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πŸ¦€ ClawHub

Wiki Knowledge Base

by @alexfly123lee-creator

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 w...

Versionv1.0.0
Downloads421
Installs1
TERMINAL
clawhub install wiki-knowledge-base

πŸ“– About This Skill


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

/
β”œβ”€β”€ raw/                  # Immutable source materials (read-only)
β”‚   └── articles/         # Web articles, reports (Obsidian Web Clipper β†’ Markdown)
β”œβ”€β”€ wiki/                 # LLM-maintained knowledge pages
β”‚   β”œβ”€β”€ index.md          # Master directory (update after every operation)
β”‚   β”œβ”€β”€ log.md            # Append-only operation log
β”‚   β”œβ”€β”€ concepts/         # Abstract concepts (AI Agent, MCP Protocol, ...)
β”‚   β”œβ”€β”€ entities/         # Concrete products/companies/tools (Smithery, Cursor, ...)
β”‚   β”œβ”€β”€ comparisons/      # Cross-entity analysis tables
β”‚   └── 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 duplicationβ€”define once, link everywhere.

    Three-layer distillation: raw/ (full articles, 10k+ words) β†’ wiki/sources/ (summaries, ~500 words) β†’ 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/.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]] β†’ 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 ": "
    

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