Mindolph is a desktop knowledge management application built with JavaFX. It provides integrated support for mind maps, markdown, PlantUML diagrams, and CSV files, along with AI-powered features such as text reframing, summarization, and chat via various LLM providers. The application features a modular architecture with core, base, FX, and domain-specific modules, an event-driven communication system, a plugin/extension system for mind map operations, and a preference management system. Users can manage workspaces, search within files, and use reusable snippets.
This repository is a comprehensive plugin and skill set for Claude Code (and compatible CLI assistants like Codex CLI and OpenCode), designed to transform the AI into a semi-automated research assistant for academic and software development workflows. It is not a standalone application but a configuration package that installs skills, commands, agents, hooks, and rules into the user's Claude environment. The architecture follows a skill-based pattern: each skill is a directory with a SKILL.md entry point, references, examples, and optional scripts, all consumed by the AI at runtime. The repository covers the full research lifecycle—ideation, literature review (via Zotero MCP and arXiv APIs), experiment analysis, ML paper writing with conference-specific LaTeX templates (NeurIPS, ICLR, ICML, ACL, AAAI), reviewer response drafting, publication chart creation, and post-acceptance promotion. It also integrates deeply with Obsidian for project knowledge management and includes a meta-toolchain for developing and QA-ing skills themselves. While primarily an application configuration, it contains substantial research-oriented content (Kaggle competition analyses, citation verification workflows, writing pattern references), making it a hybrid tool that bridges software engineering and data science research. The codebase is actively maintained, well-documented, and demonstrates strong craftsmanship in its script utilities and hook infrastructure.
This repository implements a personal knowledge management system centered around LLM-powered content processing. It follows a four-layer architecture: raw/ for immutable source materials, wiki/ for LLM-generated structured knowledge (summaries, concepts, indexes), brainstorming/ for intermediate outputs like chat logs and health reports, and artifacts/ for user-created work. The system processes various input formats (articles, books, PDFs, Facebook exports) through user-triggered commands like /compile and /convert-to-md, which leverage external tools (pandoc, pymupdf) and LLMs to extract insights and build interconnected knowledge networks. The core innovation lies in separating raw consumption from synthesized understanding, allowing users to accumulate source material without immediate processing, then periodically run LLM-driven synthesis to surface patterns across documents. Key workflows include extracting concepts that appear in multiple sources, maintaining bidirectional links between related ideas, and preserving tension between personal experience and external research in concept notes. The system emphasizes provenance tracking, with every synthesized piece linking back to its source via wikilinks. While the system relies on external LLMs and tools for its core functionality, it provides well-defined, reusable components for common data preparation tasks. The Facebook JSON-to-Markdown converter stands out as a robust, standalone script handling encoding normalization, media extraction, and frontmatter generation. The overall design demonstrates thoughtful consideration of knowledge work friction points, though reproducibility is hampered by undocumented environment dependencies and lack of sample data.
TiddlyWiki5 is a mature, extensible personal wiki application that runs entirely as a single HTML file in the browser or as a Node.js server. It manages content as individual 'tiddlers' — small, addressable pieces of text with metadata — and provides a powerful custom filter language for querying, transforming, and aggregating them. The application features a widget-based UI rendering engine, a pluggable wiki-text parser with dozens of rules, a built-in HTTP server with REST-like endpoints, encryption, and extensive integration plugins (geospatial maps via Leaflet/Turf, math typesetting via KaTeX, Markdown parsing, CodeMirror editing, QR codes, etc.). The architecture is highly modular: core functionality is implemented via module types (widgets, filter operators, parser rules, startup hooks, saver backends) that can be extended or overridden by plugins. The codebase has been continuously developed since 2011 and supports both client-side single-page application mode and server-side wiki mode. Despite its impressive extensibility and long history, the codebase shows signs of technical drift. The boot kernel (boot/boot.js) is a large monolithic file that mixes utility functions, the wiki store, module loading, and platform-specific startup logic. Many third-party libraries (e.g., JSZip v2.6.1, Leaflet.markercluster 1.5.3, minified CodeMirror modes) are vendored and outdated, requiring manual patching. The code style is inconsistent, with some modules using modern ES6+ features while others remain in ES5. The filter language and widget infrastructure are well-architected and reusable, but the tight coupling between core modules makes extraction for external use non-trivial. The project remains actively maintained with frequent commits, a large community, and an extensive test suite, making it a solid foundation for knowledge management applications, though significant refactoring would be needed to bring it to contemporary best practices.
This repository is a content-based Claude Code skill package rather than a conventional software service. It contains two agent skills — /tutor-setup and /tutor — that an LLM coding assistant executes using filesystem and WebFetch tools. tutor-setup scans a working directory, auto-detects whether it contains source code or documents, and generates an Obsidian StudyVault: concept notes, dashboards, exam-traps, and practice questions, with strict rules for source traceability, tag registries, wiki-linking, and folder conventions. tutor-setup operates through detailed phase models (D1-D9 for documents, C1-C9 for codebases), and codebase mode produces architecture maps, module notes, API surfaces, and onboarding exercises. tutor then reads that vault and runs interactive 4-question quiz rounds, updating concept-level progress files and a compact dashboard; it supports diagnostic, weak-area drill, section review, and hard-mode sessions, all governed by a zero-hint quiz design rule in references/quiz-rules.md. The implementation is almost entirely Markdown instructions plus two small Bash install/uninstall scripts; there is no compiled code, test suite, CI pipeline, or dependency manifest. The operational logic is prompt/instruction engineering that Claude Code interprets. External dependencies are Claude Code (or a compatible npx-skills-aware assistant), Obsidian for reviewing generated vaults, pdftotext/poppler for PDF extraction, and the assistant's built-in Read, Write, Edit, Glob, Grep, Bash, WebFetch, and AskUserQuestion tools. The data flow is unidirectional: source files in the CWD are scanned and extracted, transformed into StudyVault markdown, then quiz answers update concept files and dashboard aggregate stats. The skill enforces a CWD boundary to avoid accessing files outside the working directory. The strong point is unusually detailed and internally consistent procedural specification: mandatory source-content verification, per-topic coverage rules, tag registries, active-recall fold callouts, interlinking requirements, and self-review quality checklists. Weaknesses are the absence of automated tests/CI and versioned dependency pins, and behavior ultimately depends on the host LLM following prompt rules correctly. The README also references a different GitHub owner (RoundTable02) than the repository path (bevibing/tutor-skills), indicating a fork or repackage whose provenance should be verified before use. Overall, it fulfills its stated purpose well, and several Markdown modules are genuinely reusable as templates for other agent skills, study-vault generators, or adaptive quiz systems.
EvoNexus is an AI-powered business operating system with a dashboard for managing various applications and services. It has features for managing systems, knowledge base, and user authentication, and uses Docker for containerization.
OpenCrew is a multi-agent operating system designed to extend OpenClaw into a manageable AI team. It enables users to create specialized agents (Chief of Staff, CTO, Builder, CIO, Knowledge Officer, Operations, Research) that collaborate through messaging platforms like Slack, Discord, and Feishu. The system implements structured collaboration through role-based channels, thread-based task isolation, and agent-to-agent communication protocols. Key features include autonomy levels (L0-L3), task classification (QAPS), two-mode agent collaboration (delegation and discussion), and three-layer knowledge distillation. The application is actively maintained with recent updates focusing on cross-agent collaboration capabilities.
Make.md is an Obsidian plugin (manifest id `make-md`) that turns a plain vault into a structured knowledge workspace. In production it registers custom leaf views (Spaces, Navigator, Blink), patches Obsidian's workspace and CodeMirror editor, and maintains a metadata layer called `Superstate` indexed by web workers. It ships a React-based frame layout engine where notes/spaces are rendered as configurable frame trees with style/action expressions evaluated at runtime, an MDB layer that reads and writes SQLite `.mdb` files via sql.js, and a predicate/filter/aggregate system powering table, calendar, list, and chart views. The plugin also includes a custom mathjs-based formula language, an HTML export pipeline, and a D3-driven visualization pipeline.
This repository is a personal knowledge base and digital garden built with MkDocs, a static site generator. It contains over 1300 markdown files covering a vast range of topics including software development, DevOps, Python, Vim/Neovim, Linux, self-hosting, life management, activism, and arts. The content is organized into topical pages and dated newsletters that serve as a changelog of the author's learning. The repository includes MkDocs configuration, a custom theme, CI/CD via GitHub Actions, and dependency management with PDM. It is not a software application or data pipeline; it is a documentation site that the author maintains as a public second brain. The codebase is minimal and standard for a MkDocs project, with no reusable software components beyond the content itself.
This repository is a curated reference directory (commonly known as an 'awesome list') for the Readwise and Readwise Reader ecosystem. It serves as a discovery tool for users seeking to enhance their Readwise experience through community-built integrations, client libraries, and specialized tools. The resource is organized into thematic sections covering official applications, client libraries across multiple programming languages (Python, Go, Ruby, Node.js, Rust), AI-powered tools, platform-specific integrations (including Obsidian, Notion, Raycast, Arc browser, and mobile platforms), and various other utilities that connect Readwise with services like Anki, Kindle, Twitter, and Zotero. As a static documentation resource, the repository provides value through comprehensive curation and organization rather than technical innovation or reusable code components. It represents a practical application of community knowledge sharing within the Readwise ecosystem, helping users locate relevant tools without extensive manual search. Maintenance indicators suggest the resource may not be consistently updated, with the last referenced content updates dating to September 2023, which could limit its usefulness for discovering the most recent tools in a rapidly evolving ecosystem.
ZhihuHelper is a desktop application built with Electron, React, and Node.js designed to scrape content from Zhihu (a Chinese Q&A platform) and export it to EPUB/HTML formats for offline reading. The application features a GUI for task configuration and logging, alongside a CLI interface powered by AdonisJS Ace. Core functionality includes fetching various Zhihu content types (answers, articles, pins, collections, etc.) via the platform's API, storing data in a local SQLite database, and generating formatted e-books with options for sorting, image quality selection, and automatic volume splitting. The architecture separates concerns into distinct layers: Electron main process for window management and IPC, React renderer for the UI, API wrappers for Zhihu endpoints, Knex-based ORM models for data persistence, and a custom EPUB generation library. While the tool effectively accomplishes its stated purpose, it exhibits signs of technical drift due to outdated dependencies and reliance on Zhihu's undocumented API, which necessitates frequent maintenance to remain functional.