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Systematic cataloging of information assets. Creates comprehensive inventories or card sorting materials from content.
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Based on SOC occupation classification
| name | content-inventory |
| description | Systematic cataloging of information assets. Creates comprehensive inventories or card sorting materials from content. |
| required_roles | {"scribe":"roles/scribe.viewer"} |
| personas | ["information-architect","content-manager","ux-researcher"] |
Create a systematic catalog of information assets within a specified path. This skill builds a comprehensive inventory including metadata, file characteristics, and format analysis. It can also generate materials for card sorting studies.
PATH - The directory or file path to inventory (e.g., "/documentation")OUTPUT_FORMAT - (Optional) The output format: "csv", "json", "markdown", "card-sort-csv", "card-sort-print" (default: "json")METADATA_EXTRACTION - (Optional) Boolean, whether to extract deep metadata (author, date, tags) (default: true)FORMAT_ANALYSIS - (Optional) Boolean, whether to analyze file formats and types (default: true)SAMPLE_SIZE - (Optional) Max number of items to include (useful for card sorting or large audits) (default: all)Recursively scan the PATH to identify all files and assets.
If SAMPLE_SIZE is specified:
Extract metadata based on METADATA_EXTRACTION and OUTPUT_FORMAT.
Compile the data into the requested OUTPUT_FORMAT.
A CONTENT_REPORT in the specified format.
Example (Card Sort CSV):
ID,Label,Description,Url
1,Reset Password,How to recover account,/auth/reset
2,Pricing,Subscription tiers,/pricing
Normalize named entities and concept variants into canonical forms using controlled vocabularies or standard taxonomies.
Analyze vocabulary overlap and identify named entities unique to domain/document corpora.
Transform lengthy documents into a semantic tree structure. It extracts sections, summaries, and hierarchies optimized for use with Large Language Models (LLMs).
Design formal ontologies using OWL/RDFS. Defines classes, properties, and relationships for complex semantic modeling.
Generate JSON-LD structured data for web content. Maps content to Schema.org types to improve search engine understanding and rich result eligibility.
Analyze multiple documents to extract entities and relationships, generating a knowledge graph structure (RDF/Turtle).