86 skills found · Page 1 of 3
max-sixty / WorktrunkWorktrunk is a CLI for Git worktree management, designed for parallel AI agent workflows
stravu / Crystal(Crystal is now Nimbalyst) Run multiple Codex and Claude Code AI sessions in parallel git worktrees. Test, compare approaches & manage AI-assisted development workflows in one desktop app.
d-kuro / Gwq🌳 Git worktree manager with fuzzy finder - Work on multiple branches simultaneously, perfect for parallel AI coding workflows 🍋
haoyu-haoyu / Multi AI WorkflowMulti-AI orchestration framework for Claude Code — coordinate Claude, Codex, and Gemini with 7 workflow modes, parallel execution, and session unification
joewinke / JatThe World's First Agentic IDE. Visual dashboard: live sessions, task management, code editor, terminal. Epic Swarm parallel workflows. Auto-proceed rules. Automation patterns. Beads + Agent Mail + 50 bash tools. Supervise 20+ agents from one UI.
imbue-ai / SculptorSculptor is a UI for running parallel coding agents in safe, isolated sandboxes, enabling powerful agent workflows.
nekocode / Agent WorktreeA Git worktree workflow tool for AI coding agents. Enables parallel development with isolated environments.
radiantone / EntangleA lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs.
teambrilliant / Claude Research Plan ImplementStructured AI development framework for Claude Code. Research → Plan → Implement workflow with parallel agents, persistent context, and session management.
sanusanth / Python Basic ProgramsWhat is Python? Executive Summary Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed. Often, programmers fall in love with Python because of the increased productivity it provides. Since there is no compilation step, the edit-test-debug cycle is incredibly fast. Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn't catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python's introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective. What is Python? Python is a popular programming language. It was created by Guido van Rossum, and released in 1991. It is used for: web development (server-side), software development, mathematics, system scripting. What can Python do? Python can be used on a server to create web applications. Python can be used alongside software to create workflows. Python can connect to database systems. It can also read and modify files. Python can be used to handle big data and perform complex mathematics. Python can be used for rapid prototyping, or for production-ready software development. Why Python? Python works on different platforms (Windows, Mac, Linux, Raspberry Pi, etc). Python has a simple syntax similar to the English language. Python has syntax that allows developers to write programs with fewer lines than some other programming languages. Python runs on an interpreter system, meaning that code can be executed as soon as it is written. This means that prototyping can be very quick. Python can be treated in a procedural way, an object-oriented way or a functional way. Good to know The most recent major version of Python is Python 3, which we shall be using in this tutorial. However, Python 2, although not being updated with anything other than security updates, is still quite popular. In this tutorial Python will be written in a text editor. It is possible to write Python in an Integrated Development Environment, such as Thonny, Pycharm, Netbeans or Eclipse which are particularly useful when managing larger collections of Python files. Python Syntax compared to other programming languages Python was designed for readability, and has some similarities to the English language with influence from mathematics. Python uses new lines to complete a command, as opposed to other programming languages which often use semicolons or parentheses. Python relies on indentation, using whitespace, to define scope; such as the scope of loops, functions and classes. Other programming languages often use curly-brackets for this purpose. Applications for Python Python is used in many application domains. Here's a sampling. The Python Package Index lists thousands of third party modules for Python. Web and Internet Development Python offers many choices for web development: Frameworks such as Django and Pyramid. Micro-frameworks such as Flask and Bottle. Advanced content management systems such as Plone and django CMS. Python's standard library supports many Internet protocols: HTML and XML JSON E-mail processing. Support for FTP, IMAP, and other Internet protocols. Easy-to-use socket interface. And the Package Index has yet more libraries: Requests, a powerful HTTP client library. Beautiful Soup, an HTML parser that can handle all sorts of oddball HTML. Feedparser for parsing RSS/Atom feeds. Paramiko, implementing the SSH2 protocol. Twisted Python, a framework for asynchronous network programming. Scientific and Numeric Python is widely used in scientific and numeric computing: SciPy is a collection of packages for mathematics, science, and engineering. Pandas is a data analysis and modeling library. IPython is a powerful interactive shell that features easy editing and recording of a work session, and supports visualizations and parallel computing. The Software Carpentry Course teaches basic skills for scientific computing, running bootcamps and providing open-access teaching materials. Education Python is a superb language for teaching programming, both at the introductory level and in more advanced courses. Books such as How to Think Like a Computer Scientist, Python Programming: An Introduction to Computer Science, and Practical Programming. The Education Special Interest Group is a good place to discuss teaching issues. Desktop GUIs The Tk GUI library is included with most binary distributions of Python. Some toolkits that are usable on several platforms are available separately: wxWidgets Kivy, for writing multitouch applications. Qt via pyqt or pyside Platform-specific toolkits are also available: GTK+ Microsoft Foundation Classes through the win32 extensions Software Development Python is often used as a support language for software developers, for build control and management, testing, and in many other ways. SCons for build control. Buildbot and Apache Gump for automated continuous compilation and testing. Roundup or Trac for bug tracking and project management. Business Applications Python is also used to build ERP and e-commerce systems: Odoo is an all-in-one management software that offers a range of business applications that form a complete suite of enterprise management applications. Try ton is a three-tier high-level general purpose application platform.
harvardinformatics / SnparcherSnakemake workflow for highly parallel variant calling designed for ease-of-use in non-model organisms.
GantisStorm / Essentials Claude CodeAll-in-one workflow plugin—loops, swarms, and teams on Claude Code's Task System. All enforce exit criteria—swarm is faster with parallel queue execution, teams add contract-first coordination. Plan your way, execute your way. Optional: Beads for persistent memory, Ralph TUI for dashboard.
esurovtsev / Langgraph AdvancedAn advanced LangGraph series exploring real-world agent workflows, dynamic tools, parallel execution, long-term memory, and human-in-the-loop designs. Includes hands-on Python notebooks for building scalable, production-ready AI agent architectures.
leandrosilva / CameronCameron is an asynchronous, parallel, and REST-like workflow engine
XCodingLab / XCodingXCoding: a lightweight AI vibe coding IDE that supports parallel multi-agent collaboration (Codex/Claude Code) and parallel multi-project development, featuring a VS Code–like editor and terminal with task-driven workflows and code change management.
masa16 / PwrakeParallel Workflow extension for Rake, runs on multicores, clusters, clouds.
icflorescu / Next Server Actions ParallelA small utility library that enables you to execute Next.js server actions in parallel - the missing ingredient to build a boilerplate-free tRPC-style server-actions workflow.
JinyuanSun / DDGScanDDGScan: an integrated parallel workflow for the in silico point mutation scan of protein
usemozzie / MozzieLocal-first desktop app that orchestrates AI coding agents in parallel — work items, git worktrees, dependency tracking, and review workflow in one window.
llsc-supercloud / Teaching ExamplesExamples for use in teaching HPSC workflows, from how to successfully parallelize code, to how to submit that code on an HPC system.
