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What you can do with Awesome Jev Typesafe

A curated list of tools, SDKs, guides, and articles for building with TypeSafe's Jev, a model that returns typed, calibrated answers instead of text.

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valentynkit/awesome-jev-typesafe on GitHub

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What do you want to do?

5 use cases

  • Give a coding agent the Jev list as a skill

    Install the Awesome Jev list so an agent can look up Jev tools and docs on its own.

    View prompt
    Install the skill from the repo valentynkit/awesome-jev-typesafe, then fetch https://awesomejev.vercel.app/projects.json once and list the entries that match <task>. Expected output: a short list of project names with one-line descriptions and URLs. Verify that every URL appears in projects.json.

  • Make a first typed Jev decision from Python or TypeScript

    Send one state and a few typed questions to Jev and read back probabilities.

    View prompt
    Using the typesafe-sdk-python repo at https://github.com/typesafe-ai/typesafe-sdk-python and the quick start at https://docs.typesafe.ai/introduction/quickstart, write a script that sends the state <state> with one Choice question over <options>. Expected output: the chosen option and its confidence. Verify that the answer is one of the options you passed in.

  • Gate a cheap model's answers with a Jev check

    Let a low-cost model answer first and escalate only the answers Jev rejects.

    View prompt
    Follow the cookbook at https://openrouter.ai/docs/cookbook/evaluate-and-optimize/jev-verified-cascade to build a cascade for <task> that uses typesafe/jev-1.13 as the checker. Expected output: a function that returns the cheap answer when the check passes and the escalated answer when it fails. Verify that the threshold is set from a labeled sample, not guessed.

  • Keep a Claude Code session from filling its context

    Use a Jev-based compaction tool to drop stale tool results and keep the rest verbatim.

    View prompt
    Install the compaction tool from https://github.com/tamaratran/fast-jev-compaction into Claude Code and run it on the session for <task>. Expected output: a context size report that shows dropped stale tool results and kept verbatim entries. Verify that no recent tool result was removed.

  • Compare Jev against a chat model on the same questions

    Run identical typed questions through Jev and an LLM API to see which fits your task.

    View prompt
    Using the repos typesafe-ai/system-one-adapter-python and typesafe-ai/typesafe-sdk-python, run the same questions from <question file> through both clients. Expected output: a table of answers and accuracy per client. Verify that both clients received identical state and question text.

How to use Awesome Jev Typesafe

  1. Check you have what it needs: Access to Jev through the TypeSafe waitlist, or through Vercel AI Gateway, Cloudflare Workers AI, OpenRouter (beta), or Netlify AI Gateway; A terminal with Node.js installed for the npx command (inferred); An API key from the TypeSafe console or the chosen gateway.
  2. Open valentynkit/awesome-jev-typesafe on GitHub and read the README and license.
  3. Open a terminal in the project where the agent works.
  4. Run npx skills add valentynkit/awesome-jev-typesafe.
  5. Start the agent and ask it to list the Jev tools that match a task, such as routing or context pruning.
  6. Check that the agent cites entries from the list and links to the matching repo.

These steps come from our top use case, Give a coding agent the Jev list as a skill, which also has a copy-paste agent prompt.

Awesome Jev Typesafe alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • Access to Jev through the TypeSafe waitlist, or through Vercel AI Gateway, Cloudflare Workers AI, OpenRouter (beta), or Netlify AI Gateway
  • A terminal with Node.js installed for the npx command (inferred)
  • An API key from the TypeSafe console or the chosen gateway
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Questions about Awesome Jev Typesafe

What is Awesome Jev Typesafe?

A curated list of tools, SDKs, guides, and articles for building with TypeSafe's Jev, a model that returns typed, calibrated answers instead of text. It is an open-source project on GitHub: https://github.com/valentynkit/awesome-jev-typesafe.

Who is Awesome Jev Typesafe for?

Developers and AI agent builders who want to add typed, probability-based decisions such as routing, gating, or judging to an app or coding workflow.

What can I do with Awesome Jev Typesafe?

5 use cases so far, for example: Give a coding agent the Jev list as a skill; Make a first typed Jev decision from Python or TypeScript; Gate a cheap model's answers with a Jev check. Each has steps and a copy-paste agent prompt.

What do I need to run Awesome Jev Typesafe?

Access to Jev through the TypeSafe waitlist, or through Vercel AI Gateway, Cloudflare Workers AI, OpenRouter (beta), or Netlify AI Gateway; A terminal with Node.js installed for the npx command (inferred); An API key from the TypeSafe console or the chosen gateway.

What are alternatives to Awesome Jev Typesafe?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Awesome Jev Typesafe safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.