Split a judgment into Choice, Score and Noul, lint it, then measure it.
Shingo Imota’s MCP server and agent skill for the Jev API. It breaks a judgment into Choice, Score and Noul questions, lints them, measures them on labelled data, and puts calibrated thresholds in code.
simota/tenbin on GitHub
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Sep 21, 2026
Repository created Sep 18, 2026. Counts read from the GitHub API on Oct 15, 2026, and they move daily — quote them with the date.
how to add a decision model to your AI workflow
1,000 yes/no decisions can cost around:
~$10 on GPT-6 Astra
~$4 on Claude Opus 5.5
~$0.04 on JEV
use language models for planning, writing and coding, and decision models for decisions
a decision model can:
> check your agent’s output before it ships
> approve a draft or request revisions
> route a task to the right agent or tool
> classify leads, tickets or comments
> score fit, quality or priority
> check whether content follows your brand rules
> pick an option from a shortlist
> decide whether to continue, stop or request a review
at a much faster speed as well than any regular LLM
here’s how to add one
1. find the decisions in your workflow
look for repeated steps that need a choice, classification or score
start with one decision you can compare against examples you’ve already reviewed
2. define the question and allowed answers
give the model the information and references it needs
for a content workflow, that could include your draft, voice DNA file, anti slop rules and approved examples
ask it to return a choice such as approve, revise or human review
3. connect the answer to an action
your software uses that answer to decide what happens next
approved drafts move to your review queue, revision requests return to the writing agent, and uncertain cases go to you
4. test before letting it act
compare its answers with your reviewed examples
if it returns confidence scores, check whether those scores match how often it gets the decision right
start with actions you can easily reverse
5. put simple checks first
use code for fixed rules, such as required fields and character limits
then use the decision model for checks that need context, before starting more expensive work
6. track quality and cost
measure cost per completed task, incorrect decisions and how often another model or a person needs to review
use the failures to improve your questions, references and allowed answers
1/ Jev, a decision model by @typesafeai, sparked a burst of projects and discussion. We tested it using Ori Eval against popular LLMs on OpenRouter at judging.
Jev was >5x faster than the next fastest model, and even its slowest requests beat every other model's median.
we almost never test new foundation models but we've been testing this for ~a week @every and it's pretty wild.
the kind of things that will be obviously indispensible in 6-12 months
it doesn't produce words as output, it produces probabilities. so it can efficiently act as a judge in cases where you'd need a Fable-level model—but in our testing was 25x faster and 600x lower priced
excellent vibe check by @hammer_mt on @every:
https://every.to/also-true-for-humans/mini-vibe-check-typesafe-s-jev-judged-everything-i-ve-written-in-0-7-seconds?utm_cta_source=home_main_a_3
