onesie – An expressive Unix-pipeable CLI for System One models like Jev

onesie is a Unix-style CLI for using System One models in shell pipelines, scripts, and agentic workflows: https://github.com/frodi-karlsson/onesie echo 'EVERYTHING IS DOWN, CALL ME NOW' | onesie 'does this convey urgency' -r # 0.99 It provides caching, batching, deduplication, unix composability, streaming, and even a way to calibrate thresholds and questions against labelled data: onesie calibrate -f shell-safety -i jsonl --map .command --id .id \ --label destroys=.destroys --label secrets=.secrets --label network=.network \ --cuts 0.25,0.5 < examples/data/shell-safety.jsonl destroys, yes/no: labelled 40, 16 yes, 24 no, 0 failed. AUC 1.00 cut flagged catches false alarms right when flagged 0.25 18 16/16 100% 81-100% 2/24 8% 2-26% 16/18 89% 67-97% 0.50 15 15/16 94% 72-99% 0/24 0% 0-14% 15/15 100% 80-100% worst misses echo-overwrite labelled yes answered 0.37 It works with TypeSafe's Jev, Jev via OpenRouter, Berget's System One models, as well as any Jev-compatible APIs.For anyone unaware, System One is Typesafe's term for a class of models. I personally find them super exciting. You can read more here: https://typesafe.ai/blog/introducing-system-one-models-and-j... (https://typesafe.ai/blog/introducing-system-one-models-and-jev).When Jev launched, one of my first toy projects was using it to "lint" files. It was tough, and I found other people doing it better, but I learned the importance of calibrating questions to minimize false negatives/positives.Later I started using scripts to interface with Jev and found myself wanting some features I couldn't find in other CLIs, so I built onesie. Here are some more examples of what onesie can do:Ask three questions in a single request echo 'Third time asking. I was charged twice and nobody answers. Fix it or I am cancelling.' | onesie --ask urgent='is this urgent' \ --ask team='who should handle this' --pick billing,shipping,technical \ --ask mood='how frustrated is the customer' --rate calm,annoyed,furious \ -o values {"urgent":0.91,"team":"billing","mood":"furious"} Filter streamed log entries for human review tail -f app.log | onesie 'does this line report a failure a person must act on' -i lines --merge -o json \ --assert 'answer.value < 0.8' | jq -c --unbuffered 'select(.answers.assert == false)' Gate the command in a script. A failed gate exits 1, and an uncertain one exits 7. Note: Probably don't run this exact gate on user input in prod. echo "$cmd" | onesie -f shell-safety -q case $? in 0) eval "$cmd" ;; 1) echo blocked ;; 7) ask_the_user ;; *) echo 'no answer, blocked' ;; esac ... And many more. Please check out the repo for more examples and a detailed reference!It's obviously hard to try out onesie without an api key for a Jev-like provider, although there are options for using mock data or dry-running. There are instructions in the repo for how to auth with a key.If you want an MCP server or a cost estimate before each run, shaharia-lab/jev-cli has both. It's a nice project.I opted out of an MCP server because my intended audience is people and local agentic workflows. For these, I'm of the opinion that a good CLI is best, especially when combined with a plugin + skills. I also felt most of onesie's standout features aren't well served by the protocol. There are other dedicated Jev MCPs out there that do just fine.I opted out of cost estimates because I'm not interested in estimating tokens or keeping a table of costs up to date when the models are so cheap.You can install it with brew install frodi-karlsson/tap/onesie or go install github.com/frodi-karlsson/onesie/cmd/onesie@latest And yes, development has been very agentic :)It's MIT-licensed.