Provides 134 ready-to-use Agent Skills that let AI agents execute multi-step scientific workflows (bioinformatics, cheminformatics, imaging, clinical research). Each skill includes curated docs and examples plus unified access to 100+ scientific databases and common Python packages — for agents that support the Agent Skills standard.
A library of ~140 ready-to-use Agent Skills that turn a coding agent (Claude Code, Cursor, Codex) into a science assistant across biology, chemistry, medicine, and drug discovery, with connectors to 100+ scientific databases and Python analysis tools.
Bundles 66 context-activated skills for Claude Code spanning backend, frontend, DevOps, security, and data/ML, loading only the relevant reference per request. A 'Common Ground' step surfaces hidden project assumptions before coding starts.
Desktop app that runs many CLI coding agents — Claude Code, Codex, Cursor, Gemini — in parallel, each in its own git worktree and branch. A built-in diff viewer, terminal, and PR tracking let you dispatch and review 10+ agents at once.
Lets AI agents (Claude Code, Codex, Cursor, and others) register through a guide and join a shared paper-trading arena, where they publish signals, debate ideas, and copy-trade each other across stocks, crypto, and Polymarket markets.
A stateful agent harness, shipped as a CLI, whose agents keep memory, skills, and prompts across sessions instead of resetting each run. Context is git-versioned via MemFS, and agents can rewrite their own prompts and skills over time.
Detects motion from Wi‑Fi channel state information (CSI) on cheap ESP32 boards and integrates natively with Home Assistant; offers an optional on‑device ML detector that requires no calibration.
Installs Claude Code-format skills into agents without native support — Cursor, Windsurf, Aider, Codex — by writing the same <available_skills> XML into their AGENTS.md. Skills stay plain files, so no MCP server is needed.
Automates multi-step web tasks by perceiving webpages as pixels and issuing low-level mouse, keyboard and scroll actions. A 7B-parameter multimodal agent trained on 145K synthetic trajectories (FaraGen), designed for on-device deployment and efficient task completion (~16 steps/task).
Agent memory that learns over time instead of just recalling past chats: retain/recall/reflect primitives turn interactions into facts, experiences, and mental models. Reports top LongMemEval scores; self-hostable with Python and Node SDKs.
Drives an LLM-powered agent to autonomously research, write, and ship ML code by accessing Hugging Face docs, datasets, repos, and cloud compute. Provides interactive CLI and headless modes, approval gates, tool routing, and integrations for HF, GitHub, and Anthropic models.
Packages reusable GitHub Copilot building blocks — agents, prompts, instructions, and skills — to make AI-assisted coding repeatable and standards-aligned for a team. Built around an RPI (Research, Plan, Implement) workflow in VS Code.