Jevify

Give your agent a System One capability.</strong><br />
  Use Jev during everyday agent work, and find what it can improve in your product.
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<p align="center">
  <a href="#get-started">Get started</a> ·
  <a href="#what-people-are-building">Community examples</a> ·
  <a href="#three-primitives-many-possibilities">Question design</a> ·
  <a href="SKILL.md">Read the skill</a>
</p>

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## Why Jevify?

An agent can have access to Jev and still read every document, load every tool result, and repeat every judgment itself. Jevify teaches it to recognize work that a fast, inexpensive decision model can handle, then gives it the tools to act.

[TypeSafe Jev](https://typesafe.ai) returns typed judgments and probabilities. It can select useful passages, rank candidates, check many items, or choose a handler. Jevify helps the agent send those decisions to Jev, bring selected source evidence into context, and continue reasoning toward your answer.

The agent's own reading and reasoning cost tokens too. Jevify makes Jev the default for bulk semantic judgments, assuming it is faster and cheaper than spending reasoning-model tokens on those judgments. The agent delegates the first pass, reads selected evidence, and finishes the task. It does not stop to build a cost comparison before using the tool.

There are two entry points:

- **Ordinary tasks:** a large document, noisy tool output, or repeated semantic checks can trigger Jevify even when you never mention Jev. The agent uses it where the task, available runtime, and external-inference permissions allow.
- **"Jevify this":** ask it to inspect a product or workflow and identify concrete improvements, including capabilities that frequent, inexpensive judgments could enable.

The skill teaches Choice, Noul, and Score, shared-s