memgraph-aco-queries

Query Memgraph ACO pheromone graphs to identify prime candidates and monitor convergence.

2|Updated Dec 20, 2025

Search Tags:#data-analytics #pheromones #memgraph #prime-factoring #graph-queries #aco

Ready to run

npx skills add https://github.com/tonyoconnell/crackrsa32 --skill memgraph-aco-queries

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memgraph-aco-queries
Source: https://github.com/tonyoconnell/crackrsa32/tree/main/.claude/skills/memgraph-aco
Command: npx skills add https://github.com/tonyoconnell/crackrsa32 --skill memgraph-aco-queries

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides powerful queries and analysis for Ant Colony Optimization (ACO) pheromone graphs in Memgraph. It helps data scientists and software engineers quickly identify high-potential prime candidates, monitor convergence, and validate pathways toward factoring RSA-style moduli.

Core Features & Use Cases

Pheromone graph analysis: Retrieve and compare candidate nodes by pheromone strength and fitness.
Convergence monitoring: Track hot candidates and overall collaboration toward factoring targets.
Prime factor discovery support: Mark factors and verify candidate paths against the target modulus.
Use Case: When experimenting with swarm intelligence for educational RSA demonstrations, quickly surface the leading factor candidates from a Memgraph-backed graph.

Quick Start

Ensure Memgraph is running locally or remotely and accessible at the standard port.
In Python, connect to Memgraph with the gqlalchemy client: mg = Memgraph(host="localhost", port=7687)
Load prime candidates into the graph, inspect top pheromone trails, and identify leading factor candidates with a few simple Cypher queries.

Frequently Asked Questions about memgraph-aco-queries

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

FAQPage Schema

How do I query pheromone graphs in Memgraph to find high-potential prime candidates?▼

Query pheromone graphs in Memgraph by retrieving candidate nodes ranked by pheromone strength and fitness using Cypher queries. Connect via gqlalchemy, inspect TRAIL edges with pheromone deposits, and extract top candidates to surface leading factor prospects for your ACO workflow.

What is Ant Colony Optimization and how does it apply to prime factorization?▼

Ant Colony Optimization is a swarm intelligence algorithm where artificial ants deposit pheromones on promising paths. In prime factorization, ACO explores candidate factors by reinforcing high-fitness paths through pheromone trails, enabling collaborative convergence toward factoring RSA-style moduli.

Can I use Memgraph to monitor convergence and validate factor candidates during ACO runs?▼

Yes. Memgraph stores PrimeCandidate nodes, TRAIL edges with pheromone and timestamp data, and fitness metrics. Query convergence metrics, track hot candidates across iterative runs, and validate candidate paths against your target modulus using graph analytics.

How do I set up Memgraph for pheromone graph analysis with Python?▼

Install gqlalchemy and connect to Memgraph locally or remotely at the standard port using `mg = Memgraph(host="localhost", port=7687)`. Load prime candidates into the graph, then run Cypher queries to inspect pheromone trails and extract leading factor candidates.

What happens when pheromone evaporation and deposition occur in the graph?▼

Pheromone deposition strengthens TRAIL edges when ants find fit candidates, while evaporation reduces older deposits over time. This dual mechanism guides the swarm toward high-quality factors while preventing stagnation on suboptimal paths.

Can I use this approach for non-educational cryptanalysis or production factorization?▼

This Skill targets educational demonstrations of swarm intelligence applied to factoring. For production cryptanalysis, consult domain-specific tools and security best practices; ACO is suited for learning, not replacing established factorization algorithms at scale.