Skip to content

jev-skills

Claude Code and Codex skills powered by Jev, published with a calibration curve measured on 4,995 real agent decisions.

Project

This summary is based on how curated lists describe the project; its README was not available to review.

Overview

This repository provides Claude Code and Codex skills that use Jev, TypeSafe's System One model, for agent judgments. Directory listings describe it as including what it calls the first published Jev calibration curve, measured on 4,995 real agent decisions.

The listing reports 256 calibrated judgements for $0.0005 in 0.72 seconds and frames this as 360 times cheaper than Claude Opus 5. Another listing describes the skills as helping shortlist files, triage findings and extract relevant log lines, and notes no repository licence at the time of review. No README was available for this entry, so details beyond these summaries are unknown.

What the decision model decides

TypeQuestionAnswers
Yes / noAgent judgment calls (e.g. is this file or finding relevant) answered as calibrated probabilities(inferred)—

How it works

  1. Agent skill sends a batch of judgments to Jev
  2. Jev returns calibrated probabilities
  3. Agent acts on results above a threshold

Strengths

  • Listing reports 256 judgments for $0.0005 in 0.72 s
  • Publishes a calibration curve on 4,995 real agent decisions per the listing

Caveats

  • No README available to verify details
  • Listed as having no repository licence at review

At a glance

Type
Project
Runtime
Claude Code / Codex skills

Use cases and integrations

How curated lists describe it

  • Claude Code & Codex skills powered by Jev, TypeSafe's System One model. 256 calibrated judgements for $0.0005 in 0.72s — 360x cheaper than Claude Opus 5. Includes the first published Jev calibration curve, measured on 4,995 real agent decisions.

    logicrw/awesome-jev-projects

Frequently asked questions

What is notable about this repo?

Listings say it includes a Jev calibration curve measured on 4,995 real agent decisions.

Which agents does it support?

Claude Code and Codex, according to the listing.

Is it licensed?

A listing notes it had no repository licence at review.

Related projects

  • Agent skill that tells you when you need an eval and how to build it, with an opt-in lane routing closed-label residue to Jev.

    BenchmarkbetaPython
  • Eval-first MCP server for Jev: prototype questions, map them over many items, and measure accuracy and thresholds on labelled data.

    MCP serverbetaTypeScript
  • Agent skills for placing, building, evaluating and improving decision-model systems, with Jev as the default hosted example.

    Agent skillbeta
  • MCP server exposing Jev as one evaluate tool, plus a harness comparing Jev's judgments with general LLMs on shared datasets.

    MCP serverbetaJavaScript
  • Claude Code and Codex skills plus a study course for building with Jev, including Jevaluate, which rates how well projects use Jev.

    Projectbeta
  • Optimises the instructions and criterion descriptions of a Jev Choice from labelled data with EvoPrompt or GEPA, scored by Brier.

    ProjectbetaPython