claude-scholar

Claude Scholar is a personal configuration system for Claude Code CLI, designed for academic research and software development. It covers the full research lifecycle from ideation to publication, offering rich skills, commands, agents, and hooks optimized for various tasks. The tool supports features like research ideation, ML project development, experiment analysis, paper writing, self-review, submission and rebuttal, post-acceptance processing, and more. It also includes supporting workflows for automated enforcement, knowledge extraction, skill evolution, and offers a structured file structure for easy navigation and usage.

HIA (Health Insights Agent) is an AI agent designed to analyze blood reports and provide personalized health insights. It features an intelligent agent-based architecture with multi-model cascade system, in-context learning, PDF upload and text extraction, secure user authentication, session history tracking, and a modern UI. The tech stack includes Streamlit for frontend, Groq for AI integration, Supabase for database, PDFPlumber for PDF processing, and Supabase Auth for authentication. The project structure includes components for authentication, UI, configuration, services, agents, and utilities. Contributions are welcome, and the project is licensed under MIT.

The Claude Code Ultimate Guide is an exhaustive documentation resource that takes users from beginner to power user in using Claude Code. It includes production-ready templates, workflow guides, a quiz, and a cheatsheet for daily use. The guide covers educational depth, methodologies, and practical examples to help users understand concepts and workflows. It also provides interactive onboarding, a repository structure overview, and learning paths for different user levels. The guide is regularly updated and offers a unique 257-question quiz for comprehensive assessment. Users can also find information on agent teams coverage, methodologies, annotated templates, resource evaluations, and learning paths for different roles like junior developer, senior developer, power user, and product manager/devops/designer.

Claude Code Mastery is a comprehensive tool for maximizing Claude Code, offering a production-ready project template with 16 slash commands, deterministic hook enforcement, MongoDB wrapper, live AI monitoring, and three-layer security. It provides a security gatekeeper, project scaffolding blueprint, MCP server integration, workflow automation through custom commands, and emphasizes the importance of single-purpose chats to avoid performance degradation.

LLM4S provides a simple, robust, and scalable framework for building Large Language Models (LLM) applications in Scala. It aims to leverage Scala's type safety, functional programming, JVM ecosystem, concurrency, and performance advantages to create reliable and maintainable AI-powered applications. The framework supports multi-provider integration, execution environments, error handling, Model Context Protocol (MCP) support, agent frameworks, multimodal generation, and Retrieval-Augmented Generation (RAG) workflows. It also offers observability features like detailed trace logging, monitoring, and analytics for debugging and performance insights.

Agent Skills is a secure, validated skill registry for professional AI coding agents. It provides a library of verified, tested, and safe capabilities to extend various AI agents with confidence. The tool addresses security concerns in marketplace skills by offering 100% open-source code, static analysis for credential theft prevention, immutable integrity to prevent supply chain attacks, and human curation to ensure safety boundaries. Users can install skills through an interactive wizard, choose from a variety of supported AI coding agents, and benefit from a growing catalog of featured skills for development, cloud, automation, design, and security tasks.

LLM-TradeBot is an Intelligent Multi-Agent Quantitative Trading Bot based on the Adversarial Decision Framework (ADF). It achieves high win rates and low drawdown in automated futures trading through market regime detection, price position awareness, dynamic score calibration, and multi-layer physical auditing. The bot prioritizes judging 'IF we should trade' before deciding 'HOW to trade' and offers features like multi-agent collaboration, agent configuration, agent chatroom, AUTO1 symbol selection, multi-LLM support, multi-account trading, async concurrency, CLI headless mode, test/live mode toggle, safety mechanisms, and full-link auditing. The system architecture includes a multi-agent architecture with various agents responsible for different tasks, a four-layer strategy filter, and detailed data flow diagrams. The bot also supports backtesting, full-link data auditing, and safety warnings for users.

SpecWeave is a spec-driven Skill Fabric for AI coding agents that allows programming AI in English. It provides first-class support for Claude Code and offers reusable logic for controlling AI behavior. With over 100 skills out of the box, SpecWeave eliminates the need to learn Claude Code docs and handles various aspects of feature development. The tool enables users to describe what they want, and SpecWeave autonomously executes tasks, including writing code, running tests, and syncing to GitHub/JIRA. It supports solo developers, agent teams working in parallel, and brownfield projects, offering file-based coordination, autonomous teams, and enterprise-ready features. SpecWeave also integrates LSP Code Intelligence for semantic understanding of codebases and allows for extensible skills without forking.

Automodel is a Python library for automating the process of building and evaluating machine learning models. It provides a set of tools and utilities to streamline the model development workflow, from data preprocessing to model selection and evaluation. With Automodel, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to find the best model for their dataset. The library is designed to be user-friendly and customizable, allowing users to define their own pipelines and workflows. Automodel is suitable for data scientists, machine learning engineers, and anyone looking to quickly build and test machine learning models without the need for manual intervention.

Smart Ralph is a Claude Code plugin designed for spec-driven development. It helps users turn vague feature ideas into structured specs and executes them task-by-task. The tool operates within a self-contained execution loop without external dependencies, providing a seamless workflow for feature development. Named after the Ralph agentic loop pattern, Smart Ralph simplifies the development process by focusing on the next task at hand, akin to the simplicity of the Springfield student, Ralph.

General Bots is a self-hosted AI automation platform and LLM conversational platform focused on convention over configuration and code-less approaches. It serves as the core API server handling LLM orchestration, business logic, database operations, and multi-channel communication. The platform offers features like multi-vendor LLM API, MCP + LLM Tools Generation, Semantic Caching, Web Automation Engine, Enterprise Data Connectors, and Git-like Version Control. It enforces a ZERO TOLERANCE POLICY for code quality and security, with strict guidelines for error handling, performance optimization, and code patterns. The project structure includes modules for core functionalities like Rhai BASIC interpreter, security, shared types, tasks, auto task system, file operations, learning system, and LLM assistance.

StyleKit is a comprehensive design system toolkit that helps both humans and AI generate consistent, high-quality UI code. It provides structured style specifications, design tokens, component recipes, prompt templates, and export tools — everything needed to go from 'I want a glassmorphism SaaS dashboard' to production-ready frontend code. With 90+ visual styles, 20+ page templates, 25+ UI components, and AI-powered tools like Prompt Builder, Smart Recommender, Style Linter, Style Analyzer, and Style Blender, StyleKit offers a platform with GitHub OAuth, ratings & comments, style submissions, instant community availability, favorites, bilingual support, PWA, and dark/light mode themes.

PAI is an open-source personal AI infrastructure designed to orchestrate personal and professional lives. It provides a scaffolding framework with real-world examples for life management, professional tasks, and personal goals. The core mission is to augment humans with AI capabilities to thrive in a world full of AI. PAI features UFC Context Architecture for persistent memory, specialized digital assistants for various tasks, an integrated tool ecosystem with MCP Servers, voice system, browser automation, and API integrations. The philosophy of PAI focuses on augmenting human capability rather than replacing it. The tool is MIT licensed and encourages contributions from the open-source community.

A comprehensive, modular, containerized Governance, Risk, and Compliance (GRC) platform built with modern technologies. Manage your entire security program from compliance tracking to risk management, third-party assessments, and external audits. The platform includes specialized modules for Compliance, Data Management, Risk Management, Third-Party Risk Management, Trust, Audit, Tools, AI & Automation, and Administration. It offers features like controls management, frameworks assessment, policies lifecycle management, vendor risk management, security questionnaires, knowledge base, audit management, awareness training, phishing simulations, AI-powered risk scoring, and MCP server integration. The tech stack includes Node.js, TypeScript, React, PostgreSQL, Keycloak, Traefik, Redis, and RustFS for storage.

Multi-agent RALPH (Reinforcement Learning with Probabilistic Hierarchies) Loop is a framework for multi-agent reinforcement learning research. It provides a flexible and extensible platform for developing and testing multi-agent reinforcement learning algorithms. The framework supports various environments, including grid-world environments, and allows users to easily define custom environments. Multi-agent RALPH Loop is designed to facilitate research in the field of multi-agent reinforcement learning by providing a set of tools and utilities for experimenting with different algorithms and scenarios.

The cc-sdd repository provides a tool for AI-Driven Development Life Cycle with Spec-Driven Development workflows for Claude Code and Gemini CLI. It includes powerful slash commands, Project Memory for AI learning, structured AI-DLC workflow, Spec-Driven Development methodology, and Kiro IDE compatibility. Ideal for feature development, code reviews, technical planning, and maintaining development standards. The tool supports multiple coding agents, offers an AI-DLC workflow with quality gates, and allows for advanced options like language and OS selection, preview changes, safe updates, and custom specs directory. It integrates AI-Driven Development Life Cycle, Project Memory, Spec-Driven Development, supports cross-platform usage, multi-language support, and safe updates with backup options.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

The Expected Parrot Domain-Specific Language (EDSL) package enables users to conduct computational social science and market research with AI. It facilitates designing surveys and experiments, simulating responses using large language models, and performing data labeling and other research tasks. EDSL includes built-in methods for analyzing, visualizing, and sharing research results. It is compatible with Python 3.9 - 3.11 and requires API keys for LLMs stored in a `.env` file.

Fast-stable-diffusion is a project that offers notebooks for RunPod, Paperspace, and Colab Pro adaptations with AUTOMATIC1111 Webui and Dreambooth. It provides tools for running and implementing Dreambooth, a stable diffusion project. The project includes implementations by XavierXiao and is sponsored by Runpod, Paperspace, and Colab Pro.

This repository contains code for the paper 'Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models'. It focuses on fine-tuning CLIP in an unsupervised manner to enhance its robustness against visual adversarial attacks. By replacing the vision encoder of large vision-language models with the fine-tuned CLIP models, it achieves state-of-the-art adversarial robustness on various vision-language tasks. The repository provides adversarially fine-tuned ViT-L/14 CLIP models and offers insights into zero-shot classification settings and clean accuracy improvements.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

This repository contains code for reproducing experiments on lie detection in black-box LLMs by asking unrelated questions. It includes Q/A datasets, prompts, and fine-tuning datasets for generating lies with language models. The lie detectors rely on asking binary 'elicitation questions' to diagnose whether the model has lied. The code covers generating lies from language models, training and testing lie detectors, and generalization experiments. It requires access to GPUs and OpenAI API calls for running experiments with open-source models. Results are stored in the repository for reproducibility.

BigCodeBench is an easy-to-use benchmark for code generation with practical and challenging programming tasks. It aims to evaluate the true programming capabilities of large language models (LLMs) in a more realistic setting. The benchmark is designed for HumanEval-like function-level code generation tasks, but with much more complex instructions and diverse function calls. BigCodeBench focuses on the evaluation of LLM4Code with diverse function calls and complex instructions, providing precise evaluation & ranking and pre-generated samples to accelerate code intelligence research. It inherits the design of the EvalPlus framework but differs in terms of execution environment and test evaluation.

RAG with txtai is a Retrieval Augmented Generation (RAG) Streamlit application that helps generate factually correct content by limiting the context in which a Large Language Model (LLM) can generate answers. It supports two categories of RAG: Vector RAG, where context is supplied via a vector search query, and Graph RAG, where context is supplied via a graph path traversal query. The application allows users to run queries, add data to the index, and configure various parameters to control its behavior.

Perplexica is an open-source AI-powered search engine that utilizes advanced machine learning algorithms to provide clear answers with sources cited. It offers various modes like Copilot Mode, Normal Mode, and Focus Modes for specific types of questions. Perplexica ensures up-to-date information by using SearxNG metasearch engine. It also features image and video search capabilities and upcoming features include finalizing Copilot Mode and adding Discover and History Saving features.

KULLM (구름) is a Korean Large Language Model developed by Korea University NLP & AI Lab and HIAI Research Institute. It is based on the upstage/SOLAR-10.7B-v1.0 model and has been fine-tuned for instruction. The model has been trained on 8×A100 GPUs and is capable of generating responses in Korean language. KULLM exhibits hallucination and repetition phenomena due to its decoding strategy. Users should be cautious as the model may produce inaccurate or harmful results. Performance may vary in benchmarks without a fixed system prompt.

MMMU is a benchmark designed to evaluate multimodal models on college-level subject knowledge tasks, covering 30 subjects and 183 subfields with 11.5K questions. It focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of various models highlights substantial challenges, with room for improvement to stimulate the community towards expert artificial general intelligence (AGI).

1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks. It can produce detailed, factual, and unbiased research reports with customization options. The tool addresses issues of speed, determinism, and reliability by leveraging parallelized agent work. The main idea involves running 'planner' and 'execution' agents to generate research questions, seek related information, and create research reports. GPT Researcher optimizes costs and completes tasks in around 3 minutes. Features include generating long research reports, aggregating web sources, an easy-to-use web interface, scraping web sources, and exporting reports to various formats.

ChatTTS is a generative speech model optimized for dialogue scenarios, providing natural and expressive speech synthesis with fine-grained control over prosodic features. It supports multiple speakers and surpasses most open-source TTS models in terms of prosody. The model is trained with 100,000+ hours of Chinese and English audio data, and the open-source version on HuggingFace is a 40,000-hour pre-trained model without SFT. The roadmap includes open-sourcing additional features like VQ encoder, multi-emotion control, and streaming audio generation. The tool is intended for academic and research use only, with precautions taken to limit potential misuse.

HebTTS is a language modeling approach to diacritic-free Hebrew text-to-speech (TTS) system. It addresses the challenge of accurately mapping text to speech in Hebrew by proposing a language model that operates on discrete speech representations and is conditioned on a word-piece tokenizer. The system is optimized using weakly supervised recordings and outperforms diacritic-based Hebrew TTS systems in terms of content preservation and naturalness of generated speech.

This repository is a collection of research lectures and experience sharing posts from frontline researchers in the field of AI. It aims to help individuals upgrade their research skills and knowledge through insightful talks and experiences shared by experts. The content covers various topics such as evaluating research papers, choosing research directions, research methodologies, and tips for writing high-quality scientific papers. The repository also includes discussions on academic career paths, research ethics, and the emotional aspects of research work. Overall, it serves as a valuable resource for individuals interested in advancing their research capabilities in the field of AI.