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content-inventory

Systematic cataloging of information assets. Creates comprehensive inventories or card sorting materials from content.

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概要

Systematic cataloging of information assets. Creates comprehensive inventories or card sorting materials from content.

インストールコマンド

npx skills add https://github.com/dandye/information-architecture --skill content-inventory

このコマンドをClaude Codeにコピー＆ペーストしてスキルをインストール

ソース

dandye/information-architecture

スター1

フォーク0

更新日2026年2月6日 23:14

ファイルエクスプローラー

2 ファイル

SKILL.md

readonly

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"]

Content Inventory Skill

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.

Inputs

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)

Workflow

Step 1: Asset Discovery

Recursively scan the PATH to identify all files and assets.

Record file paths, names, and sizes.
Filter out administrative files.

Step 2: Sampling (Optional)

If SAMPLE_SIZE is specified:

Select a representative sample (e.g., top traffic, key sections, or random).
Ensure coverage across the hierarchy.

Step 3: Metadata Extraction & Formatting

Extract metadata based on METADATA_EXTRACTION and OUTPUT_FORMAT.

Standard Inventory: Title, URL/Path, Author, Date, Type.
Card Sorting:
- Label: Clean, user-friendly title.
- Description: Short summary/snippet.
- ID: Unique identifier.

Step 4: Output Generation

Compile the data into the requested OUTPUT_FORMAT.

Required Outputs

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

Quick Reference

Purpose: Governance, Audit, and UX Research (Card Sorting).
Use Case: Migration planning, creating datasets for OptimalSort/Maze.

このリポジトリの他の Skills

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vocabulary-overlap-analysis

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Analyze vocabulary overlap and identify named entities unique to domain/document corpora.

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document-tree-generate

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Transform lengthy documents into a semantic tree structure. It extracts sections, summaries, and hierarchies optimized for use with Large Language Models (LLMs).

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ontology-define

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Design formal ontologies using OWL/RDFS. Defines classes, properties, and relationships for complex semantic modeling.

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json-ld-generate

dandye/information-architecture

Generate JSON-LD structured data for web content. Maps content to Schema.org types to improve search engine understanding and rich result eligibility.

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knowledge-graph-generate

dandye/information-architecture

Analyze multiple documents to extract entities and relationships, generating a knowledge graph structure (RDF/Turtle).

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ソース

dandye

dandye/information-architecture

GitHub リポジトリを開く Creator のリポジトリを見る

インストールコマンド

ダウンロード

Manusで実行

役立つ用途SOC

編集者芸術・デザイン・エンタメ・スポーツ・メディア職27-3041L4

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"]

Content Inventory Skill

Inputs

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)

Workflow

Step 1: Asset Discovery

Recursively scan the PATH to identify all files and assets.

Record file paths, names, and sizes.
Filter out administrative files.

Step 2: Sampling (Optional)

If SAMPLE_SIZE is specified:

Select a representative sample (e.g., top traffic, key sections, or random).
Ensure coverage across the hierarchy.

Step 3: Metadata Extraction & Formatting

Extract metadata based on METADATA_EXTRACTION and OUTPUT_FORMAT.

Standard Inventory: Title, URL/Path, Author, Date, Type.
Card Sorting:
- Label: Clean, user-friendly title.
- Description: Short summary/snippet.
- ID: Unique identifier.

Step 4: Output Generation

Compile the data into the requested OUTPUT_FORMAT.

Required Outputs

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

Quick Reference

Purpose: Governance, Audit, and UX Research (Card Sorting).
Use Case: Migration planning, creating datasets for OptimalSort/Maze.