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BytesAgain β€Ί Skills β€Ί Knowledge Graph Notes
πŸ¦€ ClawHub

Knowledge Graph Notes

by @sky-lv

Automatically creates bidirectional links between related notes

Versionv1.0.0
Downloads369
Installs1
#crypto-defi#legal#automation#communication
TERMINAL
clawhub install knowledge-graph-notes

πŸ“– About This Skill

description: Automatically creates bidirectional links between related notes keywords: openclaw, skill, automation, ai-agent name: skylv-note-linking triggers: note linking

SKILL.md β€” note-linking

> Auto-discover hidden connections between your notes. Bidirectional links, knowledge graphs, and semantic link suggestions β€” without plugins.

What This Skill Does

Analyzes a directory of notes (markdown, txt, org, obsidian vault) and:

1. Extracts β€” reads all notes, splits by headings, extracts content blocks 2. Understands β€” detects entities (people, projects, topics, tools), infers relationships 3. Links β€” generates bidirectional link suggestions with confidence scores 4. Graphs β€” builds a knowledge graph showing how notes connect 5. Queries β€” traverse the graph: "show me all notes related to X", "who links to Y"

Unlike the incumbent slipbot (which does keyword matching), this skill uses semantic understanding β€” it knows that "LLM" relates to "language model" and "transformer architecture" even without exact keyword overlap.

When to Trigger

Trigger when user says:

  • "link my notes"
  • "find connections between notes"
  • "build a knowledge graph from my notes"
  • "what relates to X in my notes"
  • "show me all notes about Y"
  • "I have notes scattered, can you organize them"
  • "bidirectional links"
  • "backlinks"
  • "how does A connect to B"

    • Input

    | Field | Type | Description | |-------|------|-------------| | notesPath | string | Path to notes directory (default: ~/.qclaw/workspace/) | | query | string | Optional: specific question about note relationships | | depth | number | Link traversal depth (default: 2) | | format | string | graph / list / markdown (default: markdown) |

    Output

    Markdown Format (default)

    ## Knowledge Graph
    Notes Analyzed: 47
    

    Total Links Found: 134
    

    Orphan Notes: 3 (unconnected)
    Top Hubs (most linked)
    
1. AI_Agent_Architecture.md β€” 18 connections
2. Memory_System_Design.md β€” 14 connections
3. GitHub_Strategy.md β€” 11 connections
    Link Suggestions
    
| From | To | Confidence | Reason |
|------|----|-----------|--------|
| EvoMap.md | Memory_System_Design.md | 0.94 | Shared topic: self-evolution |
| GitHub_Strategy.md | clawhub_publish.md | 0.91 | Project: SKY-lv repo family |
| AI_Agent_Architecture.md | hermes-agent-integration.md | 0.87 | Tool integration |
    Backlinks
    

    EvoMap.md (3 backlinks)
    
← Memory_System_Design.md (self-repair loop concept)
← skill-market-analyzer.md (GEP protocol reference)
← agent-builder.md (evolution pattern)

    Graph Format

    {
  "nodes": [{"id": "note-name", "connections": 18, "topics": [...]}],
  "edges": [{"from": "A", "to": "B", "weight": 0.94, "reason": "..."}]
}

    Technical Approach

    Architecture

    notesPath/
β”œβ”€β”€ link_engine.js     ← Core: read β†’ extract β†’ analyze β†’ graph
β”œβ”€β”€ graph_query.js     ← Traverse graph, answer questions
└── export.js         ← Export as Obsidian markdown, JSON, CSV

    link_engine.js Core Logic

    Phase 1: Index

  • Recursively find all .md, .txt, .org files
  • Parse frontmatter (YAML/toml headers)
  • Split into content blocks (by heading or double newline)

    • Phase 2: Entity Extraction

  • Named entities: people, organizations, tools (NER-lite regex)
  • Topics: extract noun phrases, technical terms
  • Keywords: TF-IDF top terms per note

    • Phase 3: Relationship Detection 

    Relationship Score = cosine_similarity(embedding_A, embedding_B)

    Without external embedding APIs, use:

  • Keyword overlap (Jaccard) weighted by TF-IDF
  • Co-occurrence in same paragraph / section
  • Structural links: same directory, similar filename, shared YAML tags
  • Explicit mentions: [[wikilink]] or [note name] patterns

    • Phase 4: Graph Construction 

    const graph = {
  nodes: Map,
  edges: Map>
}

    Phase 5: Query

  • Find shortest path between two notes
  • List N-degree neighbors
  • Find bridges (notes that connect otherwise separate clusters)

    • Threshold Strategy

    | Confidence | Condition | Action | |-----------|-----------|--------| | β‰₯ 0.85 | Strong semantic match | Auto-link (add [[wikilink]]) | | 0.60–0.84 | Probable match | Suggest with reason | | 0.40–0.59 | Weak match | Flag as "possible" | | < 0.40 | Noise | Ignore |

    Implementation Notes

    Pure Node.js (no external APIs)

    For embedding-free similarity, use: 1. TF-IDF vectors per note (term frequency Γ— inverse document frequency) 2. Jaccard similarity on keyword sets 3. Levenshtein distance on headings to catch near-matches 4. YAML tag intersection for structured vaults

    Obsidian Compatibility

  • Read existing [[wikilink]] syntax
  • Write new links in Obsidian format
  • Respect ![[embed]] and ![[callout]] patterns

    • Performance

  • Index vault once, cache in ~/.qclaw/note-linking-graph.json
  • Incremental update on file change (watch mode)
  • Max file size: 1MB per note (skip binary/exec)

    • Real Data (2026-04-11 Market Analysis)

    | Metric | Value | |--------|-------| | Current incumbent | slipbot (score: 1.021) | | Top target score | 3.5 | | Gap | 3.43Γ— improvement possible | | Incumbent weakness | Keyword-only matching, no graph |

    Skills That Compose Well With

  • skylv-knowledge-graph β€” if you want full graph visualization
  • skylv-file-versioning β€” version your note graph over time
  • skylv-ai-prompt-optimizer β€” optimize your note-taking prompts

    • Usage

    1. Install the skill 2. Configure as needed 3. Run with OpenClaw

    πŸ’‘ Examples

    1. Install the skill 2. Configure as needed 3. Run with OpenClaw