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    ai-team-builder-kanban

    by Roy Yuen

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

    Updated May 2026
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    $5

    · or 25 credits

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    Included in download

    • Decompose large coding projects into verifiable AI agent task cards.
    • Design handoff protocols to prevent context loss between specialized agents.
    • Includes example output and usage patterns
    • Instant install
    • One-time purchase

    Sample Output

    A real example of what this skill produces.

    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"}

    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.

    Use Cases

    • 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.

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    Tags

    multi-agent
    kanban
    orchestration
    workflow-design
    hermes
    sdlc

    Creator

    Roy Yuen

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