DeepSeek Harness 插件

dsh-plugin-lifensame

Global memory plugin for the DeepSeek Harness: persistent cross-session memories, automatic per-task recall, and model-facing add/search/list/delete/summarize tools(英文原文)

跳到安装方式

来源信息

GitHub 仓库
lifensame/dsh-plugin
最近更新
2026年8月15日
分类
工具与能力
GitHub stars
3
载体类型
plugin
目录证据
上游声明已找到 dsh.bundle
证据路径
package.json#dsh.bundle
核对版本
0.1.0-rc.8
上游核对日期
2026-08-20

该证据由上游目录提供。本站没有安装、运行或安全审核这个插件。

安装

默认先复制一段 Prompt,让 Agent 读 GitHub 仓库和源码;需要自己装时再切到命令。

复制这段 Prompt,发给 DSH、Codex 或其他 Agent,让它先读 GitHub 仓库和源码。

请先不要安装或执行任何命令。阅读这个插件的 GitHub 仓库、README 和关键源码,然后用清楚、直接的方式回答以下问题,帮助我判断它是否适合我的需求:

1. 这个插件是什么,解决什么问题;
2. 适合哪些用户和典型使用场景;
3. 安装后如何使用,并给出一个最小使用示例;
4. 有哪些已知限制,以及隐私、安全、兼容性或维护风险;
5. 给出“推荐 / 有条件推荐 / 不推荐”的明确建议和理由。

请区分仓库明确说明、根据源码推断和未知信息。证据不足时请明确说明,不要猜测或照抄 README。

GitHub:https://github.com/lifensame/dsh-plugin
插件名:dsh-plugin-lifensame
作者:lifensame

检查来源文件

安装前先看这个插件目录里的 README 和其他文件。

文件资源管理器4 个文件
README.md来源说明 · 只读预览
README 语言

dsh-memory

Global memory plugin for the DeepSeek Harness (dsh). It gives every agent a persistent, cross-session long-term memory: durable facts, user preferences, and decisions that survive across sessions, projects, and workspaces — and it automatically recalls them into every task.

![Q版 DeepSeek 记忆吉祥物](assets/q-version-deepseek-memory.jpg)

> 📖 Full usage tutorial (install, verify, daily use, config, FAQ): [docs/tutorial.md](./docs/tutorial.md) (中文)

> 中文说明见 README.zh.md

What it does

FeatureHow it works
Global storeMemories are plain Markdown files under $DSH_HOME/memory/*.md (shared by every session, not tied to any workspace).
Automatic recallThe memory context is injected into every agent step, so every task starts with your memories already loaded.
Automatic inputThe agent saves memories itself through tools — no manual file editing.
Auto-summarizememory_summarize distills a long input (or a recap of the conversation) into a compact memory using the language model.
Human commands/memory lists (or searches) memories from the chat UI.

Model-facing tools

  • memory_add — save a durable fact / preference / decision. Reusing the same title (or id) updates the existing memory.
  • memory_search — keyword search over titles, tags, and body.
  • memory_list — list all memories (optionally filtered by tag).
  • memory_delete — forget a memory by id.
  • memory_summarize — distill text into a compact memory via the LLM, then save it.

The standing injected instruction also tells the agent to proactively keep memory current (save new durable facts, update stale ones, avoid duplicates).

Install

With the GitHub CLI and pnpm installed, add the plugin to a profile:

# git source (this repository)
dsh plugin --profile web add github:lifensame/dsh-plugin

# or, from a local checkout
dsh plugin --profile web add <path/to/dsh-plugin>

# or, once published, from npm
dsh plugin --profile web add dsh-memory

Then restart the profile. The first time you add it, pnpm installs the package and dsh reconciles it into the profile's bundle list automatically.

How memories are stored

Each memory is one Markdown file with YAML frontmatter:

---
id: favorite-color-abc123
title: Favorite color
tags: [prefs]
importance: high
created: 2026-01-01T00:00:00.000Z
updated: 2026-01-02T00:00:00.000Z
---
The user's favorite color is blue.

They live under $DSH_HOME/memory (~/.dsh/memory by default), so you can also edit them by hand or put them under version control.

Configuration

All options are optional; the values below are the defaults.

# profile cordis.patch.yml — the dsh-memory row
- id: memory
  name: dsh-memory
  config:
    memoryDir: memory              # directory (relative to $DSH_HOME) holding the .md files
    maxInjectedBytes: 16384        # UTF-8 budget for the auto-injected memory context
    searchDefaultLimit: 5          # default result count for memory_search
    summarizeProvider: ''          # explicit LLM provider route for memory_summarize (empty = default model)
    summarizeModel: ''             # explicit LLM model id, paired with summarizeProvider
    summarizeMaxInputBytes: 24576  # max input size memory_summarize accepts
    summarizeMaxOutputTokens: 512  # max tokens for the summarization call
    summarizeTimeoutMs: 60000      # timeout for the summarization call

Q-version logo

A ready-to-use prompt for generating a chibi (Q版) DeepSeek-memory mascot is in [docs/image-prompt.md](./docs/image-prompt.md).

License

MIT