# AI use cases for Awesome Jev (AppitStudio/awesome-jev) Repo: [AppitStudio/awesome-jev](https://github.com/AppitStudio/awesome-jev) | Use cases: 4 | Page: https://dedupedai.com/use-cases/appitstudio-awesome-jev/ A curated, community-maintained directory of Jev resources, runnable decision examples, and an installable guide skill for building with TypeSafe AI's System One model. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Find the right Jev starter with a coding agent: https://dedupedai.com/use-cases/appitstudio-awesome-jev/pick-jev-starter-with-guide-skill/ - Run a Jev decision workflow offline with mock responses: https://dedupedai.com/use-cases/appitstudio-awesome-jev/run-offline-jev-decision-demo/ - Build a typed Jev decision call with the Python SDK: https://dedupedai.com/use-cases/appitstudio-awesome-jev/build-typed-jev-decision-with-python-sdk/ - Pick Choice, Score, or Noul for an uncertain task: https://dedupedai.com/use-cases/appitstudio-awesome-jev/choose-decision-pattern-for-uncertain-results/ ## How to find the right Jev starter with a coding agent with Awesome Jev Repo: [AppitStudio/awesome-jev](https://github.com/AppitStudio/awesome-jev) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README Install the Awesome Jev Guide skill and have your agent match your workflow to a Jev example. **Who it is for:** A developer who has a repetitive decision in their workflow, has heard of Jev, and does not yet know which example in the repo fits. **The problem:** Jev has several example patterns and SDKs, and it is not obvious which one matches a given workflow or stack. **The outcome:** A recommended starting example in this repo, with an explanation of what to adapt and which open questions remain. ### Steps 1. Make sure Node.js 22 or later is installed. 2. In your project directory, run `npx skills add AppitStudio/awesome-jev --skill awesome-jev-guide`. 3. Select your coding agent when prompted. For Codex, you can add `--agent codex`. 4. Open your coding agent in that project and describe your workflow in plain language, filling in the bracketed prompt from the README. 5. Ask the agent to name the best starting point in this repo and list what needs adapting. ### Prompt to paste into your agent ```text Use awesome-jev-guide in the AppitStudio/awesome-jev repo. Describe my workflow as , then recommend one starting example and list what I must adapt. Verify that the recommendation points to a file that exists in the repo. ``` ### Caveat The guide is a starting point; the README says mocked tests and reported live smoke checks do not establish model quality on your own workload. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/appitstudio-awesome-jev/pick-jev-starter-with-guide-skill/ ## How to run a Jev decision workflow offline with mock responses with Awesome Jev Repo: [AppitStudio/awesome-jev](https://github.com/AppitStudio/awesome-jev) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README Execute a full decision example using synthetic responses, with no account or API key. **Who it is for:** A developer who wants to see how a state-and-questions workflow behaves before paying for or configuring live access. **The problem:** Setting up an API key and account before seeing a working Jev workflow slows down evaluation. **The outcome:** A local run of a complete workflow that shows how questions, answers, and fallback handling fit together. ### Steps 1. Clone the AppitStudio/awesome-jev repository to your machine. 2. Open docs/getting-started.md and follow its local run steps. 3. Run the example with its synthetic mock responses. 4. Read docs/decision-patterns.md to see how Choice, Score, and Noul differ in the example. 5. Change one question in the example and confirm the output changes as expected. ### Prompt to paste into your agent ```text In the AppitStudio/awesome-jev repo, follow docs/getting-started.md to run the local example with mock responses. Report the output of the run and confirm that no network call or API key was needed. ``` ### Caveat The mock responses are synthetic, so the run shows the workflow shape, not how Jev performs on real data. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/appitstudio-awesome-jev/run-offline-jev-decision-demo/ ## How to build a typed Jev decision call with the Python SDK with Awesome Jev Repo: [AppitStudio/awesome-jev](https://github.com/AppitStudio/awesome-jev) | Difficulty: Advanced | Time: 60 minutes | Basis: From the README Send state and a typed question through the Python SDK and use the returned answer in code. **Who it is for:** A Python developer who needs a yes or no, a ranking, or a category from Jev inside an existing script or service. **The problem:** Turning a fuzzy decision in code into a typed answer that the program can branch on takes careful setup and error handling. **The outcome:** A Python function that asks Jev a typed question and handles the returned answer, using the synchronous or asynchronous client. ### Steps 1. Read the Python SDK repository at github.com/typesafe-ai/typesafe-sdk-python and follow its install instructions. 2. Read the official quick start at docs.typesafe.ai/introduction/quickstart to make a first request. 3. Choose the synchronous or asynchronous client based on your program's structure. 4. Define your state and a typed question, then call the client and read the typed answer. 5. Set the retry behavior you need, since the SDK supports configurable retries. ### Prompt to paste into your agent ```text Use the typesafe-sdk-python client in my to ask Jev one typed question about . Return the typed answer and the code that handles it. Verify that the answer type matches the question definition. ``` ### Caveat The README points to the SDK repository for usage details, so check its current install and call syntax before relying on this path. Risk, credentials: Live calls need a TypeSafe API key, so store it in a private environment variable and never paste it into a chat or commit it. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/appitstudio-awesome-jev/build-typed-jev-decision-with-python-sdk/ ## How to pick Choice, Score, or Noul for an uncertain task with Awesome Jev Repo: [AppitStudio/awesome-jev](https://github.com/AppitStudio/awesome-jev) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README Match a decision task to the right Jev primitive and define what happens when the result is unsure. **Who it is for:** A builder designing an automated decision who has to decide whether the output should be a choice, a score, or a Noul answer. **The problem:** Using the wrong Jev decision primitive, or ignoring uncertain results, can make an automated step act on weak answers. **The outcome:** A written decision pattern for your task, with a rule for handling uncertain results before the code acts on them. ### Steps 1. Read docs/decision-patterns.md in the repo. 2. List the decisions your workflow makes and mark each one as a choice, a score, or a yes-or-no judgment. 3. Match each decision to Choice, Score, or Noul. 4. Write a rule for what the workflow does when an answer is uncertain. 5. Test the rule with one uncertain example before wiring it into live code. ### Prompt to paste into your agent ```text Using docs/decision-patterns.md in the AppitStudio/awesome-jev repo, map the decisions in to Choice, Score, or Noul. Output a table with one uncertain-result rule per decision. Verify that every row names exactly one primitive. ``` ### Caveat The pattern guide describes how to match tasks to primitives; it does not measure how accurate Jev is for your data. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/appitstudio-awesome-jev/choose-decision-pattern-for-uncertain-results/