wiki-knowledge-base

name

wiki-knowledge-base

description

Build and maintain a local Markdown-based knowledge wiki with Obsidian-style double-links. Inspired by Karpathy's "let's build" approach. Use when the user wants to create a personal knowledge base, wiki, or structured information repository from research articles, competitive analysis, or domain knowledge. Triggers on phrases like "build a wiki", "knowledge base", "knowledge graph", "organize research", "wiki maintenance", "wiki lint", or when working in a directory with wiki/concepts/entities structure.

Wiki Knowledge Base

Build a local, Obsidian-compatible knowledge wiki from raw research materials. Uses a concept-entity-comparison-source architecture with double-link ([[slug]]) networking.

Directory Structure

<project-root>/
├── raw/                  # Immutable source materials (read-only)
│   └── articles/         # Web articles, reports (Obsidian Web Clipper → Markdown)
├── wiki/                 # LLM-maintained knowledge pages
│   ├── index.md          # Master directory (update after every operation)
│   ├── log.md            # Append-only operation log
│   ├── concepts/         # Abstract concepts (AI Agent, MCP Protocol, ...)
│   ├── entities/         # Concrete products/companies/tools (Smithery, Cursor, ...)
│   ├── comparisons/      # Cross-entity analysis tables
│   └── sources/          # Structured summaries of raw/ materials
└── outputs/              # Generated reports, lint results

Page Format

Every wiki page requires YAML frontmatter:

---
title: Page Title
type: concept | entity | source-summary | comparison
sources:
  - raw/articles/filename.md
related:
  - "[[related-slug]]"
created: YYYY-MM-DD
updated: YYYY-MM-DD
confidence: high | medium | low
---

Naming Conventions

• File names: kebab-case (ai-agent.md, mcp-model-context-protocol.md)
• Double-links: must use slug format [[slug]], never Chinese text or PascalCase
• Source references: plain text path to raw/ files in frontmatter

Four Page Types

Type	Purpose	Example
concept	Abstract domain knowledge, definitions, frameworks	AI Agent, MCP Protocol, Coding Agent
entity	Specific products, companies, tools with facts/data	Smithery, Cursor, Claude Code
comparison	Side-by-side analysis tables	MCP Platform Comparison
source-summary	Structured summary of a raw article	提炼 key findings from raw/

Concept vs Entity: concept = "what is X?" (category), entity = "what is Y specifically?" (instance). This avoids duplication—define once, link everywhere.

Three-layer distillation: raw/ (full articles, 10k+ words) → wiki/sources/ (summaries, ~500 words) → wiki/concepts/ + wiki/entities/ (structured knowledge).

Workflow: Ingest

When new materials arrive in raw/:

1. Read new files in raw/
2. Discuss key findings with user
3. Create wiki/sources/<slug>.md summary with proper frontmatter
4. Create or update related concept/entity pages, extracting information from the source
5. Update wiki/index.md with new entries
6. Append operation to wiki/log.md

Workflow: Query

When answering questions from the wiki:

1. Read wiki/index.md to locate relevant pages
2. Read related concept/entity/comparison pages
3. Synthesize answer using [[slug]] citations
4. If answer has lasting value, propose saving as a new wiki page

Workflow: Lint

Run health checks periodically (or when asked):

1. Contradiction detection: Find conflicting claims across pages (e.g., different numbers for same metric)
2. Orphan detection: Find pages with no inbound [[double-link]] from other pages (index.md doesn't count)
3. Dangling links: Find [[links]] pointing to non-existent files
4. Ambiguous links: Find links using Chinese/PascalCase instead of slug format
5. Missing concepts: Find entities mentioned in text but without their own page
6. Content quality: Flag pages with confidence: low or thin content (<100 words)
7. Source coverage: Check that concept/entity pages link back to their source summaries

Fix strategy:

• Dangling links: sed batch-replace to correct slug format
• Ambiguous links: replace with correct slug, or remove [] if too generic (e.g., [[AI]] → plain text)
• Orphan source pages: add [[source-slug]] in corresponding concept/entity page body
• Contradictions: verify against source pages, unify to source-of-truth data

Save lint report to outputs/lint-YYYY-MM-DD.md.

Workflow: Git

After every operation batch:

git add -A && git commit -m "<type>: <description>"

Commit message format: <type>: <description> where type is ingest, lint, fix, create.