Skill: sky-lv/skylv-knowledge-graph-notes (version 1.0.0)

--- description: Automatically creates bidirectional links between related notes keywords: openclaw, skill, automation, ai-agent name: skylv-note-linking triggers: note linking --- # SKILL.md — note-linking > Auto-discover hidden connections between your notes. Bidirectional links, knowledge graphs, and semantic link suggestions — without plugins. ## What This Skill Does Analyzes a directory of notes (markdown, txt, org, obsidian vault) and: 1. **Extracts** — reads all notes, splits by headings, extracts content blocks 2. **Understands** — detects entities (people, projects, topics, tools), infers relationships 3. **Links** — generates bidirectional link suggestions with confidence scores 4. **Graphs** — builds a knowledge graph showing how notes connect 5. **Queries** — 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** — 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** — 18 connections 2. **Memory_System_Design.md** — 14 connections 3. **GitHub_Strategy.md** — 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 ```json { "nodes": [{"id": "note-name", "connections": 18, "topics": [...]}], "edges": [{"from": "A", "to": "B", "weight": 0.94, "reason": "..."}] } ``` --- ## Technical Approach ### Architecture ``` notesPath/ ├── link_engine.js ← Core: read → extract → analyze → graph ├── 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** ```javascript const graph = { nodes: Map<noteId, {file, topics, keywords, blocks}>, edges: Map<noteId, Map<noteId, {score, reasons, type}>> } ``` **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 | |-----------|-----------|--------| | ≥ 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 × 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 | 3.43× improvement possible | | Incumbent weakness | Keyword-only matching, no graph | --- ## Skills That Compose Well With - `skylv-knowledge-graph` — if you want full graph visualization - `skylv-file-versioning` — version your note graph over time - `skylv-ai-prompt-optimizer` — optimize your note-taking prompts ## Usage 1. Install the skill 2. Configure as needed 3. Run with OpenClaw


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)