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dsh-asset-library

Memory Updated 2026.09.16

Run the following command in DeepSeek Harness:

dsh plugin install AmigaMeow/dsh-asset-library

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

Run dsh plugin install AmigaMeow/dsh-asset-library in your DeepSeek Harness terminal to install this plugin; source code is available at https://github.com/AmigaMeow/dsh-asset-library

About this plugin

When collaborating with DeepSeek, the proposals, analyses, and specs AI writes for you get scattered across dozens of workspace files. A few days later you only remember having done something but cannot find which file it was. dsh-asset-library was built for exactly this gap—it docks quietly on the right side of your workspace, dedicated to discovering, organizing, and archiving AI-generated outputs.

At its core is an asset discovery scoring system: each file is rated 0 to 100 on Markdown structure, heading completeness, AI language patterns, and source location. Genuinely valuable AI documents surface as recommendations while scaffolding and source code are excluded outright. A 15-second auto-scan means the moment AI finishes a task the sidebar gently expands; one click on Save and it lands in a local Markdown asset library organized by project, session, time, and type. Built-in session import, slash commands, model tools, HTTP API, and a Markdown reader with auto-generated collapsible TOCs round out the experience.

Ideal for developers and knowledge workers who frequently produce documents, analyses, and design specs with DeepSeek but struggle to find, manage, and reuse those AI outputs. Zero external dependencies—install and go.

Use Cases

  • Auto-detect and suggest archiving AI-generated documents after a task completes
  • Browse all AI outputs from the current session in a contextual sidebar
  • Save assistant replies as searchable notes with a single slash command

Best For

  • Developers who frequently generate documents, analyses, and specs with DeepSeek
  • Knowledge workers managing AI outputs scattered across workspaces
  • Teams that want to systematically archive and reuse AI collaboration insights