Schema Compute

Computed Frontmatter

Auto-compute derived fields from note content. Generates metadata like word count, link count, and reading time.

When to Use

Invoke when you want to:

• Add analytics fields to notes
• Track note length and complexity
• Generate reading time estimates
• Add last_updated timestamps

Available Computed Fields

Arguments

Process

1. Parse User Input

Recognize computation requests:

• "add computed fields to projects/Phoenix.md"
• "calculate word count for this note"
• "add reading time to my meeting notes"

2. Call MCP Tool

mcp__flywheel__compute_frontmatter({
  path: "projects/Phoenix.md",
  fields: ["word_count", "link_count", "reading_time"]
})

3. Format Results

## Computed Frontmatter

For: projects/Phoenix.md

| Field | Value | Method |
|-------|-------|--------|
| word_count | 1523 | Prose word count |
| link_count | 24 | Outgoing wikilinks |
| backlink_count | 15 | Incoming links |
| reading_time | "8 min" | word_count / 200 |

### Suggested YAML
```yaml
word_count: 1523
link_count: 24
backlink_count: 15
reading_time: "8 min"

Add these to frontmatter? (y/n)

## Six Gates Compliance

| Gate | Implementation |
|------|----------------|
| 1. Read Before Write | compute_frontmatter reads file first |
| 2. File Exists | Validates path before computation |
| 3. Chain Validation | N/A (single step) |
| 4. Mutation Confirmation | Shows preview, requires confirmation |
| 5. Health Check | Uses MCP health infrastructure |
| 6. Post Validation | Confirms changes after applying |

## Use Cases

- **Content analytics**: "How long are my notes?"
- **Complexity tracking**: "Which notes have many links?"
- **Reading estimates**: "Add reading time to blog posts"
- **Timestamp management**: "Update last_updated field"

## Integration

Works well with other skills:
- **schema-infer**: See if computed fields are common
- **vault-search**: Query by computed values
- **schema-apply**: Add to batch operations

---

**Version:** 1.0.0