GPU/CPU Rule Engineering Toolkit

Hashcat Rules Suite

A complete suite of specialized tools for Hashcat rule processing — deduplication, ranking, generation, benchmarking, and extraction.

OpenCL GPU Multi-Core CPU NumPy Optimized Python Engine Memory-Mapped I/O Interactive Mode MAB Algorithm Signature Dedup Efficiency Analytics

9Specialized Tools

100K+Rules Supported

50×Loading Speed

GPUAccelerated

Complete Engineering Workflow

From intelligent deduplication and statistical analysis to GPU-accelerated ranking with memory-mapped loading, performance benchmarking, and automated rule extraction.

Minimizer Concentrator Ranker Rulest Cleaner Aether Email Extractor Efficiency

Tool Suite Overview

MinimizerCPU-only

Signature-based deduplication removing functionally identical rules. Built-in 30-word probe set — no wordlist required.

Pure Python, no GPU needed
Preserves original file order
Customizable probe sets
tqdm progress + statistics

View docs

ConcentratorGPU

GPU-accelerated rule processor and generator using OpenCL, Markov models, and combinatorial math with interactive mode.

OpenCL GPU acceleration
Markov chain generation
Interactive session mode
Pareto Curve Analysis

View docs

Ranker v5GPU

GPU-accelerated rule ranking using Multi-Armed Bandit with Early Elimination. Memory-mapped loading for 50× speed boost.

MAB + Early Elimination
Memory-mapped file loading
16,000+ rules supported
NumPy-optimized scoring

View docs

RulestGPU

Advanced rule set generation with statistical analysis, frequency ranking, and automated pipeline creation for high-quality rulesets.

Statistical rule analysis
Frequency-based ranking
Automated pipeline
Leetspeak rule support

View docs

Rule CleanerBrowser

Remove invalid rules from Hashcat logs containing startup errors. Client-side processing with auto-cleanup, no uploads needed.

100% client-side processing
100MB file support
Simulate before applying
Auto memory cleanup

Open tool

Email ExtractorBrowser

Generate Hashcat rules from email list patterns — trailing digit extraction, username analysis, and domain filtering.

Dual format support
Pattern recognition
Domain filtering
Hashcat rule export

Open tool

AetherGPU

GPU-accelerated rule performance benchmarking with radar charts, heatmaps, and statistical summaries. 50 built-in test words.

OpenCL GPU acceleration
Radar + heatmap charts
Config confirmation UX
Sorted export + reports

View docs

OpenCL KernelGPU

Complete Hashcat rules implementation for GPU processing. All major rule categories, production-ready kernel code.

All Hashcat rules implemented
GPU-optimized parallel code
Ready-to-use kernel
Full documentation

View kernel

Efficiency AnalyticsInteractive Charts

Interactive data visualization of ruleset efficiency — scatter plots, tier leaders, and strategic recommendations based on real benchmark data.

Efficiency metric: (%Recovered ÷ Rules) ×1000
Golden Zone identification
Balanced picks for daily use
Speedrun / Overnight strategies

Explore Analytics

Minimizer

Concentrator

Ranker

Rulest

Cleaner

Email Extractor

Aether

OpenCL

Efficiency

Minimizer Pure Python · CPU-only

Core Features

∑

Signature-based dedup

Computes output tuple across all probes. Rules with identical signatures are functionally equivalent and removed.

⌗

Built-in 30-word probe set

Covers short/long words, mixed-case, digits, specials, repeated chars. No external wordlist needed.

Extensible probes

Add words via --extra-probes or sample from a wordlist with --probe-file.

Processing Workflow

Step 1

Assemble probe set

Built-in words + extra-probes + sampled from probe-file, deduplicated.

Step 2

Apply rules

Pure-Python interpreter applies each rule to every probe word.

Step 3

Compute signatures

Collect all transformed outputs into a tuple per rule.

Step 4

Deduplicate

Dict maps signature → first rule seen. Duplicates discarded, order preserved.

Concentrator v3.0 GPU · OpenCL

Core Features

⚡

OpenCL GPU acceleration

Massively parallel rule processing for fast generation and validation.

Markov chain modeling

Statistical n-gram analysis for high-probability rule synthesis.

⤳

Interactive mode

Live session for iterative rule exploration and real-time feedback.

∏

Combinatorial math

Pareto Curve Analysis

Processing Pipeline

Step 1

Load wordlist & rules

Memory-mapped loading for maximum throughput.

Step 2

Markov analysis

Build n-gram model of character transitions.

Step 3

GPU rule generation

OpenCL kernel applies and validates candidate rules in parallel.

Step 4

Score & filter

Rank by hit rate, filter by threshold, export ranked ruleset.

Ranker v5.0 GPU · MAB

Core Features

∞

Multi-Armed Bandit ranking

MAB with Early Elimination allocates compute to top performers, converging fast.

⚡

Memory-mapped loading

50× faster rule file loading via OS-level mmap. Handles 16,000+ rules.

↑

NumPy-optimized scoring

Vectorized hit counting and confidence interval computation.

Ranking Workflow

Step 1

mmap rule files

Load thousands of rules into virtual memory instantly.

Step 2

GPU batch test

OpenCL evaluates each rule against hash corpus in parallel.

Step 3

MAB exploration

Bandit allocates more trials to high-confidence rules.

Step 4

Ranked export

Output sorted by hit rate. Compatible with all Hashcat modes.

Rulest GPU · Statistical

Core Features

∫

Statistical analysis

Character frequency, bigram, and transformation analysis across corpora.

↑

Frequency-based ranking

Rules ordered by coverage probability from real-world data.

⚙

Automated pipeline

End-to-end from raw wordlist to optimized, ready-to-use ruleset.

Generation Pipeline

Step 1

Corpus analysis

Extract statistical patterns from training wordlist.

Step 2

Rule candidate synthesis

Generate candidates from patterns with leetspeak and chaining.

Step 3

GPU scoring

Evaluate candidates against held-out corpus via OpenCL.

Step 4

Export ranked rules

Produce final .rule file sorted by coverage.

Rule Cleaner Browser · Client-side

Core Features

🔒

Client-side processing

All parsing happens in your browser — no server uploads, full privacy.

↑

100MB file support

Handles large log and rule files efficiently in-browser memory.

✓

Simulate mode

Preview which rules would be removed before downloading cleaned file.

Cleaning Workflow

Step 1

Upload files

Upload Hashcat log file + the rule file to clean (100MB max each).

Step 2

Parse log

Extract invalid line numbers from startup error messages.

Step 3

Remove invalid rules

Filter rule file, keeping only lines not flagged as invalid.

Step 4

Download & auto-clean

Cleaned file downloads, memory freed automatically.

Email Rule Extractor Browser · Client-side

Core Features

Dual format support

Handles username+digits-tag@domain and standard email formats.

≡

Pattern recognition

Identifies common transformations (digit suffixes, case patterns, leet).

⊘

Domain filtering

Focus on specific domains or exclude unwanted ones from analysis.

Extraction Workflow

Step 1

Parse email list

Read addresses and split into username + domain components.

Step 2

Analyze patterns

Detect recurring digit suffixes, separators, and case patterns.

Step 3

Generate rules

Produce Hashcat rule syntax for each detected transformation.

Step 4

Export rules

Download .rule file ready for Hashcat.

Aether GPU · Benchmark

Core Features

⚡

OpenCL GPU acceleration

Parallel rule performance testing with multi-iteration averaging.

◈

Advanced visualizations

Radar charts, heatmaps, and statistical summaries for analysis.

✓

Config confirmation

Interactive setup with validation before execution begins.

⌥

50 built-in test words

Curated set covering all rule categories — no external dict needed.

Benchmarking Pipeline

Step 1

Load & configure

Load rule files, display config summary for confirmation.

Step 2

Init OpenCL context

Compile GPU kernel targeting your device.

Step 3

Multi-iteration testing

Each rule tested multiple times for statistical robustness.

Step 4

Report & export

Generate visualizations and export sorted rules + reports.

OpenCL Kernel GPU · C/OpenCL

Core Features

≡

Complete rule implementation

All Hashcat rules implemented for GPU parallel processing.

⚡

GPU optimization

Kernel code written for coalesced memory access and warp efficiency.

⊞

Production-ready

Used by Aether and Ranker — battle-tested in real cracking sessions.

Supported Rule Categories

Case modification rules

Capitalize, lowercase, uppercase, toggle, reverse-case.

Substitution & replacement

Character swap, overstrike, purge by value.

Prefix & suffix operations

Prepend, append, multi-char padding.

Position & memory ops

Extract, omit, rotate, memorize, restore.

Ruleset Efficiency Analytics Interactive Charts

Core Metrics

∑

Efficiency metric

(%Recovered ÷ Rules Count) × 1000 — find the best return per rule.

◈

Golden Zone identification

Scatter plot showing efficiency vs. rules count on log scale.

⌗

Tier leaders

Best recovery % in small, medium, large, and ultra categories.

Strategic Recommendations

⚡ Speedrun / CTF

Robot-Best10 (136k eff), conc_MT_250 (40k eff)

🛠️ Daily Workflow

HashMob.20k (31.08% rec), conc_MT_25000 (31.82% rec)

🌑 Overnight / Long

misty.rule (45.22% rec), Fordyv4a (56.94% rec)

📊 Full interactive dashboard

View scatter plots, bar charts, and balanced pick tables.

Open Full Analytics

About This Project

This entire project — rulest, concentrator, and all the tooling around it — was built purely as a hobby and learning exercise. There was no grand plan, just curiosity about whether frequency analysis on debug output could produce better rules than manual curation or purely random generation. Turns out: yes, noticeably so.

If you find the tools or the methodology useful, great. If you have ideas for improvement, the repos are open. And if you just enjoy poking at this kind of thing for the same reason — have fun.

JavaScript Required

Hashcat Rules Suite

Tool Suite Overview