Build a metadata extraction CLI with Box AI — using agents.md as your spec

Structured metadata in Box serves as a powerful tool for enhancing search, filters, retention policies, automation, and workflows, and with Box AI, users can extract structured data from unstructured documents and apply it as metadata in a systematic manner. This process is exemplified by building a simple Python CLI that orchestrates the extraction of specific fields from files stored in Box, transforming them into metadata using Box AI Extract Structured, and then writing them back to the files. Central to this implementation is the use of an "agents.md" file, which acts as a comprehensive specification for the project, detailing the expected CLI interface, authentication methods, and necessary SDK imports, thereby allowing AI coding tools to produce consistent and predictable results without needing extensive prompts. The architecture involves a straightforward workflow where the CLI facilitates the extraction, normalization, and metadata writing process, maintaining a clear separation of responsibilities within the code to ensure simplicity and ease of maintenance. This method not only enhances the reliability of AI-assisted development but also provides a robust framework that can be adapted for various workflows beyond metadata extraction, emphasizing the importance of defining project constraints and structure within the repository for effective collaboration with AI tools.

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