Organize unstructured files into a canonical directory structure with a searchable index.
npx skills add https://github.com/0xUrsanomics/utopia-os --skill filing-0xursanomics
Please help me install this Agent Skill. Skill: filing Source: https://github.com/0xUrsanomics/utopia-os/tree/main/skills/filing Command: npx skills add https://github.com/0xUrsanomics/utopia-os --skill filing-0xursanomics
This skill solves the issue of confident false negatives where files exist but remain undiscoverable because they lack a canonical home and an index entry.
Run the filing script with the apply flag to move files from your inbox into the canonical archive and update the index.
High-intent search queries and answers about installing and using this skill.
Canonical directory organization is achieved by moving or copying unstructured file collections into a structured archive. The process uses signature-based classification to place documents, images, and logs into predictable directories while maintaining an audit trail.
Automated indexing ensures AI recall by appending generated metadata to a central index. This creates a searchable record so the AI can reliably discover and find files it has stored, solving the problem of confident false negatives where files exist but remain undiscoverable.
Yes, Python 3 is required to run the filing scripts for local file system archiving. System-level utilities for text and OCR extraction are also necessary to process mixed reports, logs, and images, ensuring all artifacts are dated and discoverable.
The best way to process a cluttered inbox of mixed reports and logs is to run the filing script with an apply flag. This automatically moves files from the inbox into the canonical archive, dates them, and updates the central index.
Yes, signature-based classification operates on local file systems to process images and logs. It leverages system-level utilities for OCR and text extraction to accurately categorize and generate metadata for every artifact in the collection.
A key limitation addressed is the risk of confident false negatives, where files lack a canonical home and index entry. Automated metadata generation prevents this by ensuring every moved file is indexed, though proper system-level utilities must be configured for extraction.
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