push-to-latamboard

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

Merge local benchmark results into the HuggingFace LatamBoard leaderboard dataset.

8|Updated Sep 12, 2025

Search Tags:#automation #benchmarking #data-pipeline #huggingface #leaderboard #ai-evaluation

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires huggingface_hub.

What problem does it solve?

This skill automates the complex process of merging new AI benchmark results into the LatamBoard leaderboard dataset, eliminating the need for manual data manipulation or frontend redeployments.

Core Features & Use Cases

Automated Merging: Integrates new model scores with existing HuggingFace datasets seamlessly.
Live Updates: Updates the leaderboard state immediately upon push, allowing for real-time visibility without frontend rebuilds.
Use Case: After running a new model evaluation, use this skill to instantly publish the performance metrics to the public leaderboard so stakeholders can view the latest rankings.

Quick Start

Execute the merge and publish script by providing the specific run ID of your completed benchmark evaluation.

Frequently Asked Questions about push-to-latamboard

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

FAQPage Schema

How do I publish benchmark results to a HuggingFace leaderboard?▼

To publish benchmark results to a HuggingFace leaderboard, you merge local evaluation output files with the central dataset. This automates integration of model performance metrics for public-facing tracking without manual data manipulation or frontend redeployments.

What is the best way to update a HuggingFace dataset with new AI evaluation metrics?▼

Updating a HuggingFace dataset with AI evaluation metrics requires merging new model scores into the existing repository. This automated merging process instantly updates the leaderboard state, providing real-time visibility without triggering frontend rebuilds.

Do I need HuggingFace authentication to push data to the leaderboard?▼

Yes, HuggingFace authentication is required to push data to the leaderboard. You must also have access to the specific benchmark output directory structure to successfully merge local output files with the central HuggingFace dataset.

How do I integrate new model scores into an existing HuggingFace dataset automatically?▼

You integrate new model scores into an existing HuggingFace dataset automatically by executing a merge and publish script with the specific run ID of your completed benchmark evaluation. This seamlessly merges local outputs with the central dataset.

Can I update public leaderboard rankings without rebuilding the frontend?▼

Yes, you can update public leaderboard rankings without rebuilding the frontend. The automated merging process updates the leaderboard state immediately upon push, allowing stakeholders to view the latest model performance rankings in real-time.