Generates comprehensive, 16-field structured JSON documentation from analyzed media and text content.
The content-documenter skill is designed to transform deep media analysis into highly structured, machine-readable JSON documentation. Used typically as the final step in a content processing pipeline, it populates a rigorous schema including sentiment analysis, narrative flow, verbatim quotes with timestamps, and personalized relevance scores. This skill is essential for developers and researchers building sophisticated knowledge bases, RAG systems, or automated content repositories that require consistent, high-fidelity metadata across diverse media types.
Key Features
01Generates multi-layered summaries including headlines, TLDRs, and detailed analysis.
0211 GitHub stars
03Calculates personalized relevance scores based on user-profile topic weights.
04Produces a standardized 16-field JSON schema for consistent content archiving.
05Identifies core themes, conceptual connections, and narrative arcs.
06Extracts exact verbatim quotes with support for video/audio timestamps.
Use Cases
01Generating SEO-friendly summaries and category tags for high-volume content management systems.
02Automating the population of searchable metadata for video-on-demand or podcast platforms.
03Structuring research data into standardized formats for Retrieval-Augmented Generation (RAG).
