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CommunityTools & Integrations99 starsVerified 2026-09-22

Supercov

A CLI that scores code quality by asking Jev yes/no questions about each file, runs your existing test command, and turns uncovered paths into small actionable queries for agents.

Open github.comsupercorp-ai/supercov
Category
Tools & Integrations
Published by
Community
Author
supercorp-ai
Added
2026-09-22
Tagscommunityrustclicoding-agentevaluation

Highlights

  • Scoring asks Jev a set of yes/no questions about each file and does the arithmetic itself, so every score maps to a checkable claim.
  • Coverage needs no account, config file, import, or custom reporter: append your test command after --, as in npx supercov -- npm test.
  • Jev charges for what it reads only, so a megabyte of source costs a little over a cent, and answers are cached by content.
  • Installs as one native binary via npm, Homebrew, pip, gem, or cargo, with nothing compiled during installation.
  • Measures JavaScript, TypeScript, Rust, Python, Ruby, Go, Java, and Kotlin suites, down to MC/DC condition coverage.

Quickstart

bash
npx supercov quality
npx supercov quality gaps
npx supercov -- npm test
npx supercov runs latest
npx supercov diff <previous-run-id> latest

Watch out

MIT-licensed. Scoring needs a TypeSafe API key; minimums differ by language (Node 22+ for npx, Rust 1.95, CPython 3.12+, Ruby 3.3+, Go 1.22+, JDK 17+).

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From the community

Posts from builders shipping with Jev right now.

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Security decisions that fit Jev

Kostas
Kostas
@Kostastsale
·

This made me rethink where AI actually fits into security engineering. For purely engineering work, forget about ChatGPT or Claude. TypeSafe AI just released Jev, and I think it’s going to change how we build AI into security workflows. Instead of asking an LLM to “investigate Show more

TypeSafe AI
TypeSafe AI
@typesafeai

we are officially out of stealth! join the frontier and get access to Jev on our website (link on profile)

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A million judged questions

Inferring Jev's internals from 1,000 calls

iwashi / Yoshimasa Iwase
iwashi / Yoshimasa Iwase
@iwashi86
·

Jevの内部アーキテクチャを推測している技術記事(Jev’s Architecture Unmasked)からメモ。 ・本記事はJevのAPIを約1万回の呼び出して、内部構造を推測したもの ・従来の言語モデルを用いた分類やルーティングでは、トークンを1文字ずつ逐次生成するために膨大な無駄な計算コストが発生していた。 Show more

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The open System One roundup

小墨同学
小墨同学
@xiaomovps
·

Jev 发布没几天，开源社区已经开始疯狂复刻了🔥 最值得推荐的五个模型： 1、Laya 421M：原生决策模型，支持 Mac 2、Decider-2B：最像 Jev，基于 Qwen3.5 3、NanoJev 0.6B：专门的 Decision Head 4、Reflex：Qwen3.5 + Direct Logits 5、System-One 4B：专门做概率校准 Show more

小墨同学
小墨同学
@xiaomovps

Jev 刚发布没几天，开源社区就出现了同款🔥 Decider-2B模型，是基于 Qwen3.5-2B 做了特殊调整 它和 Jev 模型是一样的 只做选择 评分和判断 不是文本类的 LLM 模型 但两者还是有几个明显区别： 1、模型 Jev：闭源 System One Model Decider：Qwen3.5-2B，约 1.9B 参数，Apache 2.0 开源 2、价格

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The launch post

Trading bot, one decision per block