Identify file content types using Magika's ML model with CLI and Python API.
npx skills add https://github.com/iamtatsuki05/dotfiles --skill magika
Please help me install this Agent Skill. Skill: magika Source: https://github.com/iamtatsuki05/dotfiles/tree/main/dotfiles/.agent/skills/magika Command: npx skills add https://github.com/iamtatsuki05/dotfiles --skill magika
Magika identifies the content type of files using a ML model to replace traditional signature checks and to verify that file extensions align with content, enabling fast and accurate classification at scale.
Identify the type of a file by running magika on its path and interpreting the resulting label.
High-intent search queries and answers about installing and using this skill.
Magika identifies file content types using a machine-learning model to replace traditional signature checks. It verifies that file extensions align with actual content, returning structured results like label, mime_type, and score for accurate classification.
Yes, you can automate batch processing for directories and streams using Magika's CLI and Python API. It applies ML-based detection across multiple files, returning structured outputs like label and group for seamless workflow integration.
Detecting MIME types for streams is handled by applying an ML model directly to the data. Magika processes streams via its Python API, outputting the detected mime_type and a confidence score for each input.
Yes, ML-based file type identification with Magika supports over 200 content types. It classifies files, directories, and streams, returning a content label, group, and extensions for each detected type.
You get structured results by calling the Python API to process file paths or streams. Magika returns structured output including label, mime_type, group, extensions, and score for direct integration into automation workflows.
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