# AI use cases for Awesome Jev (Amal-David/awesome-jev) Repo: [Amal-David/awesome-jev](https://github.com/Amal-David/awesome-jev) | Use cases: 4 | Page: https://dedupedai.com/use-cases/amal-david-awesome-jev/ A curated list of open-source projects, demos, SDKs and skills built around Jev, TypeSafe's model for yes/no judgments, choices, and scores. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Lint Jev questions before sending them to production: https://dedupedai.com/use-cases/amal-david-awesome-jev/lint-jev-questions-before-deploy/ - Check changed code lines against plain-English rules: https://dedupedai.com/use-cases/amal-david-awesome-jev/check-diffs-against-plain-english-rules/ - Flag anomalous fields in data files with a Jev-based CLI: https://dedupedai.com/use-cases/amal-david-awesome-jev/flag-anomalous-fields-in-data-files/ - Score log entries to find the ones that need a closer look: https://dedupedai.com/use-cases/amal-david-awesome-jev/pick-log-entries-that-need-review/ ## How to lint Jev questions before sending them to production with Awesome Jev Repo: [Amal-David/awesome-jev](https://github.com/Amal-David/awesome-jev) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Catch missing options and broken state references in Jev questions before they run. **Who it is for:** A developer writing Jev Choice, Noul, or Score questions who wants to catch request errors before deploying them. **The problem:** A malformed question with missing options or a broken state reference can produce bad decisions that are hard to trace later. **The outcome:** A question file that passes an offline lint check, ready to send to Jev through the TypeSafe Python SDK. ### Steps 1. Open the wellposed repository at https://github.com/suraj-phanindra/wellposed and read its README for how to run the linter. 2. Read the official TypeSafe Skill at https://github.com/typesafe-ai/skills for guidance on writing Noul, Choice, and Score questions. 3. Run the linter on your question file and note any findings for missing options, broken state references, or wrong question types. 4. Fix each finding and rerun the linter until it reports no problems. 5. Send the corrected question with the TypeSafe Python SDK at https://github.com/typesafe-ai/typesafe-sdk-python, which includes a support-ticket routing example. ### Prompt to paste into your agent ```text Use the wellposed repo (https://github.com/suraj-phanindra/wellposed) to lint the question file at . Fix any missing options or broken state references it reports, then return the corrected file and the linter output. Verify that the linter now reports zero findings. ``` ### Caveat The README describes wellposed only as an offline request linter, so check its own docs for exact usage before relying on 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/amal-david-awesome-jev/lint-jev-questions-before-deploy/ ## How to check changed code lines against plain-English rules with Awesome Jev Repo: [Amal-David/awesome-jev](https://github.com/Amal-David/awesome-jev) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Flag rule violations on the lines a change adds, using rules written in plain English. **Who it is for:** A developer or reviewer who wants automatic feedback on a branch diff without writing formal lint rules. **The problem:** Standard linters miss team conventions that are easy to state in words but hard to encode as rules. **The outcome:** A list of findings tied to added lines in the diff, each matched to a plain-English rule. ### Steps 1. Open the softlint repository at https://github.com/blazejkustra/softlint and read its README for setup and how rules are written. 2. Write your team's rules as short plain-English statements. 3. Install softlint following its README, then run it on the diff of your branch. 4. Review each finding, which points to a line added in the diff, and decide whether to fix or accept it. ### Prompt to paste into your agent ```text Use the softlint repo (https://github.com/blazejkustra/softlint) to check the diff of against these plain-English rules: . Return each finding with its file, added line, and the rule it breaks. Verify that every finding points to a line added in the diff. ``` ### Caveat The README does not say which model or service softlint calls, so confirm where diff content goes before running it on private code. 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/amal-david-awesome-jev/check-diffs-against-plain-english-rules/ ## How to flag anomalous fields in data files with a Jev-based CLI with Awesome Jev Repo: [Amal-David/awesome-jev](https://github.com/Amal-David/awesome-jev) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Find unusual values in selected fields of a data file and review them in local previews. **Who it is for:** An analyst or data engineer checking a CSV or other supported file for odd values before using it. **The problem:** Manual review of many rows for anomalies is slow and misses patterns in selected fields. **The outcome:** A set of flagged anomalies in chosen fields, with local previews and cached assessments for review. ### Steps 1. Open the jevotron repository at https://github.com/cmungall/jevotron and read its README to confirm which file formats it supports. 2. Choose the file and the fields you want checked. 3. Run the jevotron CLI as its README describes on your file and fields. 4. Open the local previews and review each flagged value. 5. Keep or discard each flag, and rerun the CLI to reuse cached assessments for unchanged values. ### Prompt to paste into your agent ```text Use the jevotron repo (https://github.com/cmungall/jevotron) to flag anomalies in the fields of the file . Return the flagged values with their row references and any local preview paths. Verify that every flagged value matches a field you selected. ``` ### Caveat The README lists local previews and cached assessments but does not say whether field values are sent to Jev, so check before using sensitive 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/amal-david-awesome-jev/flag-anomalous-fields-in-data-files/ ## How to score log entries to find the ones that need a closer look with Awesome Jev Repo: [Amal-David/awesome-jev](https://github.com/Amal-David/awesome-jev) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README Rank log entries by a Jev score so reviewers read the most suspicious ones first. **Who it is for:** An engineer on call who has too many log lines to read and needs the risky entries surfaced first. **The problem:** Reading every log entry during an incident takes too long, and important entries get buried. **The outcome:** A ranked list of log entries with scores, so the top entries get reviewed first. ### Steps 1. Open the Jev Logs repository at https://github.com/reachjalil/jevlogs and read its README for how entries are scored. 2. Export the log entries you want to review to a local file. 3. Run Jev Logs on the exported file as its README describes. 4. Read the entries in score order and mark which ones need a closer look. ### Prompt to paste into your agent ```text Use the Jev Logs repo (https://github.com/reachjalil/jevlogs) to score the log entries in and return them sorted by score. Verify that the highest-scored entries are listed first. ``` ### Caveat The README gives only a one-line description of Jev Logs, so confirm its input format and how scores are produced in its own docs. 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/amal-david-awesome-jev/pick-log-entries-that-need-review/