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KameniAlexNea
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code-review

Review code and deliverables for functional correctness and quality standards.

Updated Feb 24, 2026
Search Tags:#code quality#quality assurance#code review#bug detection#ml competitions#deliverable check
One-click install
npx skills add https://github.com/KameniAlexNea/gladius-agent --skill code-review-kamenialexnea
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: code-review
Source: https://github.com/KameniAlexNea/gladius-agent/tree/main/gladius/utils/templates/skills/code-review
Command: npx skills add https://github.com/KameniAlexNea/gladius-agent --skill code-review-kamenialexnea

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the process of reviewing code and deliverables, ensuring they meet quality standards, are free from critical errors, and adhere to specified formats, thereby reducing bugs and improving overall product reliability.

Core Features & Use Cases

  • ML Competition Review: Catches data leakage, metric errors, and submission format bugs.
  • Open-Ended Task Review: Verifies functional correctness, completeness, and packaging.
  • Quality Scoring: Assigns a quality score (0-100) with justification.
  • Use Case: Before submitting a solution to an ML competition, use this Skill to perform a final check for common pitfalls like target leakage or incorrect metric implementation.

Quick Start

Use the code-review skill to check the attached python script for common ML competition errors.

Frequently Asked Questions about code-review

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

FAQPage Schema
How do I check my python script for data leakage before submitting to an ML competition?▼

To check for data leakage in an ML competition script, use automated code review to identify target leakage, metric implementation errors, and submission format bugs. It assigns a quality score with justification to ensure functional correctness.

How do I review deliverables for functional correctness and packaging in open-ended tasks?▼

Reviewing deliverables for open-ended tasks requires verifying functional correctness, completeness, and packaging robustness. Automated code review assesses these quality standards and provides a quantifiable score with detailed reasoning.

What is the best way to catch metric errors and submission format bugs in machine learning code?▼

The best way to catch metric errors and submission format bugs is performing automated code review specifically targeting ML competitions to validate correctness and adherence to specified formats.

Can I get a quality score for my code to ensure it meets quality assurance standards?▼

You can get a quality score for your code by running a review process that evaluates functional correctness and completeness. It assigns a score from 0 to 100 with justification based on quality standards.

Does automated code review work for checking robustness in python scripts?▼

Automated code review works for checking robustness in python scripts by assessing open-ended tasks for functionality and packaging. It reduces bugs and improves product reliability by ensuring adherence to quality standards.

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