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autoresearch-v2

Coordinate empirical testing and formal proof generation for iterative mathematical functions.

9|2|Updated Apr 28, 2026
Search Tags:#formal verification#hypothesis generation#parameter sweep#number theory#cycle classification#mathematical conjecture
One-click install
npx skills add https://github.com/AndrewK404/autoresearch-v2 --skill autoresearch-v2
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoresearch-v2
Source: https://github.com/AndrewK404/autoresearch-v2/tree/main
Command: npx skills add https://github.com/AndrewK404/autoresearch-v2 --skill autoresearch-v2

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, pyarrow, gmpy2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of generating, testing, and refining scientific hypotheses through autonomous cycle management and formal verification techniques.

Core Features & Use Cases

  • Hypothesis Generation and Tracking: Creates and maintains a formal ledger of conjectures and falsifications.
  • Automated Parameter Sweeps: Performs large-scale empirical searches over model parameters and data configurations.
  • Formal Verification: Implements algebraic and number-theoretic proofs to confirm or refute conjectures.
  • Use Case: Researchers exploring generalized mathematical maps can use this Skill to automatically produce and verify cycle counts, residue-class properties, and structural classifications at scale.

Quick Start

Request the AI to analyze a specific parameter family by asking it to generate hypotheses about cycle counts in the given map.

Frequently Asked Questions about autoresearch-v2

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

FAQPage Schema
How do I automate hypothesis generation and formal verification for number-theoretic cycles?▼

You can automate hypothesis generation and formal verification by running large-scale parameter sweeps over iterative functions, using combinatorial enumeration and Diophantine conditions to produce cycle classifications and formal proofs.

How do I perform large-scale parameter sweeps for mathematical conjecture testing?▼

Large-scale parameter sweeps for mathematical conjecture testing execute combinatorial enumeration across model parameters, tracking empirical results in a formal ledger to identify structural properties within complex number-theoretic families.

Does this approach support formal proof generation for Diophantine conditions?▼

Yes, formal proof generation for Diophantine conditions is supported through algebraic and number-theoretic deductive proofs, confirming or refuting generated conjectures during the automated cycle classification process.

Can I use numpy and pandas for structural classification of iterative mathematical functions?▼

Yes, numpy and pandas are core dependencies used alongside gmpy2 and pyarrow to manage large-scale empirical searches and structural classification data for iterative mathematical functions within complex number-theoretic families.

What is the best way to track mathematical conjectures and falsifications during empirical testing?▼

The best way to track mathematical conjectures during empirical testing is maintaining a formal ledger that automatically records generated hypotheses, parameter sweep results, and structural classifications for iterative mathematical maps.

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