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Jev

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joetawil7
jev

Sets up, checks or changes the optional Jev judge, the TypeSafe decision model that ship-check asks about review findings (is this real harm, is a small one worth fixing now, what proof does a small fix need). Shows whether it is on for each repo, which key it uses, switches it on or off, and sends one test request. Runs only when the user types it.

Overview

Publisherjoetawil7
Repositoryfirst-pass
Skill namejev
Stars
113
Forks
0
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by joetawil7 on GitHub. Read the source before you install it.

Installation

Install the Jev AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/joetawil7/first-pass/tree/main/skills/jev
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/joetawil7/first-pass.git /tmp/first-pass
mkdir -p .claude/skills
cp -r /tmp/first-pass/skills/jev .claude/skills/jev
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jev in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Jev on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Jev is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

jev

Jev is TypeSafe's decision model: it picks one option from a list and says how sure it is. With the Claude Code plugin, ship-check can ask it three things about a review's findings:

  • harm: does this finding do real harm (money, data, a side effect done twice or sent wrong, security, legal, a crash, stuck work, or the change not doing its job)? Jev can make a finding real harm. It can never clear one the review named, and an answer below its confidence floor, or any failure, leaves the call to the rules.
  • priority: for the list of smaller findings, which to recommend fixing now.
  • proof: for a small fix the user picked, the least proof it needs (the repo's checks only, a unit test, a test against the real database or service, or a browser test). A real-harm fix always gets the full proof.

Without the judge, the rules make each call as written. Nothing here is needed to use first-pass.

What leaves the machine

For each finding asked about: its scenario, its worst case, who meets it and, for proof, the fix in one line, after secrets, emails and phone numbers are blanked. They go to TypeSafe's API (https://api.typesafe.ai/v1/systemone) with the key. TypeSafe charges per word sent; check the current price on its models page (https://docs.typesafe.ai/models) before telling the user a number. Its data terms: https://docs.typesafe.ai/legal.

1. Where it stands

For the current folder, and for one repo of each group the user keeps apart (two companies with separate accounts):

node "${CLAUDE_SKILL_DIR}/../../scripts/cli.mjs" jev status <repo>

It prints on or off, why, where the key comes from (a variable name and a file, never the key) and the config file's path.

2. What the user wants

From what they typed (nothing: show the status and ask; "off" or "on": do that), or ask in one message:

  • Do they have a Jev API key? If not, say where one comes from (TypeSafe's console, https://console.typesafe.ai/keys) and stop.
  • Where is it set? The name of the variable that holds it, and whether it is in the environment or in an env file (the file's path; it must be one git ignores). Never ask for the key itself; if one is pasted, don't repeat it, and say it should be rotated.
  • For which repos? One key for everything, or a key per group of repos when they belong to separate accounts (then every group is named, and a repo no entry names has the judge off).

3. Write the config

The config file is the path jev status printed (first-pass/jev.json in the Claude Code config folder, ~/.claude unless CLAUDE_CONFIG_DIR says otherwise). Read it first if it exists and change only what the user asked:

One key for everything:

json
{ "keys": [{ "env": "TYPESAFE_API_KEY" }] }

A key per account:

json
{
  "keys": [
    { "env": "COMPANY_A_TYPESAFE_API_KEY", "file": "/abs/path/company-a/.env.local", "repos": ["/abs/path/company-a", "/abs/path/company-a-site"] },
    { "env": "COMPANY_B_TYPESAFE_API_KEY", "repos": ["/abs/path/company-b"] }
  ]
}
  • env: the variable's name. It is read from the environment first, then from file.
  • file: optional, an env file holding NAME=value.
  • repos: absolute paths. The deepest one that holds the folder wins, and a checkout of a named repo elsewhere (a worktree, a clean checkout in the temp folder) counts as that repo, even inside a folder named for another key. An entry without repos covers every folder, and is allowed only as the only entry: with separate accounts, a folder no entry names has the judge off, never another account's key.
  • With separate accounts, name each repo's own folder. Never name a folder that also holds another account's repos (the main folder above them all, say): a named folder covers every folder inside it, new repos included, so a repo cloned there later would get that key.
  • "off": true switches the judge off everywhere and keeps the rest. "model" pins a version (default jev-latest); pin one when you have tuned against it.
  • "url": only to send somewhere other than TypeSafe's endpoint (https, or this machine for a test server); jev status then shows it. Leave it out otherwise, and remove one the user did not ask for.
  • Before writing a file path, check git ignores it: git -C <its repo> check-ignore -q <file>. If it is not ignored, say so and don't use it.

4. Check it works

Run jev status <repo> again for each group, then once per key:

node "${CLAUDE_SKILL_DIR}/../../scripts/cli.mjs" jev test <repo>

It sends one made-up finding (no code, no customer data) and prints Jev's pick and how sure it was. A refused key or no answer prints why.

5. Report

Jev judge: <on for <repos> (key from <NAME>, in the environment | in <file>) | off, <why>>, one line per key
Verified: jev test → <Jev's pick and confidence, or the error>, one line per key
Config: <the file, and what changed>

Frequently asked questions

What does the Jev AI skill do?

Sets up, checks or changes the optional Jev judge, the TypeSafe decision model that ship-check asks about review findings (is this real harm, is a small one worth fixing now, what proof does a small fix need). Shows whether it is on for each repo, which key it uses, switches it on or off, and sends one test request. Runs only when the user types it.

Why use Jev on TypingMind?

Because you install it once and use it with any model. Jev is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Jev in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/joetawil7/first-pass/tree/main/skills/jev. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Jev?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Jev?

As many as you like. As long as a model supports skills, you can use Jev with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Jev AI skill free?

Yes. It is published on GitHub by joetawil7 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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