This repository is a Claude Code/Pi skills framework for academic research and writing. It packages markdown skills, agent definitions, and Python tooling that together support the full research pipeline: topic discovery, deep research, paper writing, review, citation verification, and meta-analysis. The dominant character is application/tooling: it has CLI entry points, a Pi wrapper, dozens of lint/check scripts, and a large test suite. It is not a data pipeline, though it includes network-backed citation lookup scripts and research-oriented data-integrity checks.
This repository implements a multi-agent AI system for generating comprehensive company research reports. The backend is a FastAPI application that orchestrates a LangGraph-based pipeline of specialized research agents: a grounding node that optionally crawls the company's website, parallel research nodes (company, industry, financial, news) that use Tavily search and LLM-generated queries to gather data, a curator that filters documents by relevance score, an enricher that fetches raw content, a briefing node that generates category-specific summaries using Gemini, and an editor that compiles the final report using GPT-4o. The frontend is a React application with TypeScript and Tailwind CSS that communicates via SSE streaming. The system is primarily a web application with a modular agent pipeline, not a data analysis project.
This repository is a curated list (awesome list) of AutoResearch use cases, implementations, benchmarks, and forks. It does not contain any source code, data, or executable components. The sole content is a README.md file that organizes links to external projects, each with descriptions and links to optimization traces. The repository serves as a directory for the AutoResearch community, not as a software application or data research pipeline. It is a static documentation resource with no runtime behavior, architecture, or data flows.
This repository is a comprehensive knowledge base and research platform for quantitative finance, primarily focused on Chinese A-share markets. It contains hundreds of blog posts, course materials, and technical articles covering factor investing, machine learning strategies, backtesting frameworks, and data engineering. The codebase includes reusable data pipelines (ParquetUnifiedStorage, CalendarModel), a custom backtesting framework (Moonshot), and numerous factor analysis scripts. It also hosts a sophisticated set of AI-assisted academic research skills (paper writing, review, deep research) defined as Claude skill files. The repository serves as both an educational resource and a personal research lab, with content spanning from beginner Python tutorials to advanced factor construction and model training. While the code is functional and demonstrates solid data engineering patterns, it is tightly coupled to Chinese financial data APIs (Tushare, akshare) and contains hardcoded paths, limiting portability. The repository is actively maintained but shows signs of technical drift in some dependencies and content duplication.
This is the research archive for the Hyperspace P2P network, a distributed AGI system. The repository primarily contains documentation, leaderboards, trained model weights, and configuration files for autonomous research agents. It serves as a durable record of experiments run by agents across 5 research domains (machine learning, search, finance, skills, causes). The main outputs include a collaboratively trained language model (qwen2.5-0.5b-hyperspace-v1) trained by 32 anonymous nodes using DiLoCo + SVD compression, and live leaderboards updated via GitHub Actions. While the repo itself is mostly static artifacts, it references a larger codebase (the Hyperspace CLI and P2P stack) that is not included. The repository is hybrid: primarily a research data archive, but also includes a standalone CLI tool (agentboard) for a blockchain-based message board.