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AI agent infrastructure and developer productivity tools dominated GitHub’s weekly trending rankings for Nov. 3-9, 2026, with NVIDIA’s Auto Ontology taking the No. 1 position and a mobile-friendly AI coding control platform recording the largest weekly increase in stars.
Among the 10 leading repositories, pingdotgg/t3code ranked seventh but gained 15,379 stars during the week, the largest increase in the group. NVIDIA/auto-ontology added 9,327 stars, while D4Vinci/Scrapling gained 8,342. TypeScript was the most common primary programming language, appearing in four repositories, followed by Python in three. Rust, Shell and CSS each appeared once.
Five repositories explicitly referenced AI agents in their descriptions, reflecting growing developer interest in tools that help autonomous software systems retain context, coordinate tasks and operate across development environments. The broader ranking also included a traditional Chinese astrology chart-generation engine and a web-scraping framework.
NVIDIA’s Auto Ontology leads the rankings
NVIDIA/auto-ontology ranked first after gaining 9,327 stars, bringing its total to 10,403. The Python framework creates a semantic reasoning layer over enterprise data, aiming to help AI systems generate more reliable queries, retrieve relevant context and make their work easier to explain.
Its workflow covers discovering an organization's data structure, assigning meaning to metrics and entities, validating generated queries and exposing contextual information to AI agents. A learning stage incorporates verified corrections and usage records back into the ontology. The project supports connections through MCP, command-line tools and APIs. It was released under the Apache 2.0 license, although its README says external contributions are not currently being accepted.
Memory and parallel workflows attract developers
Thedotmack/claude-mem ranked second, gaining 6,443 stars to reach 114,906, the highest cumulative total among the 10 repositories. The TypeScript tool records an AI agent's work, compresses it into searchable memory and restores relevant context in later sessions. Its goal is to reduce repeated work and prevent agents from revisiting files or repeating mistakes already addressed in previous sessions.
The project supports Claude Code, Codex, Cursor and OpenCode, among other tools. Its documentation distinguishes between systems that can both record and retrieve session memories and several others, including Copilot CLI, Goose, Roo Code and Warp, which can retrieve memories but do not record sessions.
In third place, max-sixty/worktrunk gained 2,313 stars, reaching 12,123. Written in Rust, the command-line tool simplifies Git worktree management for developers running multiple AI agents simultaneously. Git worktrees allow separate working directories for different branches, but setting them up manually can involve repetitive commands. Worktrunk automates branch-based naming and directory placement, while offering hooks and copy-on-write build caching.
Anthropic’s skills library and AI game development
Anthropic's public anthropics/skills repository ranked fourth, adding 3,169 stars to reach 189,665, the largest overall total in the ranking. It contains reusable instructions, scripts and supporting resources that help Claude perform specialized tasks consistently.
The collection spans creative work such as art, music and design, as well as technical activities including web application testing and MCP server development. It also covers business functions such as internal communications and branding. Each skill has its own directory containing a SKILL.md file with instructions and metadata. Anthropic has also made document-generation and editing skills for DOCX, PDF, PPTX and XLSX files available under the Apache 2.0 license.
Donchitos/Claude-Code-Game-Studios placed fifth, gaining 5,235 stars for a cumulative 37,012. The Shell-based project organizes a Claude Code session like a game development studio, with 49 specialized agents assigned to director, department-lead and specialist roles. Each has defined responsibilities, reporting lines and quality checkpoints. The project says users retain final decision-making authority. Its documentation lists 72 workflow skills in the repository description and 74 in the README.
New interfaces for controlling AI agents
OpenMausBot, in sixth place, gained 7,621 stars to reach 18,271. The open-source project presents itself as an alternative to Grok Bot and provides a virtual machine environment for bots. Users can interact with multiple AI bots through a chat interface, with individual bots connected to locally running Claude or Codex instances.
Each bot can have its own personality, model, cloud computer and connected applications. The project explicitly says it has no affiliation with xAI, Meta, OpenAI or Manus. It also says it has not issued a token and is unrelated to a cryptocurrency using the same name. Available distributions include macOS, Windows x64 and Ubuntu 24.04 x64.
Seventh-ranked pingdotgg/t3code recorded the week's biggest gain, adding 15,379 stars to reach 52,514. T3 Code describes itself as an agent-harness control surface: a platform for controlling AI coding agents installed on a user's computer through mobile applications for iOS and Android, a web interface and an Electron-based desktop application.
Its documented integrations include Codex, Claude, Cursor, Grok Build, OpenCode, Antigravity and fx. Users must install and authenticate at least one supported agent before using the platform. The project is released under the MIT license, and its developers say they are not selling a product, emphasizing that users can fork the code and develop their own versions.
Design quality becomes another focus
Nutlope/hallmark ranked eighth. The design skill is intended for use with Claude Code, Codex, Cursor and OpenCode, with the goal of producing interfaces that do not have the recognizable appearance of generic AI-generated designs.
According to its README, Hallmark uses a selection of 50 themes and asks users to choose three to guide page creation. It then checks the resulting interface in a real browser for common design shortcomings, including repetitive decorative text above headings, arbitrary color changes to individual words and forms that display thank-you messages without actually submitting information.
The rankings illustrate the range of experimentation taking place around AI-assisted software development. Developers are building not only coding agents, but also the memory systems, workflow management tools, interfaces and quality checks needed to make those agents more useful in practical projects.
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