ai Team Builder Kanban

    2

    Architect durable multi-agent Kanban boards with structured handoffs and role-based task decomposition.

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    Works with the AI tools you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+17 more

    See it in action

    You say

    Design a multi-agent plan to migrate our legacy API. We have a linter, a refactor dev, and a QA lead. Include task dependencies and metadata for the handoffs.

    Your agent does

    BOARD: API Migration PROFILES: [LinterBot, RefactorAgent, QA-Lead] TASK-001: Run static analysis (LinterBot) -> STATUS: Todo TASK-002: Rewrite endpoints (RefactorAgent) -> PARENT: TASK-001 METADATA: {"success_criteria": "0 lint errors", "handoff": "Pass artifact path to RefactorAgent"}

    What you get

    Decompose large coding projects into verifiable AI agent task cards.Design handoff protocols to prevent context loss between specialized agents.Map dependencies and 'blocked' logic for parallel AI agent workflows.Standardize worker and orchestrator roles for durable autonomous teams.

    About this skill

    Multi-Agent Task Orchestration

    Complexity is the enemy of automation. When a single AI agent attempts a large-scale project, context drift and task fatigue lead to failure. This skill solves that by architecting Hermes-style Kanban handoff boards—a structured framework for decomposing complex jobs into manageable tasks distributed across a specialized AI team.

    How it Works

    Instead of a single long prompt, this skill generates a blueprint for an entire workflow. It defines specific Agent Profiles (like Researchers, Implementers, and Verifiers) and maps them to a durable Kanban board structure. It focuses on the crucial "handoff" moments, ensuring that when one agent finishes, the next has the exact context, metadata, and artifacts needed to continue without human intervention.

    Key Features

    • Task Decomposition: Breaks monolithic goals into "Ready," "Running," and "Blocked" states with explicit dependency mapping.
    • Handoff Protocol: Defines structured completion metadata and handoff comments to ensure zero context loss between agents.
    • Role Separation: Clearly distinguishes between Orchestrator (planning/unblocking) and Worker (execution) responsibilities.
    • Resilience Engineering: Built-in logic for handling retries, blocked tasks, and verification gates.

    The output is a comprehensive technical plan ready to be implemented in systems like Hermes or any multi-agent framework requiring strict state management and audit trails.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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