An efficient task manager. Designed to minimize tool confusion and maximize LLM budget efficiency while providing powerful search, filtering, and organization capabilities across multiple file formats (Markdown, JSON, YAML)
MCP stdio — tarefas, anotações e agentes de IA Innov (INNOV_API_BASE_URL + token Sanctum)
MCP Tasks Server, giving LLMs task management capabilities.
Model Context Protocol implementation for TypeScript
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MCP server for Penpot integration
Standalone MCP (Model Context Protocol) server - stdio/http/websocket transports, connection pooling, tool registry
Offload tasks to a pool of workers on node.js and in the browser
MCP server for interacting with Azure DevOps
Run an array of functions in parallel
ClickUp MCP Server - Powering AI Agents with full ClickUp task, document, and chat management capabilities.
MCP Server for Asana
A Model Context Protocol server implementation for Nx
MediaPipe Vision Tasks
Coinbase Design System - MCP Server
Cloud Tasks API client for Node.js
A TypeScript framework for building MCP servers.
Run an array of functions in parallel, but limit the number of tasks executing at the same time
High-priority task queue for Node.js and browsers
Lint files staged by git
GitHub Copilot CLI brings the power of Copilot coding agent directly to your terminal.
Runs a list of async tasks, passing the results of each into the next one
List of ML tasks for huggingface.co/tasks
<h1 align="center">Backlog.md</h1> <p align="center">Markdown‑native Task Manager & Kanban visualizer for any Git repository</p>
A Model Context Protocol (MCP) server that provides tools to interact with the TickTick API
MCP server for OmniFocus on macOS, built on the fast-mcp gem. Exposes OmniFocus tasks, projects, perspectives, and tags to MCP clients over stdio.
Claude Swarm enables you to run multiple Claude Code instances that communicate with each other via MCP (Model Context Protocol). Create AI development teams where each instance has specialized roles, tools, and directory contexts. Define your swarm topology in simple YAML and let Claude instances collaborate across codebases. Perfect for complex projects requiring specialized AI agents for frontend, backend, testing, DevOps, or research tasks.
llm.rb is Ruby's most capable AI runtime. It runs on Ruby's standard library by default. loads optional pieces only when needed, and offers a single runtime for providers, agents, tools, skills, MCP, A2A (Agent2Agent), RAG (vector stores & embeddings), streaming, files, and persisted state. As a bonus, llm.rb is also available for mruby. It supports OpenAI, OpenAI-compatible endpoints, Anthropic, Google Gemini, DeepSeek, xAI, Z.ai, AWS Bedrock, Ollama, and llama.cpp. It also includes built-in ActiveRecord and Sequel support, plus concurrent tool execution through threads, tasks (via async gem), fibers, ractors, and fork (via xchan.rb gem).
+pikuri+ is the convenience bundle for the pikuri AI-assistant toolkit. It ships no Ruby code of its own beyond a tiny entry file that +require+'s each sibling gem; +gem install pikuri+ pulls in pikuri-core, pikuri-extractors, pikuri-pdf, pikuri-skills, pikuri-tasks, pikuri-memory, pikuri-workspace, pikuri-code, pikuri-mcp, pikuri-subagents, pikuri-vectordb, and pikuri-assistant in one shot, and +require 'pikuri'+ boots all of them. Privacy-conscious users who want a minimal dependency tree to audit should install +pikuri-core+ directly and opt into the extension gems they actually need — same +bundle add+ pattern Rails users have always had. See each pikuri-* gem's README for its individual surface.
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