Frontmatter Schema Checker

Detect fields with inconsistent types across your vault (schema violations).

When to Use

Invoke when you want to:

• Find frontmatter fields with mixed types (string vs array)
• Detect data quality issues
• Prepare for vault migrations
• Ensure metadata consistency

Arguments

Process

1. Parse User Input

Recognize schema check requests:

• "check my frontmatter for inconsistencies"
• "find schema violations"
• "are there type mismatches in my metadata?"
• "frontmatter health check"

2. Call MCP Tool

mcp__flywheel__find_frontmatter_inconsistencies()

3. Format Results

Inconsistencies Found:

Frontmatter Schema Check
=================================================

⚠️ Found 3 fields with type inconsistencies

-------------------------------------------------

Field: tags
  Expected: array (majority usage)
  Inconsistencies: 12 notes

  Type Distribution:
    array:   89 notes (88%)  ✓ Expected
    string:  12 notes (12%)  ⚠️ Should be array

  Example violations:
    - daily-notes/2025-12-15.md: tags: "work" (string)
    - projects/alpha.md: tags: "project, active" (string)

  Fix suggestion:
    Convert string values to arrays:
    tags: "work" → tags: ["work"]
    tags: "project, active" → tags: ["project", "active"]

-------------------------------------------------

Field: priority
  Expected: number (majority usage)
  Inconsistencies: 5 notes

  Type Distribution:
    number:  23 notes (82%)  ✓ Expected
    string:   5 notes (18%)  ⚠️ Should be number

  Example violations:
    - tasks/review.md: priority: "high" (string)
    - tasks/urgent.md: priority: "1" (quoted number)

  Fix suggestion:
    Convert to numeric values:
    priority: "high" → priority: 1
    priority: "1" → priority: 1

-------------------------------------------------

Field: date
  Expected: string (YYYY-MM-DD format)
  Inconsistencies: 2 notes

  Type Distribution:
    string:  340 notes (99%)  ✓ Expected
    number:    2 notes (1%)   ⚠️ Numeric dates

  Example violations:
    - archive/old-note.md: date: 20231215 (number)

  Fix suggestion:
    Convert to string format:
    date: 20231215 → date: "2023-12-15"

-------------------------------------------------

Summary:
  Total fields checked: 15
  Fields with issues: 3
  Notes affected: 19

Recommended actions:
  1. Fix 'tags' field in 12 notes (string → array)
  2. Fix 'priority' field in 5 notes (string → number)
  3. Fix 'date' field in 2 notes (number → string)

=================================================

No Inconsistencies:

Frontmatter Schema Check
=================================================

✓ No type inconsistencies found!

All 15 frontmatter fields have consistent types
across all notes in your vault.

Fields verified:
  - type: string (234 notes)
  - status: string (156 notes)
  - tags: array (101 notes)
  - priority: number (28 notes)
  - date: string (342 notes)
  ... and 10 more fields

Your vault schema is healthy! ✓

=================================================

Understanding Schema Violations

Use Cases

• Data quality: "Is my frontmatter consistent?"
• Migration prep: "What needs fixing before export?"
• Dataview debugging: "Why is my query not working?"
• Bulk cleanup: "What fields have type issues?"

Integration

Works well with other skills:

• vault-schema: Understand field overview first
• vault-field-values: See specific value distributions
• vault-search: Find notes to fix by field criteria

Vault Rules Observed

This skill (and the Schema Enforcer agent) follow vault rules:

• Never add wikilinks to YAML frontmatter
• Keys must be lowercase without special characters
• Values should be plain text (except author [[@username]])
• Protected folders must use subfolders

Version: 1.0.0