deepseekplugins

DeepSeek Harness plugin

dsh-memory-canvas

DSH plugin: offload bulky tool logs to refs/*.md and keep a Mermaid canvas. Don't pip the wrong dsh.

Tools & Capabilities
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Source facts

Repository
guobinmengxiang-rgb/dsh-memory-canvas
Latest update
Aug 17, 2026
Category
Tools & Capabilities
GitHub stars
0
Format
plugin
Catalog evidence
Upstream dsh.bundle evidence
Evidence path
package.json#dsh.bundle
Checked against
0.1.0-rc.8
Upstream check date
2026-08-20

This evidence comes from the upstream catalog. This site has not installed, run, or security-reviewed the plugin.

Install

Start with a prompt that asks an agent to review the GitHub repository and source. Switch to the command if you want to install it yourself.

Copy this prompt into DSH, Codex, or another agent and ask it to review the GitHub repository and source first.

Do not install or run any commands yet. Read this plugin's GitHub repository, README, and relevant source code. Then answer the questions below clearly and directly so I can decide whether it fits my needs:

1. What is this plugin, and what problem does it solve?
2. Who is it for, and what are its typical use cases?
3. How is it used after installation? Include one minimal example.
4. What known limitations or privacy, security, compatibility, or maintenance risks does it have?
5. Give a clear recommendation: recommend, conditionally recommend, or do not recommend, with reasons.

Distinguish statements documented by the repository, inferences from source code, and unknowns. If evidence is insufficient, say so explicitly. Do not guess or simply repeat the README.

GitHub: https://github.com/guobinmengxiang-rgb/dsh-memory-canvas
Plugin: dsh-memory-canvas
Author: guobinmengxiang-rgb

Check the source files

Read the README and other files from this plugin directory before installing.

File explorer4 files
README.mdSource · read only
README language

dsh-memory-canvas

DeepSeek Harness plugin. Offload bulky tool logs to refs/*.md. Keep a Mermaid canvas in context. Recall by node_id. Zero daemon.

CAUTION: don't pip the wrong dsh. Official CLI: npx @deepseek-ai/dsh web. The PyPI package deepseek-harness-cli is a different tool (dsh doctor / dsh chat).

GitHub topic: dsh-plugin. Chinese: README.zh.md

Demo (no dsh)

node demo.mjs

Writes .dsh-memory/refs/{id}.md, prints the canvas, recalls by id, and asserts the body matches.

Install into a profile

This package is plain JavaScript. There is no prepare or build step, so a git install does not need allowBuilds.

dsh plugin --profile web add github:guobinmengxiang-rgb/dsh-memory-canvas

Check the composed layer without booting:

dsh --profile web --dump-config

You should see a dsh-memory-canvas layer. Then start the profile as usual.

Official CLI is @deepseek-ai/dsh (today 0.1.0-rc.6).

Tools

The agent must call these tools. This plugin does not auto-intercept tool logs.

ToolParamsResult
memory_offloadtitle, body (required)short id, path, canvas updated
memory_recallid (required)stored body (truncated at 8k with a note)
memory_canvasnoneMermaid markdown of the task canvas

Storage (default): <cwd>/.dsh-memory/canvas.md and <cwd>/.dsh-memory/refs/{id}.md. Override root with DSH_MEMORY_DIR.

Canvas nodes use a 24-character title. Full text lives in refs/.

What this is not

Inspired by Tencent's short-term symbolic memory (MIT idea, our code): refs + Mermaid canvas + node_id.

Honest limits

  • DeepSeek Harness is a developer preview: THERE WILL BE COMPATIBILITY-BREAKING CHANGES.
  • The plugin runs on your machine.
  • This bundle cordis.patch.yml uses the package name dsh-memory-canvas, not a filesystem path.

Official docs and related

  • Your first plugin: https://deepseek-harness.github.io/deepseek-harness/en/develop/basic/
  • Package and install a plugin: https://deepseek-harness.github.io/deepseek-harness/en/develop/basic/publish
  • Cordis: https://github.com/cordiverse/cordis
  • Sister plugin: https://github.com/guobinmengxiang-rgb/dsh-xhs-note
  • Course: https://github.com/guobinmengxiang-rgb/dsh-getting-started
  • Awesome list: https://github.com/guobinmengxiang-rgb/awesome-dsh

License

MIT (c) 2026 国彬 张

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