reviewing-problems

Audit competitive programming problem packages for mechanical consistency and statement clarity.

Updated Jul 29, 2026

Search Tags:#audit #quality-assurance #code-review #competitive-programming #testlib #problem-setting

Ready to run

npx skills add https://github.com/LLaammTTeerr/competitive-programming --skill reviewing-problems

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reviewing-problems
Source: https://github.com/LLaammTTeerr/competitive-programming/tree/main/skills/reviewing-problems
Command: npx skills add https://github.com/LLaammTTeerr/competitive-programming --skill reviewing-problems

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This skill performs a comprehensive, end-to-end audit of a competitive programming problem package to ensure it is ready for deployment, catching subtle issues that automated tools and authors often miss.

Core Features & Use Cases

Mechanical Audit: Automatically detects constraint drift, incomplete phases, orphan files, and matrix holes using specialized tools.
Judgement Audit: Evaluates statement clarity, assumed definitions, unproven invariants, and checker/validator alignment through a fresh-context review process.
Use Case: Before shipping a new problem to a contest judge, use this skill to verify that the statement is unambiguous, the test suite is robust, and the package structure is perfectly aligned with the intended difficulty and constraints.

Quick Start

Invoke the reviewing-problems skill by providing the absolute path to your problem directory to initiate a full mechanical and judgement-based audit.

Frequently Asked Questions about reviewing-problems

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

FAQPage Schema

How do I audit a competitive programming problem package for constraint drift and statement clarity?▼

To audit a competitive programming problem package, you can use an automated tool that evaluates problem directories for constraint drift, checker-validator alignment, and statement ambiguities. It coordinates automated mechanical checks with human-in-the-loop judgement reviews.

What is checker-validator alignment in competitive programming quality assurance?▼

Checker-validator alignment in competitive programming quality assurance ensures that the checker and validator logic match the problem constraints and intended behavior. The audit evaluates this alignment through a fresh-context review process to catch inconsistencies.

Can I automatically detect orphan files and incomplete phases in a testlib problem directory?▼

Yes, you can automatically detect orphan files and incomplete phases in a testlib problem directory. The mechanical audit phase identifies these structural issues, along with matrix holes and constraint drift, using specialized automated tools.

Does the competitive programming problem audit support testlib package structures?▼

Yes, the competitive programming problem audit supports testlib package structures by evaluating problem directories for constraint drift, orphan files, and matrix holes. It verifies package structure alignment with the intended difficulty and constraints.

What's the best way to review problem statements for assumed definitions and unproven invariants?▼

The best way to review problem statements for assumed definitions and unproven invariants is through a judgement audit. This process evaluates statement clarity using a fresh-context review to identify ambiguous definitions and hidden invariants.

Why does my problem package have constraint drift after setting up the test cases?▼

Constraint drift in a problem package occurs when the stated constraints do not match the actual test data or validator limits. Running a mechanical audit detects this drift automatically, ensuring the test suite is robust and aligned before deployment.