Vault Suggest

Suggest Wikilinks Skill

Suggest wikilinks for the current note using Flywheel MCP's intelligent analysis.

Purpose

This skill analyzes the current note and suggests:

• Entities mentioned but not linked
• Relevant concepts based on content
• Similar note connections
• Tag-based link opportunities

Enhanced with get_unlinked_mentions for targeted entity linking.

When to Use

Invoke when you want to:

• Improve current note linking: "suggest links" or "what should I link"
• Find missed connections: "link suggestions" or "wikilink suggestions"
• Link specific entity: "suggest links for databricks"
• After writing: "what to link" after completing a note

Process

1. Get Current Note

Extract current note path from context or ask user:

• If in editor context: Use active note path
• If unclear: Ask user which note

2. Call MCP Tool

Call: mcp__flywheel__suggest_wikilinks
Parameters: {
  text: note_content
}

3. Enhance with Unlinked Mentions

For each high-confidence suggestion:

Call: mcp__flywheel__get_unlinked_mentions
Parameters: {entity: "Databricks"}

This provides:

• Exact locations where entity is mentioned
• Context around each mention
• Line numbers for precise linking

4. Display Suggestions

Wikilink Suggestions for: work/projects/etl-pipeline.md

Found 8 linkable entities:

HIGH CONFIDENCE (90+%)
  1. Databricks (95%)
     "Working with Databricks on the ETL pipeline"
     Target: tech/data/Databricks.md
     Occurrences: 3 mentions in this note

  2. Azure (88%)
     "Deploy to Azure production environment"
     Target: tech/cloud/Azure.md
     Occurrences: 2 mentions in this note

MEDIUM CONFIDENCE (70-89%)
  3. Python (82%)
     "Write Python scripts for data transformation"
     Target: tech/languages/Python.md
     Occurrences: 1 mention

LOW CONFIDENCE (50-69%)
  4. Spark (65%)
     "Use Spark for distributed processing"
     Target: tech/data/Spark.md
     Occurrences: 1 mention

Options:
1. Apply all high-confidence links (2 entities, 5 total mentions)
2. Review and select links individually
3. Apply all links (8 entities)
4. Cancel

5. Apply Links (If User Confirms)

Option 1 (High Confidence Only):

• Apply links for high-confidence entities
• Skip medium/low confidence
• Safe, conservative approach

Option 2 (Review Each):

• For each suggestion:
  • Show context
  • Ask: "Link this entity? (y/n/skip-all)"
  • Apply if yes

Option 3 (Apply All):

• Link all entities across all mentions
• Aggressive linking approach
• May create some false positives

Implementation Details

Confidence Scoring

Flywheel MCP provides confidence scores:

90-100%: Very high confidence
  - Exact title match
  - Clear context
  - Unambiguous entity

70-89%: High confidence
  - Good title match
  - Relevant context
  - Minor ambiguity possible

50-69%: Medium confidence
  - Partial match
  - Ambiguous context
  - Multiple candidates possible

Below 50%: Low confidence (don't suggest)

Safe Linking Rules

When applying links automatically:

NEVER link inside:

• YAML frontmatter (between --- markers)
• Code blocks (triple backtick fenced)
• Inline code (single backticks)
• Existing wikilinks
• Markdown links
• Bare URLs

ALWAYS:

• Use word boundaries (don't partial match)
• Preserve original capitalization
• Link only FIRST occurrence per entity (convention)
• Validate link target exists

Related Skills

• unlinked-mentions: Find mentions of specific entity (this skill uses it!)
• backlinks: See existing connections
• orphans: Find notes with no backlinks
• fix-links: Fix broken links (this creates new links)

Related Hooks

• wikilink-auto.py (PostToolUse hook): Auto-applies wikilinks after edits
  • This skill is the MANUAL version
  • Hook uses cache, this skill uses MCP (more accurate)
  • Hook is automatic, this skill requires invocation

Six Gates Compliance

Performance

• MCP call (suggest): ~500ms-1s depending on note length
• MCP call (unlinked_mentions): ~200ms per entity
• File edit: ~100ms
• Total: 2-5 seconds for typical note with 5-10 suggestions