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KameniAlexNea
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submit-check

Validate submission CSV files against sample files for machine learning competitions.

Updated Feb 24, 2026
Search Tags:#validation#data integrity#machine learning#csv#competition#submission
One-click install
npx skills add https://github.com/KameniAlexNea/gladius-agent --skill submit-check
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: submit-check
Source: https://github.com/KameniAlexNea/gladius-agent/tree/main/gladius/utils/templates/skills/submit-check
Command: npx skills add https://github.com/KameniAlexNea/gladius-agent --skill submit-check

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures that your submission files meet the required format and data integrity standards before you upload them to a competition platform, preventing rejections due to common errors.

Core Features & Use Cases

  • Column Validation: Verifies that submission column names and order precisely match the sample.
  • Row Count Check: Ensures the number of rows in your submission aligns with the sample.
  • Data Integrity: Detects NaN or infinite values in numeric columns and checks probability ranges for classification tasks.
  • Use Case: Before submitting your model's predictions to a Kaggle competition, use this Skill to automatically check if your submission.csv file is correctly formatted and contains valid data.

Quick Start

Validate the submission file at /path/to/my/submission.csv against the sample submission.

Frequently Asked Questions about submit-check

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

FAQPage Schema
How do I validate a submission CSV file before uploading it to a machine learning competition?▼

To validate a submission CSV, check it against the competition's sample submission file to ensure exact column names, matching row counts, and the absence of NaN or Inf values. This verifies data integrity and format adherence before upload.

What does CSV validation for machine learning competitions check for?▼

CSV validation checks for exact column name and order matching against a sample submission, identical row counts, absence of NaN/Inf values in numeric columns, and valid probability ranges for classification tasks to ensure platform compliance.

How do I check if my Kaggle submission has the correct column order and row count?▼

You can check your Kaggle submission by comparing it against a sample submission file, which verifies that the column names, column order, and the total number of rows precisely match the expected competition format.

Does submission validation detect NaN values and invalid probability ranges in classification predictions?▼

Yes, submission validation detects NaN or infinite values in numeric columns and checks that probability ranges are valid for classification tasks. This ensures your prediction data maintains strict integrity before submission.

Can I use this CSV validation for any machine learning competition submission format?▼

You can use this CSV validation for any machine learning competition submission that provides a sample submission file. It requires a sample file to verify column names, row counts, and data integrity for your specific competition format.

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