scientific-workflow

A blueprint-driven AutoResearch runtime for orchestrating AI research workflows from idea generation and experiments to paper writing and peer review.

Open-source, local-first desktop AI research workbench for scientific computing with Python/R, MCP bioinformatics tools, SSH/WSL/GPU runtimes, and OpenAI/Anthropic models.

An AI workflow skill pack for research, competitions, and innovation projects.

Paired-scientist AI assistant for reproducible research — lightweight Go runtime, lifecycle hooks, manuscript integration, 12 baseline scientific skills. PicoClaw-compatible.

HyperFlow: a scientific workflow engine

Open-source infrastructure for AI-native scientific labs

Turn Claude Code into an interactive scientific workspace: workflow, file system, and knowledge graph.

AITP Research Charter and Protocol: a charter-first protocol, contract, and adapter surface for AI-assisted theoretical physics research.

A collection of tools for the analysis of nanocrystals.

This repository contains exemplary workflow using programming tools and libraries for data rescue of historical biodiversity data. These tools combine OCR and text mining approaches to retrieve valuable information like species, functional traits and environments.

Local-first agentic research platform that turns project bundles into evidence-bound scientific papers with deterministic gates, scientist-panel review, fault localization, and rollback.

R-LAM is a reproducibility-constrained execution framework for Large Action Models in scientific workflow automation. It enables adaptive, agent-driven workflow execution while enforcing strict guarantees on auditability, determinism, and replayability.

Human-led, evidence-gated full-cycle research execution for Codex and Claude Code · 人类主导的科研全流程辅助工具

AI-assisted 13-stage academic research workbench for Codex: 40 bundled skills, research ledger, novelty audit, three-round review, de-AI polish, publication-grade figures.

AI research assistant for astronomy with reproducible data workflows, literature reasoning, evidence graphs, and provenance.

Evidence-first, experiment-traceable, reproducible-by-default research collaboration OS for Codex.

Local-first provenance manifests and optional AI audits for research evidence bundles

🌱 This repository is devoted to the emergence of Generative Simulation—a concept that hybridizes large language models (LLMs) and simulation. Simulation engines are developed or wrapped in such a way that they can be directly manipulated by generative AI, without requiring intensive retraining or fine-tuning.

A Git-backed protocol for traceable LLM-assisted research, separating experiment plans, raw results, analysis, and critique.

Run multi-agent AI research, manage experiments, and execute GPU workflows in one open source lab.