4 مستودعات
Downloading and managing software versions directly from GitHub repositories into local directories.
Distinct from GitHub Tools: Shortlist candidates focus on cluster bootstrapping or general GitHub API clients, not general package management from GitHub.
Explore 4 awesome GitHub repositories matching development tools & productivity · GitHub-Based Package Management. Refine with filters or upvote what's useful.
This project is a package manager for Home Assistant that enables the discovery and installation of community-made scripts, integrations, and frontend assets. It functions as a custom component manager and a GitHub-based package manager, providing a centralized community store to extend smart home functionality via remote repositories. The system distinguishes itself through a specialized catalog and indexing service that organizes third-party extensions by category and country. It includes a version-tracking update engine that monitors commit hashes, tags, and branches to manage stable and p
Functions as a package manager that discovers and downloads versions, tags, and branches directly from GitHub.
Universal Android Debloater Next Generation is a cross-platform desktop application for removing pre-installed system applications from Android devices. It communicates with devices through the Android Debug Bridge (ADB) protocol, supporting both wired USB connections and wireless network pairing for debloating without a physical cable. The tool manages multiple connected Android devices simultaneously and provides package state backup and restore capabilities, allowing users to save and reapply the enabled or disabled state of system packages after factory resets or OS upgrades. The applicat
Downloads and caches up-to-date debloating recommendations from a remote GitHub repository.
Packages custom agent code, dependencies, and infrastructure into reusable Git repositories.
DeepDanbooru is a deep learning tool for tagging anime-style images with Danbooru-style tags. It uses a pre-trained convolutional neural network to analyze images and predict tags identifying characters, attributes, and artwork details. The project provides a complete pipeline for training custom tag recognition models. Users can prepare datasets by downloading tag definitions from a remote Danbooru server using authenticated API requests, then store image-tag pairs in a structured SQLite database. The training workflow supports filtering datasets by rating or score criteria, configuring hype
Downloads Danbooru tag definitions via authenticated API for training and estimation tasks.