passto-desk

Convert plain text discussions into a shared Excalidraw workbench with a v3 domain artifact.

Updated Apr 27, 2026
One-click install
npx skills add https://github.com/Handy369/passto-pi-frame --skill passto-desk
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: passto-desk
Source: https://github.com/Handy369/passto-pi-frame/tree/main/extensions/passto-desk/skills/passto-desk
Command: npx skills add https://github.com/Handy369/passto-pi-frame --skill passto-desk

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

passto-desk fixes the disconnect between free-form discussion and durable shared collaboration by forcing conversations to be externalized into a structured, multi-layer workbench that both agents and humans can iteratively modify.

Core Features & Use Cases

  • Four-phase workflow: converts discussion into semantic objects/relations → structural view → visual mapping → Excalidraw scene, ensuring stable roundtrips and consistent evolution.
  • Shared collaboration backbone: produces a v3 domain model (passto-desk-domain-json/v3) that supports continuous updates instead of one-off drawings.
  • Readability-first outputs: generates a minimal, human-readable skeleton first, then incrementally improves lane/group/labels/notes when needed.

What problem does it solve?

It is ideal when you need to collaboratively externalize: processes, architecture, dependencies, stages, decisions, and relationships into a shared visual artifact that can be safely edited over multiple turns.

Quick Start

Ask the AI to use passto-desk to create a shared workbench from your discussion, starting with a minimal skeleton of the main flow and key dependencies.

Frequently Asked Questions about passto-desk

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

FAQPage Schema
How do I turn a chat discussion into a shared Excalidraw workbench?

To turn a chat into a shared workbench, passto-desk models discussion semantics, structures views, maps to visuals, and produces a round-trippable domain v3 artifact for Excalidraw. It generates a minimal human-readable skeleton first, then incrementally improves lanes and labels.

What is the best way to collaboratively map architecture visualization and workflow dependencies?

Collaborative mapping of architecture visualization works best by externalizing relationships into structured, multi-layer visual artifacts. This Skill forces chats into a semantic-to-visual pipeline, ensuring stable roundtrips and consistent evolution of workflow dependencies across multiple turns.

Can I use Excalidraw for multi-turn collaborative editing of complex decision stages?

Yes, Excalidraw supports multi-turn collaborative editing when paired with a structured domain v3 model. It safely handles iterative modifications of decision dependencies by generating a semantic backbone that both agents and humans can continuously update.

How does semantic modeling transform plain text into a visual mapping scene?

Semantic modeling transforms plain text by extracting objects and relations, structuring them into views, and mapping them to a visual scene. This four-phase pipeline ensures readability-first outputs with controlled runtime imports favoring structured edges, lanes, and notes.

Does collaborative workflow mapping support round-trippable domain JSON outputs?

Yes, collaborative workflow mapping supports round-trippable domain JSON v3 outputs. The generated artifact acts as a shared collaboration backbone, allowing continuous updates and safe editing rather than producing one-off, static drawings.

When should I not use a shared workbench map for process visualization?

You should not use a shared workbench map for one-off questions or simple processes that lack object relationships and decision dependencies. It is designed for multi-turn collaboration where architecture, stages, and workflows require iterative refinement.