Skill: beanapologist/goldenseed (version 1.1.0)

--- name: goldenseed description: Deterministic entropy streams for reproducible testing and procedural generation. Perfect 50/50 statistical distribution with hash verification. Not cryptographically secure - use for testing, worldgen, and scenarios where reproducibility matters more than unpredictability. tags: [testing, procedural-generation, deterministic, reproducibility, golden-ratio] version: 1.0.0 author: beanapologist license: GPL-3.0+ --- # 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.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)