18 boosters for "collaboration" — AI-graded, open source, ready to install
A comprehensive guide to creating and managing autonomous agents within the CrewAI framework, enabling developers to build specialized AI agents with defined roles, capabilities, and collaboration features. Ideal for AI engineers and developers building multi-agent systems.
An MCP Server that integrates Alibaba Cloud DevOps API capabilities into AI assistants, enabling automated participation in development workflows and team collaboration optimization. Useful for teams using Alibaba Cloud infrastructure who want AI-assisted DevOps processes.
An MCP server that integrates Liveblocks real-time collaboration APIs into Claude, enabling AI agents to build and manage collaborative features like comments, presence, and document synchronization. Useful for developers building AI-powered collaborative applications.
Establishes standardized Git workflow patterns including conventional commit formatting, rebase strategies, and pre-commit hooks to maintain clean repository history and improve team collaboration.
Markdown Agents is a framework for defining AI agents using simple markdown files with YAML frontmatter, enabling version-controlled, portable agent configuration across Claude Code and Desktop. Developers benefit from reduced cognitive load, git-native memory tracking, and predictive AI collaboration without session amnesia.
This booster provides structured AI instructions for the Windsurf platform, establishing conventions for maintaining an AI Memory Bank and using development tools like screenshot verification. Developers working on cmsstore projects benefit from consistent AI collaboration patterns and preserved institutional knowledge.
AGENTS.md-Templates provides ready-to-use templates and best practices for documenting multi-agent AI systems with clear role definitions, capabilities, and collaboration patterns. Developers building multi-agent applications benefit from standardized documentation that ensures reusability and clarity across projects.
A system prompt that instructs Claude to evaluate employee self-reviews on six writing-quality dimensions (outcomes, specificity, clarity, conciseness, ownership, collaboration) and return structured JSON scores with rationales and suggestions. Useful for HR and engineering leaders who need to assess self-review quality at scale.
PRD-to-Project is a Windsurf rules configuration that automates project setup from product requirement documents, enabling AI-assisted collaborative development with structured workflows, security patterns, and quality standards. Developers and product teams benefit from reduced boilerplate setup and streamlined AI collaboration.
Clawgram is a photo-first social network designed for AI agents to share images and interact within a secure, sandboxed environment. It benefits AI developers building multi-agent systems that need safe social collaboration features.
A Cursor IDE configuration framework that establishes structured collaboration rules between humans and AI assistants, defining roles, communication styles, and contribution areas. Developers using Cursor will benefit from explicit guidelines on how to work with AI collaborators and onboard new ones.
Cursor-Demo provides a comprehensive set of development rules and workflows (SPARC methodology) designed to guide AI-assisted coding in Cursor with emphasis on code quality, security, and structured collaboration between human developers and autonomous agents.
Multi-agent collaboration architect for complex phase tasks. Designs team compositions, manages shared context, orchestrates handoffs, resolves conflicts, and drives convergence toward phase deliverables within the dev-system pipeline.
A system prompt for orchestrating bug fixes across coding platforms (Claude, Cursor, Windsurf, ChatGPT) with enforced guardrails, diagnostic workflows, and multi-phase collaboration processes.
AI Agents provides a framework for designing accountable, self-learning agents that operate within organizations through defined lifecycle stages, interaction patterns, and governance controls. It benefits teams building AI-native organizations who need structured approaches to agent deployment, oversight, and human-AI collaboration.
Orchestrates multi-agent systems with distributed consensus, fault tolerance, and emergent behavior patterns. Ideal for developers building collaborative AI systems, decentralized applications, and complex distributed coordination workflows.
Multi-agent collaboration architect for complex phase tasks. Designs team compositions, manages shared context, orchestrates handoffs, resolves conflicts, and drives convergence toward phase deliverables within SDLC pipelines.
A multi-agent orchestration system that designs team compositions, manages shared context, and resolves conflicts to drive complex development tasks toward completion. Ideal for teams tackling large-scale, multi-phase projects requiring coordinated agent workflows.
