{ "schemaVersion": "1.0", "item": { "slug": "goldenseed", "name": "GoldenSeed", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/beanapologist/goldenseed", "canonicalUrl": "https://clawhub.ai/beanapologist/goldenseed", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/goldenseed", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=goldenseed", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "README.md", "SKILL.md", "install.sh" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-30T16:55:25.780Z", "expiresAt": "2026-05-07T16:55:25.780Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=network", "contentDisposition": "attachment; filename=\"network-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/goldenseed" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/goldenseed", "agentPageUrl": "https://openagent3.xyz/skills/goldenseed/agent", "manifestUrl": "https://openagent3.xyz/skills/goldenseed/agent.json", "briefUrl": "https://openagent3.xyz/skills/goldenseed/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "GoldenSeed - Deterministic Entropy for Agents", "body": "Reproducible randomness when you need identical results every time." }, { "title": "What This Does", "body": "GoldenSeed generates infinite deterministic byte streams from tiny fixed seeds. Same seed → same output, always. Perfect for:\n\n✅ Testing reproducibility: Debug flaky tests by replaying exact random sequences\n✅ Procedural generation: Create verifiable game worlds, art, music from seeds\n✅ Scientific simulations: Reproducible Monte Carlo, physics engines\n✅ Statistical testing: Perfect 50/50 coin flip distribution (provably fair)\n✅ Hash verification: Prove output came from declared seed" }, { "title": "What This Doesn't Do", "body": "⚠️ NOT cryptographically secure - Don't use for passwords, keys, or security tokens. Use os.urandom() or secrets module for crypto." }, { "title": "Installation", "body": "pip install golden-seed" }, { "title": "Basic Usage", "body": "from gq import UniversalQKD\n\n# Create generator with default seed\ngen = UniversalQKD()\n\n# Generate 16-byte chunks\nchunk1 = next(gen)\nchunk2 = next(gen)\n\n# Same seed = same sequence (reproducibility!)\ngen1 = UniversalQKD()\ngen2 = UniversalQKD()\nassert next(gen1) == next(gen2) # Always identical" }, { "title": "Statistical Quality - Perfect 50/50 Coin Flip", "body": "from gq import UniversalQKD\n\ndef coin_flip_test(n=1_000_000):\n \"\"\"Demonstrate perfect 50/50 distribution\"\"\"\n gen = UniversalQKD()\n heads = 0\n \n for _ in range(n):\n byte = next(gen)[0] # Get first byte\n if byte & 1: # Check LSB\n heads += 1\n \n ratio = heads / n\n print(f\"Heads: {ratio:.6f} (expected: 0.500000)\")\n return abs(ratio - 0.5) < 0.001 # Within 0.1%\n\nassert coin_flip_test() # ✓ Passes every time" }, { "title": "Reproducible Testing", "body": "from gq import UniversalQKD\n\nclass TestDataGenerator:\n def __init__(self, seed=0):\n self.gen = UniversalQKD()\n # Skip to seed position\n for _ in range(seed):\n next(self.gen)\n \n def random_user(self):\n data = next(self.gen)\n return {\n 'id': int.from_bytes(data[0:4], 'big'),\n 'age': 18 + (data[4] % 50),\n 'premium': bool(data[5] & 1)\n }\n\n# Same seed = same test data every time\ndef test_user_pipeline():\n users = TestDataGenerator(seed=42)\n user1 = users.random_user()\n \n # Run again - identical results!\n users2 = TestDataGenerator(seed=42)\n user1_again = users2.random_user()\n \n assert user1 == user1_again # ✓ Reproducible!" }, { "title": "Procedural World Generation", "body": "from gq import UniversalQKD\n\nclass WorldGenerator:\n def __init__(self, world_seed=0):\n self.gen = UniversalQKD()\n for _ in range(world_seed):\n next(self.gen)\n \n def chunk(self, x, z):\n \"\"\"Generate deterministic chunk at coordinates\"\"\"\n data = next(self.gen)\n return {\n 'biome': data[0] % 10,\n 'elevation': int.from_bytes(data[1:3], 'big') % 256,\n 'vegetation': data[3] % 100,\n 'seed_hash': data.hex()[:16] # For verification\n }\n\n# Generate infinite world from single seed\nworld = WorldGenerator(world_seed=12345)\nchunk = world.chunk(0, 0)\nprint(f\"Biome: {chunk['biome']}, Elevation: {chunk['elevation']}\")\nprint(f\"Verifiable hash: {chunk['seed_hash']}\")" }, { "title": "Hash Verification", "body": "from gq import UniversalQKD\nimport hashlib\n\ndef generate_with_proof(seed=0, n_chunks=1000):\n \"\"\"Generate data with hash proof\"\"\"\n gen = UniversalQKD()\n for _ in range(seed):\n next(gen)\n \n chunks = [next(gen) for _ in range(n_chunks)]\n data = b''.join(chunks)\n proof = hashlib.sha256(data).hexdigest()\n \n return data, proof\n\n# Anyone with same seed can verify\ndata1, proof1 = generate_with_proof(seed=42, n_chunks=100)\ndata2, proof2 = generate_with_proof(seed=42, n_chunks=100)\n\nassert data1 == data2 # ✓ Same output\nassert proof1 == proof2 # ✓ Same hash" }, { "title": "Debugging Flaky Tests", "body": "When your tests pass sometimes and fail sometimes, replace random values with GoldenSeed to reproduce exact scenarios:\n\n# Instead of:\nimport random\nvalue = random.randint(1, 100) # Different every time\n\n# Use:\nfrom gq import UniversalQKD\ngen = UniversalQKD()\nvalue = next(gen)[0] % 100 + 1 # Same value for same seed" }, { "title": "Procedural Art Generation", "body": "Generate art, music, or NFTs with verifiable seeds:\n\ndef generate_art(seed):\n gen = UniversalQKD()\n for _ in range(seed):\n next(gen)\n \n # Generate deterministic art parameters\n palette = [next(gen)[i % 16] for i in range(10)]\n composition = next(gen)\n \n return create_artwork(palette, composition)\n\n# Seed 42 always produces the same artwork\nart = generate_art(seed=42)" }, { "title": "Competitive Game Fairness", "body": "Prove game outcomes were fair by sharing the seed:\n\nclass FairDice:\n def __init__(self, game_seed):\n self.gen = UniversalQKD()\n for _ in range(game_seed):\n next(self.gen)\n \n def roll(self):\n return (next(self.gen)[0] % 6) + 1\n\n# Players can verify rolls by running same seed\ndice = FairDice(game_seed=99999)\nrolls = [dice.roll() for _ in range(100)]\n# Share seed 99999 - anyone can verify identical sequence" }, { "title": "References", "body": "GitHub: https://github.com/COINjecture-Network/seed\nPyPI: https://pypi.org/project/golden-seed/\nExamples: See examples/ directory in repository\nStatistical Tests: See docs/ENTROPY_ANALYSIS.md" }, { "title": "Multi-Language Support", "body": "Identical output across platforms:\n\nPython (this skill)\nJavaScript (examples/binary_fusion_tap.js)\nC, C++, Go, Rust, Java (see repository)" }, { "title": "License", "body": "GPL-3.0+ with restrictions on military applications.\n\nSee LICENSE in repository for details.\n\nRemember: 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." } ], "body": "GoldenSeed - Deterministic Entropy for Agents\n\nReproducible randomness when you need identical results every time.\n\nWhat This Does\n\nGoldenSeed generates infinite deterministic byte streams from tiny fixed seeds. Same seed → same output, always. Perfect for:\n\n✅ Testing reproducibility: Debug flaky tests by replaying exact random sequences\n✅ Procedural generation: Create verifiable game worlds, art, music from seeds\n✅ Scientific simulations: Reproducible Monte Carlo, physics engines\n✅ Statistical testing: Perfect 50/50 coin flip distribution (provably fair)\n✅ Hash verification: Prove output came from declared seed\nWhat This Doesn't Do\n\n⚠️ NOT cryptographically secure - Don't use for passwords, keys, or security tokens. Use os.urandom() or secrets module for crypto.\n\nQuick Start\nInstallation\npip install golden-seed\n\nBasic Usage\nfrom gq import UniversalQKD\n\n# Create generator with default seed\ngen = UniversalQKD()\n\n# Generate 16-byte chunks\nchunk1 = next(gen)\nchunk2 = next(gen)\n\n# Same seed = same sequence (reproducibility!)\ngen1 = UniversalQKD()\ngen2 = UniversalQKD()\nassert next(gen1) == next(gen2) # Always identical\n\nStatistical Quality - Perfect 50/50 Coin Flip\nfrom gq import UniversalQKD\n\ndef coin_flip_test(n=1_000_000):\n \"\"\"Demonstrate perfect 50/50 distribution\"\"\"\n gen = UniversalQKD()\n heads = 0\n \n for _ in range(n):\n byte = next(gen)[0] # Get first byte\n if byte & 1: # Check LSB\n heads += 1\n \n ratio = heads / n\n print(f\"Heads: {ratio:.6f} (expected: 0.500000)\")\n return abs(ratio - 0.5) < 0.001 # Within 0.1%\n\nassert coin_flip_test() # ✓ Passes every time\n\nReproducible Testing\nfrom gq import UniversalQKD\n\nclass TestDataGenerator:\n def __init__(self, seed=0):\n self.gen = UniversalQKD()\n # Skip to seed position\n for _ in range(seed):\n next(self.gen)\n \n def random_user(self):\n data = next(self.gen)\n return {\n 'id': int.from_bytes(data[0:4], 'big'),\n 'age': 18 + (data[4] % 50),\n 'premium': bool(data[5] & 1)\n }\n\n# Same seed = same test data every time\ndef test_user_pipeline():\n users = TestDataGenerator(seed=42)\n user1 = users.random_user()\n \n # Run again - identical results!\n users2 = TestDataGenerator(seed=42)\n user1_again = users2.random_user()\n \n assert user1 == user1_again # ✓ Reproducible!\n\nProcedural World Generation\nfrom gq import UniversalQKD\n\nclass WorldGenerator:\n def __init__(self, world_seed=0):\n self.gen = UniversalQKD()\n for _ in range(world_seed):\n next(self.gen)\n \n def chunk(self, x, z):\n \"\"\"Generate deterministic chunk at coordinates\"\"\"\n data = next(self.gen)\n return {\n 'biome': data[0] % 10,\n 'elevation': int.from_bytes(data[1:3], 'big') % 256,\n 'vegetation': data[3] % 100,\n 'seed_hash': data.hex()[:16] # For verification\n }\n\n# Generate infinite world from single seed\nworld = WorldGenerator(world_seed=12345)\nchunk = world.chunk(0, 0)\nprint(f\"Biome: {chunk['biome']}, Elevation: {chunk['elevation']}\")\nprint(f\"Verifiable hash: {chunk['seed_hash']}\")\n\nHash Verification\nfrom gq import UniversalQKD\nimport hashlib\n\ndef generate_with_proof(seed=0, n_chunks=1000):\n \"\"\"Generate data with hash proof\"\"\"\n gen = UniversalQKD()\n for _ in range(seed):\n next(gen)\n \n chunks = [next(gen) for _ in range(n_chunks)]\n data = b''.join(chunks)\n proof = hashlib.sha256(data).hexdigest()\n \n return data, proof\n\n# Anyone with same seed can verify\ndata1, proof1 = generate_with_proof(seed=42, n_chunks=100)\ndata2, proof2 = generate_with_proof(seed=42, n_chunks=100)\n\nassert data1 == data2 # ✓ Same output\nassert proof1 == proof2 # ✓ Same hash\n\nAgent Use Cases\nDebugging Flaky Tests\n\nWhen your tests pass sometimes and fail sometimes, replace random values with GoldenSeed to reproduce exact scenarios:\n\n# Instead of:\nimport random\nvalue = random.randint(1, 100) # Different every time\n\n# Use:\nfrom gq import UniversalQKD\ngen = UniversalQKD()\nvalue = next(gen)[0] % 100 + 1 # Same value for same seed\n\nProcedural Art Generation\n\nGenerate art, music, or NFTs with verifiable seeds:\n\ndef generate_art(seed):\n gen = UniversalQKD()\n for _ in range(seed):\n next(gen)\n \n # Generate deterministic art parameters\n palette = [next(gen)[i % 16] for i in range(10)]\n composition = next(gen)\n \n return create_artwork(palette, composition)\n\n# Seed 42 always produces the same artwork\nart = generate_art(seed=42)\n\nCompetitive Game Fairness\n\nProve game outcomes were fair by sharing the seed:\n\nclass FairDice:\n def __init__(self, game_seed):\n self.gen = UniversalQKD()\n for _ in range(game_seed):\n next(self.gen)\n \n def roll(self):\n return (next(self.gen)[0] % 6) + 1\n\n# Players can verify rolls by running same seed\ndice = FairDice(game_seed=99999)\nrolls = [dice.roll() for _ in range(100)]\n# Share seed 99999 - anyone can verify identical sequence\n\nReferences\nGitHub: https://github.com/COINjecture-Network/seed\nPyPI: https://pypi.org/project/golden-seed/\nExamples: See examples/ directory in repository\nStatistical Tests: See docs/ENTROPY_ANALYSIS.md\nMulti-Language Support\n\nIdentical output across platforms:\n\nPython (this skill)\nJavaScript (examples/binary_fusion_tap.js)\nC, C++, Go, Rust, Java (see repository)\nLicense\n\nGPL-3.0+ with restrictions on military applications.\n\nSee LICENSE in repository for details.\n\nRemember: 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." }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/beanapologist/goldenseed", "publisherUrl": "https://clawhub.ai/beanapologist/goldenseed", "owner": "beanapologist", "version": "1.1.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/goldenseed", "downloadUrl": "https://openagent3.xyz/downloads/goldenseed", "agentUrl": "https://openagent3.xyz/skills/goldenseed/agent", "manifestUrl": "https://openagent3.xyz/skills/goldenseed/agent.json", "briefUrl": "https://openagent3.xyz/skills/goldenseed/agent.md" } }