Route enumerable judgment steps - did it work, which option, how risky, is this safe to run - to the Jev judgment model through the jevjudge and jevgate MCP tools, batched into one call per state.

MITAuto-check passedAgent Workflows

Install Jev Use

skills CLI
$ npx skills add majiayu000/claude-skill-registry --skill jev-use -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install majiayu000/claude-skill-registry jev-use --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bash/jev-use .claude/skills/jev-use && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
jev-use
GitHub stars
666
Used in
2 other repos
Token cost
~2.5k tokens
SKILL.md length
1,148 words
Files
2
Skills in repo
1,273
Repo updated
First seen
Licence
MIT

At a glance

Route enumerable judgment steps - did it work, which option, how risky, is this safe to run - to the Jev judgment model through the jevjudge and jevgate MCP tools, batched into one call per state.

  • Works in 4 steps: Install and wire the server → Route each step before working on it → Batch every question about one state… → …
  • Tasks that involve MCP servers
  • SKILL.md covers Overview, When to Use This Skill, How It Works and Examples, plus 4 more sections
  • Calls npx; needs TYPESAFE_API_KEY and OPENROUTER_API_KEY

What it does

Jev Use is an agent skill from majiayu000/claude-skill-registry. Route enumerable judgment steps - did it work, which option, how risky, is this safe to run - to the Jev judgment model through the jevjudge and jevgate MCP tools, batched into one call per state.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

It sits in Agent Workflows, covering MCP servers. It works with Bash. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.

When your agent uses it

  • Tasks that involve MCP servers

Example prompts

  • “/jev-use”

Requirements

  • Node.js
  • A credential in TYPESAFE_API_KEY
  • A credential in OPENROUTER_API_KEY

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Install and wire the server
  2. Route each step before working on it
  3. Batch every question about one state into one call
  4. Honor the escalation contract

What it can do on your machine

Read from SKILL.md and the folder at commit 2d14a69. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • TYPESAFE_API_KEY
    • OPENROUTER_API_KEY
    • AI_GATEWAY_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Jev Use loads about 2.5k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 1,148 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 2d14a69, republished under its MIT licence (© majiayu000). 1,148 words, ~2,491 tokens.

Download SKILL.mdSave it as .claude/skills/jev-use/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
jev-use
description
Route enumerable judgment steps - did it work, which option, how risky, is this safe to run - to the Jev judgment model through the jev_judge and jev_gate MCP tools, batched into one call per state.
category
agent-orchestration
risk
safe
source
community
source_repo
shitianfang/jev-use
source_type
community
date_added
2026-09-19
author
shitianfang
tags
agent-orchestration, mcp, routing, judgment, escalation
tools
claude, codex
license
MIT

Jev Use

Overview

Splits an agent loop by whether a step must produce text. Steps that only produce a decision - is the build done, which of these 30 elements to click, is this shell command safe, keep or drop this message - are handed to Jev, TypeSafe's judgment model, which answers typed yes/no, pick-one and score questions about a state in one forward pass instead of generating tokens. The agent stays the planner and the writer; Jev takes the quick calls.

Everything Jev should not decide comes back under a typed escalation contract, so the handoff is explicit in both directions rather than a guess.

When to Use This Skill

  • Use when the next step is a judgment over context you already have ("did X succeed?", "which option?", "how bad is this?") rather than something to write.
  • Use when you are about to check the same state several times in a row, so the questions can be batched into one call.
  • Use when you want a risk check on a proposed tool call before running it.
  • Do not use when the step must produce new content - text, code, free-form tool arguments - or when the options cannot be enumerated. Those are structurally the model's own work.

How It Works

Step 1: Install and wire the server
bash
npx -y jev-use install

install wires the stdio MCP server into Claude Code, Codex and pi through each harness's own CLI - whichever it finds - and npx -y jev-use doctor verifies backend resolution with one live round trip. Set a provider credential in the environment the agent runs in (TYPESAFE_API_KEY, OPENROUTER_API_KEY or AI_GATEWAY_API_KEY, auto-detected in that order), or set JEV_BACKEND=mock to run keyless with no network calls.

Step 2: Route each step before working on it
The step is...Route
Producing new content: text, code, free-form tool argsYou
A judgment, but the options can't be enumeratedYou
A yes/no or "did it work?" over context you already havejev_judge (noul)
Picking the next action from options you can listjev_judge (choice)
Rating quality/severity/urgency on levels you can describejev_judge (score)
"Is this action safe to run?" before something riskyjev_gate
Step 3: Batch every question about one state into one call

jev_judge takes a single state string plus a questions[] array. Latency is flat in question count, and the cost of the shared state amortizes across the batch, so 13 batched questions cost far less than 13 separate calls. Never call it once per question.

Step 4: Honor the escalation contract

Each verdict carries {id, type, answer, confidence, escalate}, plus reason and hint exactly when escalate is true. An escalated verdict is handed back to you - it is a normal verdict with a hint, never an exception:

reasonWhenWhat it means for you
writingpre-callThe step must produce new text or code - structurally yours.
open_endedpre-callNot expressible as noul/choice/score; nothing to enumerate.
oversizedpre-callThe state exceeds the size limit - shrink it or take the questions over.
unsurepost-callThe answer is too flat to act on; it stays in answer as a prior.
unreachableon failureJev could not be reached - proceed as if it did not exist.

The two pre-call reasons come from a deterministic router, so a step that was never Jev's does not spend a request.

Examples

Example 1: One state, three questions, one call
jsonc
// jev_judge input
{
  "state": "CI run #142: build ok, 214 tests passed, 0 failed; 1 test quarantined as flaky last week",
  "questions": [
    { "id": "passed", "type": "noul",   "question": "Did the run fully succeed?" },
    { "id": "next",   "type": "choice", "question": "Next action?",
      "options": { "merge": "everything green", "rerun": "looks flaky", "hold": "needs attention" } },
    { "id": "risk",   "type": "score",  "question": "How risky is merging now?",
      "levels": ["routine", "worth a look", "incident"] }
  ]
}
jsonc
// result (shape exact, values illustrative)
{
  "verdicts": [
    { "id": "passed", "type": "noul",   "answer": 0.97, "confidence": 0.94, "escalate": false },
    { "id": "next",   "type": "choice", "answer": "merge", "confidence": 0.34, "escalate": true,
      "reason": "unsure",
      "hint": "Treat the answer as a prior, not a decision - reason it out yourself." },
    { "id": "risk",   "type": "score",  "answer": 0.8, "confidence": 0.81, "escalate": false,
      "legend": { "0": "routine", "1": "worth a look", "2": "incident" } }
  ],
  "escalated": true
}

Two verdicts are usable immediately. The third came back escalated with reason: "unsure", so that one question - and only that one - returns to you, with Jev's answer kept as a hint.

A score answer is the expected position on your own levels: 0.8 means "between routine and worth a look, closer to the latter", and legend maps the indices back to your words.

Example 2: Risk check before a tool call
jsonc
// jev_gate input
{
  "state": "Cleaning up build output in the project checkout after a failed release build",
  "tool": "Bash",
  "input": { "command": "rm -rf ./dist" }
}
jsonc
// result
{ "decision": "deny", "confidence": 0.88, "hint": "..." }

jev_gate returns allow, deny or escalate for one proposed action. allow is silence: it falls through to the harness's normal permission flow, so the gate can never grant anything - it can only deny or ask. If Jev is unreachable the gate steps aside rather than granting.

Show full SKILL.md (479 more words)Show less

Best Practices

  • ✅ Do: Collect every question you have about one state and send them in a single jev_judge call.
  • ✅ Do: Put the relevant facts - tool output, file excerpts, task intent - into state. Jev sees nothing else about your session.
  • ✅ Do: Read escalate on every verdict before acting on answer.
  • ✅ Do: Describe options and levels in your own words; a label -> meaning map sharpens a choice.
  • ❌ Don't: Call jev_judge once per question.
  • ❌ Don't: Route trivia. If you already know the answer, just act - a call you did not need is still a call.
  • ❌ Don't: Treat an unsure answer as a decision, or treat allow from jev_gate as authorization.

Limitations

  • Jev judges only what is in state; it has no view of your conversation, repository or tool history, so a thin state produces a thin judgment.
  • State has a size ceiling (roughly 30k tokens). Beyond it the request escalates as oversized instead of being judged.
  • Confidence is not uniform across backends: the default escalation threshold is 0.75, but through the Vercel gateway no confidence field is returned and a top-minus-runner-up margin is used instead, with a default threshold of 0.4.
  • The advantage is narrower than an unconfigured comparison suggests. The project publishes its losing runs in bench/RESULTS.md: against enum-constrained baselines the latency lead is about 3x, not the 14x an unconstrained comparison shows. The benchmarks in bench/examples/ are re-runnable scripts, so check the claim on your own workload.
  • This skill does not replace environment-specific validation, testing, or expert review.

Security & Safety Notes

  • Data egress: whatever you put in state is sent to the provider you configure. Keep secrets, credentials and customer data out of it, or set JEV_BACKEND=mock, which judges locally with no key and no network call.
  • Credentials: the provider key is read from the environment (TYPESAFE_API_KEY, OPENROUTER_API_KEY, AI_GATEWAY_API_KEY). Never paste a key into a state string, a prompt, or a committed file.
  • jev_gate is not a permission system. It can deny or ask; it cannot grant. Keep your harness's own approval rules in place, and expect the gate to step aside if the backend is down.
  • npx -y jev-use install edits local harness configuration through each harness's own CLI. Run it on a machine you control and re-run npx -y jev-use doctor afterwards to see what resolved.

Common Pitfalls

  • Problem: Verdicts come back consistently unsure. Solution: The state is usually missing the fact the question depends on. Put the concrete tool output or file excerpt into state instead of a summary of it, and give options/levels distinguishable meanings.
  • Problem: The call escalates with reason: "oversized". Solution: Trim state to the evidence the questions actually need, or split one oversized state into two smaller judged states.
  • Problem: The step never reaches Jev and returns writing or open_ended. Solution: That is the router working. The step was structurally yours; do it yourself rather than rephrasing it to get past the check.

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/bash/jev-use of majiayu000/claude-skill-registry.

  • SKILL.md
  • metadata.json

Open the folder on GitHubat commit 2d14a69

Used in 2 other repositories

We found 7 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 8, 2026.

Compare with similar skills

Jev Use next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Jev Use compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Jev Use this skillmajiayu000/claude-skill-registry6662 repos~2.5kAutomated safety check: PassMIT
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Record Demoapify/mcpc983—~3.3kAutomated safety check: NotesApache-2.0
Releasejgravelle/jcodemunch-mcp2.7k—~6.5kAutomated safety check: PassCustom licence
Mcpcapify/mcpc983—~3.5kAutomated safety check: PassApache-2.0
Tool Selectiondatabricks-solutions/ai-dev-kit1.9k—~519Automated safety check: PassCustom licence

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Works with

Categories

Questions about Jev Use

What does Jev Use do?

Route enumerable judgment steps - did it work, which option, how risky, is this safe to run - to the Jev judgment model through the jevjudge and jevgate MCP tools, batched into one call per state. Jev Use is an agent skill from majiayu000/claude-skill-registry. Route enumerable judgment steps - did it work, which option, how risky, is this safe to run - to the Jev judgment model through the jevjudge and jevgate MCP tools, batched into one call per state.

When should I use Jev Use?

Jev Use fits situations like: tasks that involve MCP servers.

How do I install Jev Use in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill jev-use -a claude-code`. Or copy the skill folder (skills/bash/jev-use in majiayu000/claude-skill-registry) into .claude/skills/jev-use in your project. Claude Code loads it when a task matches its description.

How do I install Jev Use in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill jev-use -a codex`. Or copy the skill folder (skills/bash/jev-use in majiayu000/claude-skill-registry) into .agents/skills/jev-use in your project. Codex loads it when a task matches its description.

Can I use Jev Use in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/claude-skill-registry --skill jev-use -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jev-use, .gemini/skills/jev-use, .github/skills/jev-use and .opencode/skills/jev-use in your project.

What does Jev Use need to run?

Going by SKILL.md and its folder, Jev Use needs the command-line tools its instructions call (npx) and credentials named TYPESAFE_API_KEY, OPENROUTER_API_KEY and AI_GATEWAY_API_KEY. Our summary lists: Node.js; A credential in TYPESAFE_API_KEY; A credential in OPENROUTER_API_KEY.

Does Jev Use access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Jev Use safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Jev Use use?

Jev Use is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Jev Use use?

About 2.5k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Jev Use?

Skills that share tags, products or a category with Jev Use: Crush Configuration (charmbracelet/crush, 29k stars), Record Demo (apify/mcpc, 983 stars), Release (jgravelle/jcodemunch-mcp, 2.7k stars) and Mcpc (apify/mcpc, 983 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jev Use?

majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 1,273 skills in this directory. The repository was last updated on October 7, 2026.

Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.