Ingest, query, and maintain markdown pages in a local wiki.
npx skills add https://github.com/Tienching/oh-my-codebuddy --skill wiki-tienching
Please help me install this Agent Skill. Skill: wiki Source: https://github.com/Tienching/oh-my-codebuddy/tree/main/skills/wiki Command: npx skills add https://github.com/Tienching/oh-my-codebuddy --skill wiki-tienching
Persistent, self-maintained markdown knowledge base for project and session knowledge.
Instruct me to add a wiki page by title and content, then query or read pages as needed.
High-intent search queries and answers about installing and using this skill.
You can persist project documentation by ingesting markdown pages with a title and content into a local wiki. The knowledge base captures session logs and design decisions, storing everything centrally for ongoing project reference.
Search markdown documentation using local keyword and tag matching, which ensures fast offline access without vector embeddings. You can link related pages by inserting cross-reference syntax like [[page-name]] directly into your content.
No, this project wiki tool does not need vector embeddings or an internet connection. It uses a local index with keyword and tag matching to query the markdown knowledge base, ensuring fast and completely offline access.
The best way to maintain markdown pages is to use the built-in lint operation on your local project wiki. You can also list, read, and delete pages to manage the knowledge base lifecycle and auto-capture session discoveries.
Yes, you can store design decisions and session logs completely offline under a local .omx/wiki directory. This self-maintained markdown knowledge base centralizes project information without requiring any external cloud database.
OrinksMaintain a local markdown wiki with keyword search and cross-references.
zzafergokConvert Markdown documents to HTML using marked.js, pandoc, gomarkdown, or static site generators.
itsmarsssCompile immutable raw sources into a three-layer Obsidian wiki with provenance fields.
dalagerEnable high-speed semantic vector search with HNSW indexing and metadata filtering.
