proof-writer

Draft dependency-aware mathematical proof packages for ML/AI theorems.

Updated Apr 1, 2026
One-click install
npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill proof-writer-wenwen555
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: proof-writer
Source: https://github.com/Wenwen555/ARIS-LVLM/tree/main/skills/proof-writer
Command: npx skills add https://github.com/Wenwen555/ARIS-LVLM --skill proof-writer-wenwen555

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This tool automates the creation of rigorous mathematical proofs for ML/AI theory. It translates a user-provided theorem statement along with explicit assumptions into a complete, verifiable proof package that is ready for review or publication.

Core Features & Use Cases

  • Direct, structured proof drafting: transforms the input into a step-by-step, dependency-aware proof document.
  • Normalization and verification: clarifies notation, assumptions, and ensures logical consistency.
  • Appendix-ready outputs: generates an organized Proof Package with an explicit strategy, dependencies, and open risks.

Quick Start

Provide a complete proof package for the given theorem statement.

Frequently Asked Questions about proof-writer

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

FAQPage Schema
How do I draft a rigorous mathematical proof for ML/AI theorems?

To draft a rigorous mathematical proof, provide your theorem statement with explicit assumptions, notation, and proof sketches. The tool transforms these inputs into a structured, dependency-aware proof document ready for review or publication.

What is a dependency-aware proof package for machine learning theory?

A dependency-aware proof package is a structured document that models the workflow from claim extraction to full justification. It enforces explicit assumptions, announced strategy, and stepwise logical consistency for exact ML/AI theorems.

Can I use proof sketches and notes to generate a verifiable ML theorem proof?

Yes, you can input proof sketches and notes alongside your theorem statement. The tool normalizes notation, clarifies assumptions, and generates an appendix-ready output with explicit dependencies and open risks.

Does the proof drafting process enforce logical consistency and explicit assumptions?

Yes, the proof drafting process enforces logical consistency by requiring explicit assumptions and notation. It models the workflow from claim extraction to a full proof document, ensuring stepwise justification throughout.

What format do I need to provide for exact ML/AI theorem verification?

You need to provide an exact theorem statement, lemma, proposition, or corollary. Include explicit assumptions, notation, and any available proof sketches or notes to generate the complete proof package.

When should I not use automated proof drafting for AI theory?

You should not use automated proof drafting when your theorem statement lacks explicit assumptions or clear notation. The tool requires a defined claim and structured inputs to enforce logical consistency and generate verifiable proofs.