DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to score tickets or traces on your own rubric with Agent Jev

Place a ticket or agent trace on a 2 to 10 level rubric and get the level and expected score.

Who it is forAn operations lead triaging support tickets or reviewing agent traces who wants a consistent ranking without reading every item.

The problemHand scoring is slow and inconsistent, and a free-text model reply is hard to convert into a stable level.

The outcomeA level from the argmax, a weighted score across the levels, and the full distribution for every item.

What you need first

  • Python with PyTorch and Transformers installed (the README benchmarks used PyTorch 2.9.1+cu128 and Transformers 4.57.6)
  • A GPU; the README timings were measured on one RTX 5060 with 8 GB
  • Git access to clone malevrigns/agent-jev and download weights from the linked Hugging Face model repository
  • A state to evaluate, such as a test runner summary, plus questions you have already phrased as boolean, choice, or score

Steps

  1. Clone the repository and start the server following the README's Run it and HTTP sections.
  2. Write 2 to 10 level descriptions for your rubric, lowest first.
  3. Send each ticket or trace as the state with a score question that uses your level descriptions.
  4. Read level for the argmax and score for the weighted sum, and store the full distribution with the item.
  5. Review items near the boundary between two levels before acting on them.

Prompt for your agent

In the agent-jev repository at <repo-path>, score each item in <items-folder> with a score question built from the rubric in <rubric-file>. Return level, score, and the full distribution for each item. Verify that every level is within the rubric range and that score matches the weighted sum of the distribution.

Caveat. The README says the Typed Decisions teacher labels are the reference for accuracy, not measured success on real coding or support work, so validate scores against your own labelled examples.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page