cercano-classify

To classify and triage errors or logs locally, you can use local AI inference to parse input text and optional predefined categories, returning a structured result with category, confidence, and reasoning without sending data to cloud services.

Local text classification for code quality assessment is the process of categorizing code or logs using on-device AI inference, enabling quick decisions on errors and code-quality issues while keeping data private.

Yes, you need an Ollama instance and the cercana server to triage stack traces locally. The Skill parses your input text and optional categories to return a category, confidence score, and reasoning.

You can use predefined categories to triage logs or opt for open-ended classification. The Skill parses raw input text alongside your optional categories to return a structured result with confidence and reasoning.

The best way to automate code quality triage without cloud exposure is using local AI inference to parse text and assign categories. This returns structured results with confidence scores and reasoning for quick debugging decisions.

Local text classification requires a separate server setup because it relies on a cercana server and an Ollama instance to run local AI inference. This ensures data privacy by processing input text entirely on-device.