submit-to-latamboard

To submit AI benchmark results via GitHub pull requests, you can automate the packaging of run manifests and model summaries into standardized JSON files for public leaderboard publication. This process bundles evaluation outputs and creates the necessary submission PR.

Packaging raw benchmark outputs into standardized JSON summaries requires processing raw evaluation data into manifest files and model summaries. This automated packaging bundles the configuration and results into the required submission format for leaderboard publication.

Yes, automating benchmark submissions requires local access to the benchy evaluation environment to process raw outputs. You also need Git configuration for repository interaction to facilitate creating GitHub pull requests for score publication.

Yes, you can use this skill to process and package benchmark results for a specific run ID. It processes the raw output from that evaluation run, packages the results, and prepares the necessary files for a submission pull request.

Yes, submitting to LatamBoard triggers automated CI/CD pipelines for live updates. Creating a GitHub pull request to publish scores facilitates the automated pipeline execution, which updates the public leaderboard with the packaged benchmark results.

Limitations of automating benchmark submissions include the strict requirement for a local benchy evaluation environment and proper Git configuration. Without these local dependencies, the skill cannot process raw outputs or interact with the repository to create pull requests.