243 skills · sorted by relevance
Add social-emotional learning—practicing emotional awareness, self-management, empathy, and collaboration—to subject lessons in OpenMAIC. It connects these goals to academic concepts through discussion, choices, feedback, retries, and reflection.
Build collaborating AI teams with CrewAI by defining roles, tasks, and shared context. Configure ordered execution or manager-led delegation, add memory and tools, and create workflows that branch based on events or results.
Compare five growth loops, mechanisms where user activity brings in more users, to find a suitable approach for a product. Develop an implementation roadmap, measurement plan, and growth estimates based on sharing, collaboration, content, or referrals.
Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.
Build brand partnership strategy — co-branding campaigns, brand collaborations, licensing deals, partner brand alignment, and joint marketing. Use when the user says "brand partnership", "brand collab", "co-branding", "brand collaboration", "partnership marketing", "brand alliance", "co-branded product", "licensing deal", "partner with another brand", "brand collaboration strategy", "how do we partner with brands", "co-marketing partnership", "joint campaign", or wants to build credibility, reach, or revenue through alignment with another brand.
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Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
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Cooperative vibe coding across machines — real-time collaborative development with humans and AI agents. Covers current tools (Live Share, Code With Me, Zed, Cursor multiplayer, tmux), git-based async patterns, AI agent coordination, and the aspirational future of multi-human multi-agent development. Activate on: "pair programming", "vibe coding together", "collaborative coding", "mob programming", "remote pairing", "code together", "multiplayer coding", "Live Share", "Code With Me", "ensemble programming", "cooperative development". NOT for: building a real-time collaboration ENGINE (use real-time-collaboration-engine), git workflow mechanics (use git-best-practices), single-user AI coding assistance (use prompt-engineer).
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Expert en collaboration entre agents IA (swarm, débat, vote, délégation, consensus)
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Cooperative vibe coding across machines — real-time collaborative development with humans and AI agents. Covers current tools (Live Share, Code With Me, Zed, Cursor multiplayer, tmux), git-based async patterns, AI agent coordination, and the aspirational future of multi-human multi-agent development. Activate on: "pair programming", "vibe coding together", "collaborative coding", "mob programming", "remote pairing", "code together", "multiplayer coding", "Live Share", "Code With Me", "ensemble programming", "cooperative development". NOT for: building a real-time collaboration ENGINE (use real-time-collaboration-engine), git workflow mechanics (use git-best-practices), single-user AI coding assistance.
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Design tools that AI agents can choose and use correctly. Define clear descriptions, inputs, and outputs, manage large search results, and plan approval checks, isolated execution environments, and interfaces for collaboration between agents.
Shape agent collaboration around focused clarification, practical options, and small steps that can be checked. This profile emphasizes early drafts, preserving the user's intent, and turning agreed direction into concrete outputs through iteration.
Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.
Design an OpenRig agent team around a project goal. Create team and agent specifications, role guidance, and collaboration rules, then validate the setup and diagnose problems with startup instructions or agent coordination.
Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
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Standing rules for how any role in an AutoSwarm company coordinates with its peers — work-item discipline, messaging, meetings, and blocking collaboration. Loaded only in company_mode.
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Resolve GitHub issues through triage, reproduction, implementation, testing, and collaboration. Use when investigating a reported bug or delivering a feature described in an issue.
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Start a cross-team project with the collaboration contract that prevents the classic collisions — who decides what, how work flows between teams, the communication channels and cadence, and what done means — agreed before the first collision instead of during it. Use when asked kick off this cross-team project right, our two teams keep colliding, define how we'll work with the other team, or set up the partnership before we start. Produces the one-page contract: decision rights, interfaces, cadence, and the done-definition.
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Standard collaboration patterns for all squad agents — worktree awareness, decisions, cross-agent communication
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Implement Git branching strategies, PR workflows, and release management patterns. Configure GitFlow, trunk-based development, or GitHub Flow for team collaboration. Use when establishing version control workflows or improving development team collaboration.
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Design learning tasks built on Ubuntu philosophy emphasising collective knowledge-building and mutual responsibility. Use when fostering collaboration that values community over individual competition.
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Collaboration platform configuration methodology for legal matter sites. Site architecture, workflow identification, dashboard design, data quality governance, and user adoption for SharePoint, Teams, and equivalent platforms. M365 is the reference implementation — outputs are platform-agnostic enough to brief IT or build simple automations without becoming a Power Automate manual. Use when setting up a matter site, identifying workflows to automate, designing reporting dashboards, managing platform data quality, or driving user adoption. Trigger on: 'set up the matter site', 'configure SharePoint', 'build a dashboard', 'what should we automate', 'brief IT on this workflow', 'nobody is using the platform', 'data quality is poor', 'set up Teams channel', 'matter site structure', 'alerts and notifications', 'user training', 'platform governance', 'status dashboard', 'what workflows can we automate', 'matter site template'.
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Use when the user mentions Higgsfield Canvas, a node-based or node graph workspace, an infinite board/canvas, chaining generations into a pipeline, or wants to wire prompts → images → videos across models on one surface. Covers what Canvas is, the node categories, the seven models that run inside Canvas, the named canvas patterns (Simple Seedance, Extend Video, Image Edit, StoryBoard With Elements, Long Video fan-out), the build-free / generate-paid cost model, reusable templates, assets-as-nodes, and Shared Canvas live collaboration. Also trigger on 'Higgsfield ComfyUI alternative', 'node workflow', or 'connect nodes to build a scene/campaign'.
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Cross-agent collaboration protocols, workflow handoff patterns, and commit message formats for TDD/Mikado/refactoring workflows
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Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.
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