Ui.Vision is a browser extension for Robotic Process Automation (RPA) that provides web and desktop automation capabilities. It features a React/Redux UI with Ant Design, supporting both table-based macros (Selenium IDE compatible) and JavaScript script macros. The extension includes computer vision for image-based automation, OCR functionality, AI-assisted macro creation, and desktop automation through native XModules. It implements a storage abstraction layer supporting browser and file system modes, backup/restore functionality, and an MCP bridge for external AI agent control. The architecture follows web extension patterns with background scripts, content scripts, and a rich UI layer, communicating via IPC mechanisms.
OysterWorkflow is an open-core desktop application (Electron + React) that records a user's computer activity via Screenpipe (screen, audio, UI events), then uses multi-stage LLM calls to extract reusable, structured 'skills' (workflow graphs) that AI agents (Codex, Claude, Hermes) can execute. The system provides a full lifecycle: capture → ingest → normalize → segment → LLM-based workflow discovery → skill extraction → workflow graph generation → quality evaluation → installation into agent skill directories → execution via MCP or Hermes CLI. It also includes a cloud sync layer (Supabase), a marketplace (ClawHub), and a local runtime HTTP server for the UI. Architecturally, it is a hybrid monolith with clear module boundaries: a desktop shell (Electron main process), a local runtime server (Express), a CLI (commander), a React UI (Vite + React Flow), and a product state layer (SQLite-backed with cloud sync). The codebase is TypeScript-first, ESM-only, and heavily tested (200+ test files). It demonstrates strong engineering practices: atomic file I/O, bounded child process management, immutable revision control for workflow graphs, and a sophisticated prompt registry with versioned inheritance. While primarily an application, it contains a significant data pipeline component (ingest → normalize → segment → LLM extraction) that could be considered a research-grade feature engineering system. The repo is actively maintained with frequent commits, CI, and issue triage. Notable risks include heavy sidecar bundling (Screenpipe, Hermes, ffmpeg), rapid prompt set version churn, and a mixed licensing model (Apache 2.0 open core + PolyForm Noncommercial for product code).
AutoPy is a cross-platform GUI automation library implemented in Rust and exposed to Python through PyO3. It provides programmatic control of the mouse and keyboard, screen capture, bitmap pixel inspection, and color detection. The library abstracts over Windows, macOS, and X11 backends, so the same Python API works across desktop platforms. It is distributed as a Python extension module built with maturin/setuptools-rust and is also usable as a Rust crate.
This repository provides an AutoHotkey v2 script that automates the export of WeChat contacts to a CSV file by simulating keyboard and mouse interactions with the WeChat desktop client. The script is designed for WeChat 3.x versions and may not work with newer 4.x versions due to UI changes. The architecture is a single monolithic script with functions for window management, user input, contact detail extraction, and CSV file generation. The main workflow is triggered by the Win+C hotkey, which prompts the user for the number of contacts and the delay between each contact extraction, then iterates through the contact list to copy and save each contact's details. While the script is tightly coupled to the WeChat UI, it contains several reusable utility functions: GetContactCount and GetContactDuration for validating numeric user input, and FormatCsvItem for sanitizing CSV output to prevent formula injection. These functions are isolated and can be extracted for use in other AutoHotkey projects. However, the core automation logic is specific to WeChat and requires the legacy 3.x client to function correctly.