deck

Manage work items as machine-checkable cards in a Kanban-style deck.

6|Updated May 4, 2026
One-click install
npx skills add https://github.com/zauberzeug/game-of-cards --skill deck-zauberzeug
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: deck
Source: https://github.com/zauberzeug/game-of-cards/tree/main/openclaw-plugin/skills/deck
Command: npx skills add https://github.com/zauberzeug/game-of-cards --skill deck-zauberzeug

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Deck provides a single, structured surface to model work as persistent, machine-checkable cards, enabling AI agents and humans to collaborate with clear lifecycle contracts and durable cross-references.

Core Features & Use Cases

  • A stable deck/ surface where each card resides in deck/<title>/ with frontmatter-based status and a machine-enforceable DoD, enabling auditable workflows.
  • Automates end-to-end task lifecycles through a defined set of skills (scan-deck, next-card, create-card, advance-card, decide-card, finish-card, card-schema, refine-deck, audit-deck).
  • Supports multiple operation modes (session, autonomous, andon-cord) to coordinate human and AI agent workloads across queues while preserving card history.

Quick Start

Create a new card with the create-card skill and drive it through open, active, and done with the advance-card and finish-card skills.

Frequently Asked Questions about deck

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I manage persistent work items for AI agent collaboration?

You can manage persistent work items for AI agent collaboration by modeling tasks as machine-checkable cards within a Kanban-style deck, enabling clear lifecycle contracts and durable cross-references between humans and AI. This provides a structured surface for auditable workflows.

What is a machine-checkable card workflow for task lifecycle tracking?

A machine-checkable card workflow tracks task lifecycles using frontmatter-based status and a machine-enforceable Definition of Done (DoD) within a stable directory structure. This enables gate-based workflow transitions and terminal states for auditable task orchestration.

How do I automate Kanban task transitions with frontmatter DoD gates?

Automate Kanban task transitions by applying skills like advance-card and decide-card to drive items through open, active, and done states. These skills enforce frontmatter-defined DoD gates and manage workflow transitions across the deck.

Can I coordinate human and AI agent workloads across multiple queues?

Yes, you can coordinate human and AI agent workloads across queues using multiple operation modes including session, autonomous, and andon-cord. These modes preserve card history while orchestrating collaborative work across different queues.

Does this card-based task orchestration support autonomous operation modes?

Yes, card-based task orchestration supports autonomous operation modes alongside session and andon-cord modes. This allows AI agents to independently manage workloads while maintaining persistent context and lifecycle tracking within the deck structure.

What are the limitations of directory-based lifecycle management for task tracking?

Directory-based lifecycle management relies on a stable deck/ surface where each card resides in deck/<title>/, which requires consistent frontmatter maintenance. Terminal states and gate-based transitions must be strictly enforced to prevent lifecycle tracking errors.