submit-to-latamboard

Name: submit-to-latamboard
Availability: InStock
Author: SURUS

Package benchmark outputs into JSON summaries and submit via GitHub pull requests.

8|Updated Sep 12, 2025

Search Tags:#automation #github #benchmarking #evaluation #pull-request #latamboard

Ready to run

npx skills add https://github.com/surus-lat/benchy --skill submit-to-latamboard

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: submit-to-latamboard
Source: https://github.com/surus-lat/benchy/tree/main/.agent/skills/submit-to-latamboard
Command: npx skills add https://github.com/surus-lat/benchy --skill submit-to-latamboard

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill streamlines the process of submitting AI benchmark results to the LatamBoard leaderboard, removing the manual overhead of packaging data and creating pull requests.

Core Features & Use Cases

Automated Packaging: Automatically bundles benchmark run manifests, model summaries, and configuration files into the required submission format.
Streamlined PR Workflow: Facilitates the creation of GitHub pull requests to publish scores, triggering automated CI/CD pipelines for live updates.
Use Case: After running a model evaluation, use this skill to process the raw output, package the results, and prepare the necessary files for a submission PR to the LatamBoard repository.

Quick Start

Use the submit-to-latamboard skill to process and package the benchmark results for run ID 20260622_143022_LIMITED.

Frequently Asked Questions about submit-to-latamboard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I submit AI benchmark results to a public leaderboard via GitHub pull requests?▼

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.

What is the best way to package raw benchmark outputs into standardized JSON summaries for publication?▼

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.

Do I need a local benchy evaluation environment and Git configuration to automate benchmark submissions?▼

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.

Can I use this skill to process and package benchmark results for a specific run ID?▼

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.

Does submitting to LatamBoard trigger automated CI/CD pipelines for live leaderboard updates?▼

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.

What are the limitations of automating benchmark submissions for AI model evaluations?▼

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.