topic-library-operator

To convert natural language requests into topic library commands, you describe your needs in plain language. The system parses intent, track mapping, direction, and count to generate executable make_topic.py commands.

Automated topic library management maps spoken prompts to executable operations for topic generation, active management, and usage tracking. It parses user intent and validates mandatory fields before issuing commands.

To generate and append topics across multiple tracks, provide a natural language prompt specifying track, direction, and count. The system translates this into a make_topic.py command with optional output paths and date filters.

Yes, you can track used topics and view statistics with date filters. The system supports viewing used topics, searching historical records, and generating statistics across one or more tracks.

When mandatory fields are missing, the system guards against invalid inputs by validating required parameters like track and direction before issuing commands, preventing incomplete make_topic.py execution.

Yes, topic library automation works without direct video production steps. It translates natural language prompts into make_topic.py commands for managing topic assets, deliberately avoiding video production phases.