. https://github.com/bytedance/deer-flow