prophetdiceskills 1.0.5

A library converting RNG cracking algorithms into callable functions with bot emulation.

🔮 Prophet Dice Skills

Prophet Dice Skills is an advanced, cryptographically-aware, Machine Learning-enhanced framework designed for analyzing, simulating, and exploiting Provably Fair RNG systems (specifically modeled after the popular HMAC-SHA256 implementations used in online casinos like Stake).

It goes beyond standard statistics by treating RNG sequences as time-series datasets, deploying deep learning, real-time streaming AI, anomaly detection, and massively parallel GPU seed cracking algorithms.

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🚀 Key Features

🧠 Groundbreaking AI Predictor Ensemble

• Time-Series Transformer (PyTorch): Deciphers micro-patterns using Multi-Head Attention to forecast non-linear deterministic RNG flaws.
• True Online Learning (river): Adaptive Random Forests and Hoeffding Trees stream process results roll-by-roll to adapt instantly without batch retraining.
• Meta-Learning Evolutionary Optimizer: Self-evolves and genetically mutates your prediction ensemble weights based on live accuracy, storing the "fittest" models automatically.
• Signal Processing Pipelines: Employs rigorous Kalman Filtering bounds and Hurst Exponent regime detection to filter noise and identify chaotic/trending moments.

🛡️ Real-Time Integrity & Anomaly Detection

• RNG Anomaly Detector: Deploys an Isolation Forest across a rolling 100-roll window. It continuously monitors the cryptographic fingerprint of your session and fires an ANOMALY breaker if the casino's RNG server silently alters behavior, aggressively slashing betting confidence.

💰 "Anti-Human" Advanced Betting System

• Burst Strategy Engine: Configurable, multi-tiered (Conservative, Balanced, Aggressive) capital allocator.
• Circuit Breakers: Automatically detects variance clusters and cuts off trading until system conditions normalize.
• Config Manager: Stores bankroll parameters effortlessly on disk (auditor_config.json).

⚡ CUDA-Accelerated Cryptanalysis

• Massive Time-Crack Sweeps: Given a target timestamp and observed nonces, uses NVIDIA GPUs via numba.cuda to brute-force solve the HMAC-SHA256 Server Seed space in milliseconds.
• Collision Farming Matrix: Analyzes deterministic hashing structures to reverse-engineer sequential Nonce targets.

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📦 Installation

pip install prophetdiceskills

Note: For Deep Learning and GPU Cracking, ensure you have an NVIDIA GPU, CUDA Toolkit installed, and PyTorch dynamically linked (pip install torch).

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📖 Quickstart Guide

1. Basic Machine Learning Prediction

from prophetdiceskills import GroundbreakingPredictor

# Initialize the Ensemble AI Predictor
predictor = GroundbreakingPredictor()

# Inject some actual history from your casino session...
history_rolls = [45.1, 99.2, 12.5, 66.8, 50.1]
for i, roll in enumerate(history_rolls):
    predictor.add(roll, identifier="my_seed", nonce=i+1)

# Request a prediction for the immediate next roll
pred = predictor.predict(current_identifier="my_seed", current_nonce=6)

if pred:
    print(f"Target: {pred['prediction']:.2f}")
    print(f"Confidence: {pred['confidence']:.2f}%")
    print(f"RNG Integrity: {pred['rng_integrity']}")

2. Auto-Updating Dashboard / Benchmarking

Test the library entirely offline without risking funds by importing the benchmark.py testing suite:

# This will spawn a dashboard via Matplotlib predicting 5,000 mathematically valid offline rolls.
python -m prophetdiceskills.benchmark --rolls 5000 --plot 

3. GPU Server Seed Brute Forcing

Requires an active NVIDIA CUDA grid. Attempt to find the casino's secret hash if you know roughly when a seed was issued and have some outcomes:

from prophetdiceskills import aggressive_time_crack

# Searches 10 seconds of unix-time space per millisecond (40 million permutations max)
seed = aggressive_time_crack(
    client_seed="my_client_seed",
    target_timestamp_ms=1600000000000,
    target_outcomes=[45.10, 99.20, 12.05],
    start_nonce=1,
    variance_sec=10 
)

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📚 Advanced Documentation

For detailed analysis of the individual components, architecture designs, and configuration tweaks, please view the /docs/ repository files.

• [docs/ml.md] - In-depth guide on the Transformer, River Integration, and Isolation Forest.
• [docs/bot.md] - Configuration flags for Risk Levels, Stop Loss, and Burst Modifiers.
• [docs/core.md] - Detailed structure of the Provably Fair SHA256 Engine and Numba Kernels.

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⚠️ Disclaimer

This library is primarily intended for educational research, penetration testing of cryptographic systems, and statistical AI analysis. Gambling mathematically guarantees a loss over infinite time due to server edges (e.g. 1% House Edge). The predictions provided by models inside this library attempt to isolate temporary anomalies in pseudo-random distributions, but cannot guarantee profit.

Use strictly at your own financial risk.