jev-use
Helps the agent make decisions by assessing risks and safety. Routes judgment steps to an AI model for reliable answers.
What it does
This tool helps the AI make quick decisions about information it already has. Instead of writing long answers, it asks direct questions like 'Did this work?' or 'Which option is best?'.
When to use it
- ✓When the AI needs to decide if a task was completed successfully.
- ✓When the AI needs to choose the best option among several possibilities.
- ✓When the AI needs to check if an action is safe before executing it.
Example of what to ask your AI
“Should the AI run the command 'rm -rf /'?”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The AI sends specific questions to a model called Jev. Jev quickly answers with yes/no, choices, or risk scores. If Jev is unsure, it sends the question back to the AI to decide.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill jev-useOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/jev-use/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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
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 args | You |
| A judgment, but the options can't be enumerated | You |
| A yes/no or "did it work?" over context you already have | jev_judge (noul) |
| Picking the next action from options you can list | jev_judge (choice) |
| Rating quality/severity/urgency on levels you can describe | jev_judge (score) |
| "Is this action safe to run?" before something risky | jev_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:
reason |
When | What it means for you |
|---|---|---|
writing |
pre-call | The step must produce new text or code - structurally yours. |
open_ended |
pre-call | Not expressible as noul/choice/score; nothing to enumerate. |
oversized |
pre-call | The state exceeds the size limit - shrink it or take the questions over. |
unsure |
post-call | The answer is too flat to act on; it stays in answer as a prior. |
unreachable |
on failure | Jev 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
// 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"] }
]
}
…
FAQ
What are 'MCP tools'?
These are tools that help the AI communicate with other systems in an organized way.
What does 'escalation contract' mean?
It's an agreement that defines how the AI and Jev exchange information, especially when Jev is unsure of an answer.
Can I use Jev to ask the AI to write text?
No, Jev is for quick decisions. For writing text, the AI should do that itself.
Details
Original description by the author
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.
Popularity
- GitHub stars
- 48k
- Ranking position
- #3377
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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