Riffo

Analyzes existing file naming patterns in a directory using machine learning to infer naming conventions, then applies learned patterns to rename multiple files simultaneously. The system likely uses sequence models or rule extraction to identify naming schemes (date formats, prefixes, suffixes, numbering) and generates rename suggestions that match detected patterns, reducing manual specification of rename rules.

Analyzes file metadata (type, creation date, size, extension) and optionally file content to automatically sort files into categorized folders. The system likely uses rule-based classification or lightweight ML models to assign files to destination folders based on detected attributes, then executes batch move operations with conflict resolution and undo capabilities.

Provides a visual preview interface showing proposed file renames and moves before execution, allowing users to review, selectively approve, or reject individual operations. The system maintains an operation queue with rollback capability, enabling users to undo batch changes if results are unsatisfactory, likely using transaction-like semantics or operation logs.

Scans specified directories or file sources to discover files matching user-defined criteria (type, date range, size, name pattern), then presents results in a unified interface for batch selection and operation. The system likely uses filesystem traversal with filtering logic to identify candidate files, supporting both simple filters (file type) and complex queries (date ranges, size thresholds).

Analyzes file attributes and optionally content to automatically assign tags or metadata that can be used for organization and search. The system likely uses classification models or rule-based inference to extract or generate metadata (e.g., 'invoice', 'screenshot', 'archived') that augments filesystem metadata, enabling richer organization and retrieval workflows.