[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6J0FEVCMYDF52R87JJY4ZXK":3,"skill-same:01M6J0FEVCMYDF52R87JJY4ZXK":72},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":55,"ownerDescription":56,"updatedAt":57,"install":58,"resources":71},"01M6J0FEVCMYDF52R87JJY4ZXK","chemistry-rdkit",null,"Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.","enuno\u002Fhyperliquid-trading-firm","enuno","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6727752?v=4","01M6J09HF6Y74150F7PEMC446X","Elvis Nuno","01M6J09HKK0YMKE39JXT8JT8VM",1,"GPL-3.0","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fchemistry-rdkit\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"chemistry rdkit","rdkit","for","tasks","with","scientific","drug discovery","molecular","computational","computational chemistry","cheminformatics","chemical","domain","compound","use","discovery","analysis","search","2026-11-10 10:12:30",false,"---\nname: chemistry-rdkit\ndescription: Computational chemistry with RDKit for molecular analysis, descriptors, fingerprints, and substructure search. Use when working with SMILES, drug discovery, or cheminformatics tasks.\nmetadata:\n  category: domain\n  trigger-keywords: \"molecule,SMILES,chemical,drug,rdkit,fingerprint,molecular,compound,reaction,cheminformatics\"\n  applicable-stages: \"9,10,12\"\n  priority: \"4\"\n  version: \"1.0\"\n  author: researchclaw\n  references: \"adapted from K-Dense-AI\u002Fclaude-scientific-skills\"\n---\n\n## RDKit Cheminformatics Best Practice\n\n### Molecular I\u002FO\n1. Create molecules from SMILES: `mol = Chem.MolFromSmiles('CCO')`\n2. Always check for None: `MolFromSmiles` returns None on invalid input\n3. Convert to canonical SMILES: `Chem.MolToSmiles(mol)`\n4. Read SDF files: `suppl = Chem.SDMolSupplier('file.sdf')`\n5. Read SMILES files: `suppl = Chem.SmilesMolSupplier('file.smi')`\n6. Write molecules: `writer = Chem.SDWriter('output.sdf')`\n\n### Molecular Descriptors\n1. Molecular weight: `Descriptors.MolWt(mol)`\n2. LogP (lipophilicity): `Descriptors.MolLogP(mol)`\n3. TPSA (polar surface area): `Descriptors.TPSA(mol)`\n4. H-bond donors\u002Facceptors: `Descriptors.NumHDonors(mol)`, `Descriptors.NumHAcceptors(mol)`\n5. Rotatable bonds: `Descriptors.NumRotatableBonds(mol)`\n6. Lipinski Rule of 5: MW \u003C= 500, LogP \u003C= 5, HBD \u003C= 5, HBA \u003C= 10\n\n### Fingerprints and Similarity\n1. Morgan (circular) fingerprints: `AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)`\n2. RDKit fingerprints: `Chem.RDKFingerprint(mol)`\n3. MACCS keys: `MACCSkeys.GenMACCSKeys(mol)`\n4. Tanimoto similarity: `DataStructs.TanimotoSimilarity(fp1, fp2)`\n5. Use radius=2 (ECFP4 equivalent) as default for most applications\n6. For virtual screening, Tanimoto > 0.7 suggests structural similarity\n\n### Substructure Search\n1. SMARTS patterns: `pattern = Chem.MolFromSmarts('[OH]')`\n2. Check match: `mol.HasSubstructMatch(pattern)`\n3. Get all matches: `mol.GetSubstructMatches(pattern)`\n4. Common SMARTS: `[#6](=O)[OH]` (carboxylic acid), `[NH2]` (primary amine)\n5. Filter compound libraries by functional group presence\n\n### Property Calculation Patterns\n1. Batch processing: iterate over SDMolSupplier, skip None entries\n2. Use `Chem.Descriptors.descList` for all available descriptors\n3. For ADMET filtering, calculate Lipinski, Veber, and PAINS filters\n4. Generate 3D coordinates: `AllChem.EmbedMolecule(mol, AllChem.ETKDG())`\n5. Minimize energy: `AllChem.MMFFOptimizeMolecule(mol)`\n\n### Common Pitfalls\n1. Always sanitize molecules (default behavior) — disable only when needed\n2. Add hydrogens explicitly for 3D work: `Chem.AddHs(mol)`\n3. Handle stereochemistry: use `Chem.AssignStereochemistry(mol)`\n4. Large SDF files: use `ForwardSDMolSupplier` for memory efficiency\n5. Kekulization errors usually indicate invalid SMILES input\n","## RDKit Cheminformatics Best Practice\n\n### Molecular I\u002FO\n1. Create molecules from SMILES: `mol = Chem.MolFromSmiles('CCO')`\n2. Always check for None: `MolFromSmiles` returns None on invalid input\n3. Convert to canonical SMILES: `Chem.MolToSmiles(mol)`\n4. Read SDF files: `suppl = Chem.SDMolSupplier('file.sdf')`\n5. Read SMILES files: `suppl = Chem.SmilesMolSupplier('file.smi')`\n6. Write molecules: `writer = Chem.SDWriter('output.sdf')`\n\n### Molecular Descriptors\n1. Molecular weight: `Descriptors.MolWt(mol)`\n2. LogP (lipophilicity): `Descriptors.MolLogP(mol)`\n3. TPSA (polar surface area): `Descriptors.TPSA(mol)`\n4. H-bond donors\u002Facceptors: `Descriptors.NumHDonors(mol)`, `Descriptors.NumHAcceptors(mol)`\n5. Rotatable bonds: `Descriptors.NumRotatableBonds(mol)`\n6. Lipinski Rule of 5: MW \u003C= 500, LogP \u003C= 5, HBD \u003C= 5, HBA \u003C= 10\n\n### Fingerprints and Similarity\n1. Morgan (circular) fingerprints: `AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)`\n2. RDKit fingerprints: `Chem.RDKFingerprint(mol)`\n3. MACCS keys: `MACCSkeys.GenMACCSKeys(mol)`\n4. Tanimoto similarity: `DataStructs.TanimotoSimilarity(fp1, fp2)`\n5. Use radius=2 (ECFP4 equivalent) as default for most applications\n6. For virtual screening, Tanimoto > 0.7 suggests structural similarity\n\n### Substructure Search\n1. SMARTS patterns: `pattern = Chem.MolFromSmarts('[OH]')`\n2. Check match: `mol.HasSubstructMatch(pattern)`\n3. Get all matches: `mol.GetSubstructMatches(pattern)`\n4. Common SMARTS: `[#6](=O)[OH]` (carboxylic acid), `[NH2]` (primary amine)\n5. Filter compound libraries by functional group presence\n\n### Property Calculation Patterns\n1. Batch processing: iterate over SDMolSupplier, skip None entries\n2. Use `Chem.Descriptors.descList` for all available descriptors\n3. For ADMET filtering, calculate Lipinski, Veber, and PAINS filters\n4. Generate 3D coordinates: `AllChem.EmbedMolecule(mol, AllChem.ETKDG())`\n5. Minimize energy: `AllChem.MMFFOptimizeMolecule(mol)`\n\n### Common Pitfalls\n1. Always sanitize molecules (default behavior) — disable only when needed\n2. Add hydrogens explicitly for 3D work: `Chem.AddHs(mol)`\n3. Handle stereochemistry: use `Chem.AssignStereochemistry(mol)`\n4. Large SDF files: use `ForwardSDMolSupplier` for memory efficiency\n5. Kekulization errors usually indicate invalid SMILES input\n",{"name":5,"metadata":42,"description":7},{"author":43,"version":44,"category":31,"priority":45,"references":46,"trigger-keywords":47,"applicable-stages":48},"researchclaw","1.0","4","adapted from K-Dense-AI\u002Fclaude-scientific-skills","molecule,SMILES,chemical,drug,rdkit,fingerprint,molecular,compound,reaction,cheminformatics","9,10,12","main","cc32fdacf303e9c64064829787a5e690bb29cd47","multiclaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fchemistry-rdkit",0,"https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm","https:\u002F\u002Fgithub.com\u002Fenuno","https:\u002F\u002Fenuno.github.io\u002FDynamicCareerCanvas","Digital Nomad, Blockchain Evangel, and Digital Solutions Architect. \r\nCreator of TerraHash Stack.\r\nCEO of Ryno Crypto Mining Services.","2026-07-02 12:46:02",{"source":8,"skillName":5,"commands":59,"prompt":69,"zipAvailable":70},[60,63,66],{"tool":61,"command":62},"npx","npx skills add enuno\u002Fhyperliquid-trading-firm --skill chemistry-rdkit",{"tool":64,"command":65},"bunx","bunx skills add enuno\u002Fhyperliquid-trading-firm --skill chemistry-rdkit",{"tool":67,"command":68},"pnpm","pnpm dlx skills add enuno\u002Fhyperliquid-trading-firm --skill chemistry-rdkit","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fchemistry-rdkit\u002FSKILL.md",true,[],[73,102,129,153,177,198],{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":84,"publishedAt":37},"01M6J14V7SBT7MBPZ9VK8NRQR8","supermemory","Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static\u002Fdynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsupermemory\u002FSKILL.md",[79],{"categoryNo":80,"categoryName":81,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[75,85,86,87,88,89,36,90,91,92,93,94,95,96,97,98,99,100,21,101],"ai agents","memory","rag","semantic search","infrastructure","user context","applications","api","knowledge","context","Art","personalization","across","learned","retention","profiles","persistent memory",{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":106,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":37},"01M6J14PEXHE2QBAWM3YZ2GM63","wolverine-strategy","WOLVERINE v2.0 — HYPE alpha hunter. Entry-only scanner, DSL-exit-only architecture. v1.1 lost -22.7% because the scanner's thesis exit chopped 25\u002F27 trades before DSL could manage them. v2.0 removes thesis exit entirely. Scanner decides entries (score 8+, 4H\u002F1H aligned, SM consensus). DSL manages all exits (wide Phase 1 for HYPE volatility, trailing tiers starting at +15% ROE). Leverage lowered to 7x. Max 4 entries\u002Fday. 3-hour cooldown between entries. DSL exit managed by plugin runtime via runtime.yaml.","MIT","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwolverine\u002FSKILL.md",[],[110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,21],"strategy","wolverine strategy","architecture","alpha","Volatility","consensus","only","trading","scanner","manage","max","exit","hyperliquid","entries","thesis","runtime","leverage","hunter","at",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":106,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":138,"publishedAt":37},"01M6J14FQY9YG5QB45QK4D98AZ","wolf-strategy","WOLF v6.3 — Fully autonomous multi-strategy trading for Hyperliquid perps via Senpi MCP. Manages multiple strategies simultaneously, each with independent wallets, budgets, slots, and DSL configs. 5+N cron architecture: 5 shared wolf crons (Emerging Movers 3min, SM Flip 5min, Watchdog 5min, Risk Guardian 5min, Health Check 10min) plus one DSL v5.2 cron per strategy (native Hyperliquid SL sync via dsl-dynamic-stop-loss skill v5.3.1). Same asset can be traded in different strategies simultaneously. Enter early on first jumps, not at confirmed peaks. Dynamic risk-based leverage per strategy. Requires Senpi MCP connection, python3, mcporter CLI, OpenClaw cron system, and dsl-dynamic-stop-loss skill (provides dsl-cli.py + dsl-v5.py).","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwolf-strategy\u002FSKILL.md",[135],{"categoryNo":136,"categoryName":137,"taxonomyType":82,"taxonomyTypeDesc":83},"01JBSEEDCAT000000000000023","Skill与智能体开发",[110,139,140,141,142,143,112,144,145,117,122,146,147,148,126,149,150,151,128,152],"wolf strategy","MCP","openclaw","cli","sync","watchdog","autonomous","perps","strategies","shared","asset","native","wallets","hyperliquid perps",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":106,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":160,"publishedAt":37},"01M6J14A20TMQ772M3H80XNB2T","wolf-howl","HOWL v2 — Hunt, Optimize, Win, Learn. Nightly self-improvement loop for the WOLF autonomous trading strategy. Runs once per day (via cron) to review all trades from the last 24 hours, compute win rates, analyze signal quality correlation, evaluate DSL tier performance, identify missed opportunities, and produce concrete improvement suggestions for the wolf-strategy skill. v2 adds fee drag ratio (FDR) analysis, holding period bucketing, LONG vs SHORT regime detection, rotation cost tracking, cumulative drift detection, and gross vs net profit factor separation. Use when setting up daily trade review automation, analyzing trading performance, or improving an autonomous trading strategy through data-driven feedback loops. Requires Senpi MCP connection, mcporter CLI, and OpenClaw cron system.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwolf-howl\u002FSKILL.md",[159],{"categoryNo":136,"categoryName":137,"taxonomyType":82,"taxonomyTypeDesc":83},[161,140,162,142,163,164,35,165,166,167,145,168,169,170,171,172,173,174,175,176],"wolf howl","review","performance","learn","loop","analyze","short","up","optimize","drift","missed","self","factor","loops","profit","data-driven",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":181,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":184,"publishedAt":37},"01M6J1469QYZMD7E6JDP8Z9X1V","whale-index","Auto-mirror top Discovery traders on Hyperliquid. Scans top 50 traders, scores on PnL rank (35%), win rate (25%), consistency (20%), hold time (10%), drawdown (10%). Creates 2-5 mirror strategies with overlap checks. Daily rebalance with 2-day watch period before swaps. Use when setting up trader mirroring, copy trading, or portfolio auto-rebalancing based on Discovery leaderboard performance.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fwhale-index\u002FSKILL.md",[],[185,186,187,163,188,189,34,33,190,191,168,192,193,147,23,194,195,196,21,197],"index","whale","whale index","auth","monitoring","auto","portfolio","daily","time","consistency","top","mirror","pnl",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":106,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":204,"publishedAt":37},"01M6J141MJ2JMM7X3BGEEW30J9","vixen-strategy","VIXEN v1.0 — Dual-mode emerging movers scanner. Built from FOX v1.6 live trading data (+34.5% ROI). Two entry modes: STALKER (steady accumulation, score 6+, catches the SM buildup BEFORE the explosion) and STRIKER (violent FIRST_JUMP, score 9+, with raw volume confirmation to filter blow-off tops). 2-hour per-asset cooldown after Phase 1 exits. DSL High Water Mode mandatory.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fhyperliquid-trading-firm\u002Fblob\u002Fmain\u002Fskills\u002Fsenpi-skills\u002Fvixen\u002FSKILL.md",[],[110,205,206,207,208,209,117,118,122,210,211,212,149,23,213,214,215,216,217,218],"vixen strategy","roi","data","first","confirmation","volume","live","filter","explosion","mode","high","v1","modes","emerging movers"]