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Goldenseed

Deterministic entropy streams for reproducible testing and procedural generation.

Rating: 3.9 (252 reviews)
Downloads: 4,255 downloads
Version: 1.0.0

Overview

Deterministic entropy streams for reproducible testing and procedural generation.

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GoldenSeed - Deterministic Entropy for Agents

Reproducible randomness when you need identical results every time.

What This Does

GoldenSeed generates infinite deterministic byte streams from tiny fixed seeds. Same seed → same output, always. Perfect for:

✅ Testing reproducibility: Debug flaky tests by replaying exact random sequences
✅ Procedural generation: Create verifiable game worlds, art, music from seeds
✅ Scientific simulations: Reproducible Monte Carlo, physics engines
✅ Statistical testing: Perfect 50/50 coin flip distribution (provably fair)
✅ Hash verification: Prove output came from declared seed

What This Doesn't Do

⚠️ NOT cryptographically secure - Don't use for passwords, keys, or security tokens. Use os.urandom() or secrets module for crypto.

Quick Start

Installation

bash

pip install golden-seed

Basic Usage

python

from gq import UniversalQKD

# Create generator with default seed
gen = UniversalQKD()

# Generate 16-byte chunks
chunk1 = next(gen)
chunk2 = next(gen)

# Same seed = same sequence (reproducibility!)
gen1 = UniversalQKD()
gen2 = UniversalQKD()
assert next(gen1) == next(gen2)  # Always identical

Statistical Quality - Perfect 50/50 Coin Flip

python

from gq import UniversalQKD

def coin_flip_test(n=1_000_000):
    """Demonstrate perfect 50/50 distribution"""
    gen = UniversalQKD()
    heads = 0
    
    for _ in range(n):
        byte = next(gen)[0]  # Get first byte
        if byte & 1:  # Check LSB
            heads += 1
    
    ratio = heads / n
    print(f"Heads: {ratio:.6f} (expected: 0.500000)")
    return abs(ratio - 0.5) < 0.001  # Within 0.1%

assert coin_flip_test()  # ✓ Passes every time

Reproducible Testing

python

from gq import UniversalQKD

class TestDataGenerator:
    def __init__(self, seed=0):
        self.gen = UniversalQKD()
        # Skip to seed position
        for _ in range(seed):
            next(self.gen)
    
    def random_user(self):
        data = next(self.gen)
        return {
            'id': int.from_bytes(data[0:4], 'big'),
            'age': 18 + (data[4] % 50),
            'premium': bool(data[5] & 1)
        }

# Same seed = same test data every time
def test_user_pipeline():
    users = TestDataGenerator(seed=42)
    user1 = users.random_user()
    
    # Run again - identical results!
    users2 = TestDataGenerator(seed=42)
    user1_again = users2.random_user()
    
    assert user1 == user1_again  # ✓ Reproducible!

Procedural World Generation

python

from gq import UniversalQKD

class WorldGenerator:
    def __init__(self, world_seed=0):
        self.gen = UniversalQKD()
        for _ in range(world_seed):
            next(self.gen)
    
    def chunk(self, x, z):
        """Generate deterministic chunk at coordinates"""
        data = next(self.gen)
        return {
            'biome': data[0] % 10,
            'elevation': int.from_bytes(data[1:3], 'big') % 256,
            'vegetation': data[3] % 100,
            'seed_hash': data.hex()[:16]  # For verification
        }

# Generate infinite world from single seed
world = WorldGenerator(world_seed=12345)
chunk = world.chunk(0, 0)
print(f"Biome: {chunk['biome']}, Elevation: {chunk['elevation']}")
print(f"Verifiable hash: {chunk['seed_hash']}")

Hash Verification

python

from gq import UniversalQKD
import hashlib

def generate_with_proof(seed=0, n_chunks=1000):
    """Generate data with hash proof"""
    gen = UniversalQKD()
    for _ in range(seed):
        next(gen)
    
    chunks = [next(gen) for _ in range(n_chunks)]
    data = b''.join(chunks)
    proof = hashlib.sha256(data).hexdigest()
    
    return data, proof

# Anyone with same seed can verify
data1, proof1 = generate_with_proof(seed=42, n_chunks=100)
data2, proof2 = generate_with_proof(seed=42, n_chunks=100)

assert data1 == data2      # ✓ Same output
assert proof1 == proof2    # ✓ Same hash

Agent Use Cases

Debugging Flaky Tests

When your tests pass sometimes and fail sometimes, replace random values with GoldenSeed to reproduce exact scenarios:

python

# Instead of:
import random
value = random.randint(1, 100)  # Different every time

# Use:
from gq import UniversalQKD
gen = UniversalQKD()
value = next(gen)[0] % 100 + 1  # Same value for same seed

Procedural Art Generation

Generate art, music, or NFTs with verifiable seeds:

python

def generate_art(seed):
    gen = UniversalQKD()
    for _ in range(seed):
        next(gen)
    
    # Generate deterministic art parameters
    palette = [next(gen)[i % 16] for i in range(10)]
    composition = next(gen)
    
    return create_artwork(palette, composition)

# Seed 42 always produces the same artwork
art = generate_art(seed=42)

Competitive Game Fairness

Prove game outcomes were fair by sharing the seed:

python

class FairDice:
    def __init__(self, game_seed):
        self.gen = UniversalQKD()
        for _ in range(game_seed):
            next(self.gen)
    
    def roll(self):
        return (next(self.gen)[0] % 6) + 1

# Players can verify rolls by running same seed
dice = FairDice(game_seed=99999)
rolls = [dice.roll() for _ in range(100)]
# Share seed 99999 - anyone can verify identical sequence

References

GitHub: https://github.com/COINjecture-Network/seed
PyPI: https://pypi.org/project/golden-seed/
Examples: See examples/ directory in repository
Statistical Tests: See docs/ENTROPY_ANALYSIS.md

Multi-Language Support

Identical output across platforms:

Python (this skill)
JavaScript (examples/binary_fusion_tap.js)
C, C++, Go, Rust, Java (see repository)

License

GPL-3.0+ with restrictions on military applications.

See LICENSE in repository for details.

Remember: GoldenSeed is for reproducibility, not security. When debugging fails, need identical test data, or generating verifiable procedural content, GoldenSeed gives you determinism with statistical quality. For crypto, use secrets module.

Installation

Terminal bash


openclaw install goldenseed

Copied!

💻Code Examples

assert proof1 == proof2 # ✓ Same hash

assert-proof1--proof2---same-hash.txt

## Agent Use Cases

### Debugging Flaky Tests

When your tests pass sometimes and fail sometimes, replace random values with GoldenSeed to reproduce exact scenarios:

value = next(gen)[0] % 100 + 1 # Same value for same seed

value--nextgen0--100--1--same-value-for-same-seed.txt

### Procedural Art Generation

Generate art, music, or NFTs with verifiable seeds:

art = generate_art(seed=42)

art--generateartseed42.txt

### Competitive Game Fairness

Prove game outcomes were fair by sharing the seed:

example.py

from gq import UniversalQKD

# Create generator with default seed
gen = UniversalQKD()

# Generate 16-byte chunks
chunk1 = next(gen)
chunk2 = next(gen)

# Same seed = same sequence (reproducibility!)
gen1 = UniversalQKD()
gen2 = UniversalQKD()
assert next(gen1) == next(gen2)  # Always identical

example.py

from gq import UniversalQKD

def coin_flip_test(n=1_000_000):
    """Demonstrate perfect 50/50 distribution"""
    gen = UniversalQKD()
    heads = 0
    
    for _ in range(n):
        byte = next(gen)[0]  # Get first byte
        if byte & 1:  # Check LSB
            heads += 1
    
    ratio = heads / n
    print(f"Heads: {ratio:.6f} (expected: 0.500000)")
    return abs(ratio - 0.5) < 0.001  # Within 0.1%

assert coin_flip_test()  # ✓ Passes every time

example.py

from gq import UniversalQKD

class TestDataGenerator:
    def __init__(self, seed=0):
        self.gen = UniversalQKD()
        # Skip to seed position
        for _ in range(seed):
            next(self.gen)
    
    def random_user(self):
        data = next(self.gen)
        return {
            'id': int.from_bytes(data[0:4], 'big'),
            'age': 18 + (data[4] % 50),
            'premium': bool(data[5] & 1)
        }

# Same seed = same test data every time
def test_user_pipeline():
    users = TestDataGenerator(seed=42)
    user1 = users.random_user()
    
    # Run again - identical results!
    users2 = TestDataGenerator(seed=42)
    user1_again = users2.random_user()
    
    assert user1 == user1_again  # ✓ Reproducible!

example.py

from gq import UniversalQKD

class WorldGenerator:
    def __init__(self, world_seed=0):
        self.gen = UniversalQKD()
        for _ in range(world_seed):
            next(self.gen)
    
    def chunk(self, x, z):
        """Generate deterministic chunk at coordinates"""
        data = next(self.gen)
        return {
            'biome': data[0] % 10,
            'elevation': int.from_bytes(data[1:3], 'big') % 256,
            'vegetation': data[3] % 100,
            'seed_hash': data.hex()[:16]  # For verification
        }

# Generate infinite world from single seed
world = WorldGenerator(world_seed=12345)
chunk = world.chunk(0, 0)
print(f"Biome: {chunk['biome']}, Elevation: {chunk['elevation']}")
print(f"Verifiable hash: {chunk['seed_hash']}")

example.py

from gq import UniversalQKD
import hashlib

def generate_with_proof(seed=0, n_chunks=1000):
    """Generate data with hash proof"""
    gen = UniversalQKD()
    for _ in range(seed):
        next(gen)
    
    chunks = [next(gen) for _ in range(n_chunks)]
    data = b''.join(chunks)
    proof = hashlib.sha256(data).hexdigest()
    
    return data, proof

# Anyone with same seed can verify
data1, proof1 = generate_with_proof(seed=42, n_chunks=100)
data2, proof2 = generate_with_proof(seed=42, n_chunks=100)

assert data1 == data2      # ✓ Same output
assert proof1 == proof2    # ✓ Same hash

example.py

# Instead of:
import random
value = random.randint(1, 100)  # Different every time

# Use:
from gq import UniversalQKD
gen = UniversalQKD()
value = next(gen)[0] % 100 + 1  # Same value for same seed

example.py

def generate_art(seed):
    gen = UniversalQKD()
    for _ in range(seed):
        next(gen)
    
    # Generate deterministic art parameters
    palette = [next(gen)[i % 16] for i in range(10)]
    composition = next(gen)
    
    return create_artwork(palette, composition)

# Seed 42 always produces the same artwork
art = generate_art(seed=42)