DSH-LLM-wiki-plugin
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DSH-LLM-wiki-plugin

Vesna-Strivozha/DSH-LLM-wiki-plugin

依托Karpathy的wiki方法论开发,可直接让DSH实现LLM wiki能力,无需搭配Obsidian+Claudian插件组合,国内网络访问无阻碍,使用门槛极低。

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MIT
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3.8 MB
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1 个月前
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一键安装扩展 / 插件指令
dsh plugin --profile web add github:Vesna-Strivozha/DSH-LLM-wiki-plugin
git clone https://github.com/Vesna-Strivozha/DSH-LLM-wiki-plugin.git
git clone git@github.com:Vesna-Strivozha/DSH-LLM-wiki-plugin.git
README.md main

DSH LLM Wiki Plugin

An Obsidian-style personal knowledge base for DeepSeek Harness.

LLM Wiki turns DeepSeek Harness into a lightweight Obsidian: you drop source files in, the LLM incrementally builds and maintains a persistent, interlinked wiki, and you then ask questions in the chat that are grounded in your knowledge base — with page citations.

English | 中文


Features

Feature What it does
🗂 One-click workspace Pick a directory and build the wiki skeleton (raw/, wiki/, index.md, log.md, schema.md).
📥 Upload + confirm Drag or select files, review them in a confirm dialog, then write to raw/.
🧠 Auto-ingest The LLM reads raw/ and generates/updates wiki/ pages, with a progress bar and PDF text extraction.
🕸 Knowledge graph Force-directed graph of [[wikilinks]] with zoom/pan and click-to-open.
🩺 Self-check (lint) Reports orphan pages, isolated pages, and dead links.
📖 Read in place Markdown renderer plus an inline PDF viewer.
💬 Chat-grounded Q&A The wiki_query tool and a prompt section answer questions with page citations.

Screenshots

LLM Wiki panel LLM Wiki panel
LLM Wiki panel LLM Wiki panel
LLM Wiki panel LLM Wiki panel

Install

dsh plugin --profile <name> add @vesna-strivozha-2026/dsh-llm-wiki

For chat Q&A, add the wiki_query tool row to your agent preset (see the host README below).

Packages

Package Role
packages/dsh-llm-wiki Host engine (the installable bundle): ingest / search / graph / lint, the wiki_query tool, and a prompt section.
packages/dsh-client-ui-llm-wiki Browser panel: file tree, upload, knowledge graph, markdown/PDF reader.

Documentation

Contributing

See CONTRIBUTING.md.

License

MIT