Send a prompt to a local model via Ollama and get the answer back immediately. Runs on this machine, so nothing leaves it and there is no API cost. Use it for work where checking the answer is cheaper than producing it: summarising long output, drafting boilerplate or commit messages, extracting fields from text. It is a small model on CPU (~16 tok/s for a 7B), so keep outputs short — maxTokens is the main latency lever. It has no file access and cannot run anything. For work needing judgement, repo context, or edits on disk, do it yourself or use codex_start.

Put a piece of text into one of the labels you supply, using a local model. The reply is validated against your label set rather than trusted: if the model answers with something outside the list, or hedges between labels, this returns matched=false with the raw reply instead of guessing. By default the model may also answer that no label fits, which comes back as declined=true. That escape hatch helps but does not hold: a small model will still pick a confident in-set label for text that belongs to none of them, so a returned label is triage, not a verdict. Set allowNone=false only when a forced choice is genuinely wanted. Good for triage — log lines, error vs warning, which files look relevant, is this diff risky. Cheap and private; use it where a wrong answer is cheap for you to detect.

List the models Ollama has on disk, with sizes, plus which one this server uses by default. Call it when a request names a model you are not sure exists.