math-olympiad

Solve competitive math problems with adversarial verification and confidence calibration.

Updated Jul 8, 2026

Search Tags:#LaTeX #confidence calibration #math problem solving #adversarial verification #math competition

Ready to run

npx skills add https://github.com/markshd12-cloud/Jarvis --skill math-olympiad-markshd12-cloud

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: math-olympiad
Source: https://github.com/markshd12-cloud/Jarvis/tree/main/agents/.claude/plugins/marketplaces/claude-plugins-official/plugins/math-olympiad/skills/math-olympiad
Command: npx skills add https://github.com/markshd12-cloud/Jarvis --skill math-olympiad-markshd12-cloud

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves complex math problems, particularly those from competitions like IMO, Putnam, USAMO, and AIME, with a focus on adversarial verification to ensure accuracy and confidence in the solutions.

Core Features & Use Cases

Adversarial Verification: Uses a fresh-context adversarial verifier to catch errors that self-verification might miss.
Confidence Calibration: Provides calibrated confidence in the solutions, avoiding bluffing.
PDF Generation: Produces clean PDFs of the verified solutions if LaTeX is available.
Use Case: When faced with a challenging math problem, use this Skill to solve it and receive a verified solution with a calibrated confidence level.

Quick Start

Solve the following problem: Prove that the sum of the squares of the first n odd numbers is equal to n times the square of n.

Frequently Asked Questions about math-olympiad

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

FAQPage Schema

How do I solve complex math competition problems with verified accuracy?▼

To solve complex math competition problems with verified accuracy, this Skill applies adversarial verification using a fresh-context verifier to catch errors that standard self-verification might miss. It ensures solution correctness for competitions like IMO, Putnam, USAMO, and AIME.

What is adversarial verification in math problem solving?▼

Adversarial verification in math problem solving is a technique that uses a fresh-context verifier to challenge and check solutions. It catches logical or computational errors that self-verification might miss, providing calibrated confidence and avoiding bluffing in the final answer.

Do I need Python to solve math problems with adversarial verification?▼

Yes, you need Python installed to run deep mode computations for solving math problems with adversarial verification. Python is required to perform the complex calculations needed to verify competition-level math solutions.

Can I generate a PDF of my math competition solutions using LaTeX?▼

Yes, you can generate a PDF of your verified math competition solutions if LaTeX is available. The Skill supports LaTeX formatting to produce clean, readable PDF documents of the final verified outputs.

What's the best way to get calibrated confidence levels for math proofs?▼

The best way to get calibrated confidence levels for math proofs is to use a solver that applies adversarial verification. This approach avoids bluffing by rigorously testing the solution against potential errors and providing a calibrated confidence score.