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design-metadata-schema

Design comprehensive metadata frameworks. Develops structured metadata templates and tagging systems.

Install with Tessl CLI

npx tessl i github:dandye/ai-runbooks --skill design-metadata-schema
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Design Metadata Schema Skill

Develop a comprehensive metadata schema for content management. This skill defines structured fields, validation rules, and standards compliance to improve searchability and management.

Inputs

  • PATH - The content domain to apply the schema to (e.g., "/content")
  • OUTPUT_FORMAT - (Optional) The output format for the schema, e.g., "json-schema", "xml", "markdown" (default: "json-schema")
  • DUBLIN_CORE - (Optional) Boolean, whether to align with Dublin Core standards (default: true)
  • CUSTOM_FIELDS - (Optional) List of custom business-specific fields to include
  • VALIDATION_RULES - (Optional) Boolean, whether to define validation logic for fields (default: true)

Workflow

Step 1: Requirement Analysis

Analyze the content types at PATH to determine metadata needs.

  • Identify common attributes (Title, Date, Author).
  • Identify specific attributes (Product ID, Version, Region).

Step 2: Schema Definition

Define the fields and their properties.

  • Standard Fields: Map to Dublin Core (Title, Creator, Subject, etc.) if enabled.
  • Custom Fields: Define fields specified in CUSTOM_FIELDS or discovered during analysis.

Step 3: Constraints & Validation

If VALIDATION_RULES is true, define:

  • Data Types: String, Date, Integer, Boolean, Enum.
  • Required/Optional: Cardinality constraints.
  • Controlled Vocabularies: Allowed values for specific fields.

Step 4: Schema Output

Generate the schema definition in the requested OUTPUT_FORMAT (e.g., JSON Schema, XML Schema, or Markdown Table).

Required Outputs

A METADATA_SCHEMA object in the specified OUTPUT_FORMAT containing:

  • Field Dictionary: Name, Description, Type, Multiplicity.
  • Validation Logic: Rules for data entry.
  • Mapping: Correspondence to standards (like Dublin Core).

Quick Reference

  • Purpose: Standardize content tagging for consistency and interoperability.
  • Standards: Dublin Core, Schema.org.
Repository
dandye/ai-runbooks
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