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dsh-session-atlas

Client Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install martinkot9336299-cell/dsh-session-atlas

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install martinkot9336299-cell/dsh-session-atlas in your terminal to install this plugin in DeepSeek Harness; full source at https://github.com/martinkot9336299-cell/dsh-session-atlas.

About this plugin

In DeepSeek Harness, conversations default to an ever-extending scroll waterfall. After a few dozen turns it becomes hard to trace a key decision, compare two lines of thought, or grasp branch structure at a glance on a phone. dsh-session-atlas unrolls that waterfall into a zoomable, draggable, navigable DAG card map: every turn is a full card, Bezier lineage lines make ancestry instantly readable, and AI responses grow on the card in real time.

Its core strengths are the branch workflow and real-time inspection toolset. You can fork from any turn, write draft cards, apply optimistic updates, and archive—exploratory thinking no longer loses its thread. A dockable Turn Inspector, a detail view, and a compare two-pane diff form the inspection trio. Streaming text renders smoothly under an FPS guard, and cross-session watchLive lets you observe another line grow in parallel. Mobile gets touch-first gestures and inertial canvas panning and zooming.

Built for deep exploratory users: researchers and engineers who branch, backtrack, and compare ideas repeatedly within a conversation, as well as teams that switch context frequently on mobile. If dsh-synapse is also installed on your machine, remove it first—both plugins share canvas state keys and will interfere with each other.

Screenshots

Use Cases

  • Tracing the full context of a key decision in a long conversation
  • Forking multiple parallel thought paths from the same turn
  • Quickly browsing and comparing branched conversations on a phone

Best For

  • Researchers who branch and backtrack repeatedly within a conversation
  • Workers who switch context frequently on mobile devices
  • Engineers who need to extract and compare material from long threads