DEDUPED Hot AI repos, turned into use cases Get the Monday 5

How to fill a missing table column from labelled rows with Paddock

Paste a table with labelled rows and let Kumo Tabular predict the missing column for new rows.

Authorization notice. Paddock is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Who it is forA data analyst with a small spreadsheet of labelled records where one column is blank for new rows.

The problemBuilding a model to predict a missing column usually means training code, which is too much work for a one-off table.

The outcomePredicted values for the missing column of the new rows, produced in the Studio Tables page with no training step.

What you need first

  • An NVIDIA GPU with the NVIDIA driver installed, or an Apple Silicon Mac running macOS 26 or later
  • Download access to the Paddock release page on GitHub (Windows, Linux, Linux arm64 on DGX Spark, or macOS)
  • Disk space for the model checkpoints you choose to download
  • For cloud comparison: an account with an OpenRouter or other OpenAI-compatible endpoint, if you want to compare against a cloud model

Steps

  1. Run paddock and open the Studio.
  2. Download a Kumo Tabular model (small, medium or large, for classification or regression) from the Studio.
  3. Open the Tables page and paste or open your table, with the labelled rows and the new rows.
  4. Run the prediction to fill in the missing column for the new rows.
  5. Review the predictions and check a few against known values before using them.

Prompt for your agent

Using the Paddock repo at truespar/paddock, send the labelled rows in <table-file> to the /v1/tabular/predictions endpoint with Kumo Tabular and return the predicted values for the unlabelled rows. Verify by checking that the number of predictions equals the number of unlabelled rows.

Caveat. The README describes Kumo Tabular as needing no training, but it does not state accuracy, so predictions should be checked before they drive decisions.

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 The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong.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