[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X8CSG39FX06EE0RKTM7QFQ":3,"skill-same:01M6X8CSG39FX06EE0RKTM7QFQ":73},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":6,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6X8CSG39FX06EE0RKTM7QFQ","biology-biopython",null,"Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA\u002FRNA\u002Fprotein sequences or biological databases.","enuno\u002Fcrewless-capital","enuno","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6727752?v=4","01M6J09HF6Y74150F7PEMC446X","Elvis Nuno","01M6X86WGZG9RH09ACTSTJ2383",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fbiology-biopython\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"biopython","biology biopython","biology","sequences","file","when","sequence","researchclaw","gene","for","with","scientific","RNA","DNA","phylogenetics","bioinformatics","domain","databases","use","parsing","2026-11-10 10:12:30",false,"---\nname: biology-biopython\ndescription: Bioinformatics with Biopython for sequence manipulation, file parsing, BLAST, and phylogenetics. Use when working with DNA\u002FRNA\u002Fprotein sequences or biological databases.\nmetadata:\n  category: domain\n  trigger-keywords: \"sequence,FASTA,genome,protein,BLAST,phylogenetic,biopython,bioinformatics,gene,DNA,RNA\"\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## Biopython Bioinformatics Best Practice\n\n### Sequence Manipulation\n1. Create sequences: `from Bio.Seq import Seq; seq = Seq(\"ATGCGA\")`\n2. Complement: `seq.complement()`; Reverse complement: `seq.reverse_complement()`\n3. Transcription: `seq.transcribe()` (DNA to RNA)\n4. Translation: `seq.translate()` (DNA\u002FRNA to protein)\n5. GC content: `from Bio.SeqUtils import gc_fraction; gc_fraction(seq)`\n6. Molecular weight: `from Bio.SeqUtils import molecular_weight`\n\n### File Parsing (SeqIO)\n1. Read FASTA: `for rec in SeqIO.parse(\"file.fasta\", \"fasta\"): ...`\n2. Read GenBank: `for rec in SeqIO.parse(\"file.gb\", \"genbank\"): ...`\n3. Read single record: `rec = SeqIO.read(\"file.fasta\", \"fasta\")`\n4. Write sequences: `SeqIO.write(records, \"output.fasta\", \"fasta\")`\n5. Convert formats: `SeqIO.convert(\"input.gb\", \"genbank\", \"output.fasta\", \"fasta\")`\n6. Index large files: `idx = SeqIO.index(\"large.fasta\", \"fasta\")` for random access\n\n### BLAST Operations\n1. Online BLAST: `from Bio.Blast import NCBIWWW; result = NCBIWWW.qblast(\"blastn\", \"nt\", seq)`\n2. Parse results: `from Bio.Blast import NCBIXML; records = NCBIXML.parse(result)`\n3. Local BLAST: run via subprocess, parse XML output with NCBIXML\n4. Always set `Entrez.email` before any NCBI access\n5. Filter results by e-value (typically \u003C 1e-5) and coverage\n\n### NCBI Database Access (Entrez)\n1. Always set email: `Entrez.email = \"your@email.com\"`\n2. Search: `handle = Entrez.esearch(db=\"pubmed\", term=\"query\")`\n3. Fetch records: `handle = Entrez.efetch(db=\"nucleotide\", id=\"ID\", rettype=\"fasta\")`\n4. Use API key for higher rate limits (10 req\u002Fs vs 3 req\u002Fs)\n5. Respect NCBI rate limits; add delays between batch requests\n\n### Phylogenetics (Bio.Phylo)\n1. Read trees: `from Bio import Phylo; tree = Phylo.read(\"tree.nwk\", \"newick\")`\n2. Draw trees: `Phylo.draw(tree)` or `Phylo.draw_ascii(tree)`\n3. Supported formats: newick, nexus, phyloxml\n4. Traverse clades: `for clade in tree.find_clades(): ...`\n5. Calculate distances: `tree.distance(clade1, clade2)`\n\n### Structure Analysis (Bio.PDB)\n1. Parse PDB: `parser = PDBParser(); structure = parser.get_structure(\"id\", \"file.pdb\")`\n2. Hierarchy: Structure > Model > Chain > Residue > Atom\n3. Get atoms: iterate through `structure.get_atoms()`\n4. Calculate distances: use atom coordinate vectors\n5. For mmCIF files: use `MMCIFParser()` instead of `PDBParser()`\n\n### Common Pitfalls\n1. Always handle `SeqIO.parse` as an iterator — it exhausts after one pass\n2. Check sequence alphabet compatibility before operations\n3. Large files: use `SeqIO.index()` not `SeqIO.to_dict()` to avoid memory issues\n4. Set proper timeout for remote BLAST queries (can take minutes)\n5. Validate parsed data — missing annotations are common in public databases\n","## Biopython Bioinformatics Best Practice\n\n### Sequence Manipulation\n1. Create sequences: `from Bio.Seq import Seq; seq = Seq(\"ATGCGA\")`\n2. Complement: `seq.complement()`; Reverse complement: `seq.reverse_complement()`\n3. Transcription: `seq.transcribe()` (DNA to RNA)\n4. Translation: `seq.translate()` (DNA\u002FRNA to protein)\n5. GC content: `from Bio.SeqUtils import gc_fraction; gc_fraction(seq)`\n6. Molecular weight: `from Bio.SeqUtils import molecular_weight`\n\n### File Parsing (SeqIO)\n1. Read FASTA: `for rec in SeqIO.parse(\"file.fasta\", \"fasta\"): ...`\n2. Read GenBank: `for rec in SeqIO.parse(\"file.gb\", \"genbank\"): ...`\n3. Read single record: `rec = SeqIO.read(\"file.fasta\", \"fasta\")`\n4. Write sequences: `SeqIO.write(records, \"output.fasta\", \"fasta\")`\n5. Convert formats: `SeqIO.convert(\"input.gb\", \"genbank\", \"output.fasta\", \"fasta\")`\n6. Index large files: `idx = SeqIO.index(\"large.fasta\", \"fasta\")` for random access\n\n### BLAST Operations\n1. Online BLAST: `from Bio.Blast import NCBIWWW; result = NCBIWWW.qblast(\"blastn\", \"nt\", seq)`\n2. Parse results: `from Bio.Blast import NCBIXML; records = NCBIXML.parse(result)`\n3. Local BLAST: run via subprocess, parse XML output with NCBIXML\n4. Always set `Entrez.email` before any NCBI access\n5. Filter results by e-value (typically \u003C 1e-5) and coverage\n\n### NCBI Database Access (Entrez)\n1. Always set email: `Entrez.email = \"your@email.com\"`\n2. Search: `handle = Entrez.esearch(db=\"pubmed\", term=\"query\")`\n3. Fetch records: `handle = Entrez.efetch(db=\"nucleotide\", id=\"ID\", rettype=\"fasta\")`\n4. Use API key for higher rate limits (10 req\u002Fs vs 3 req\u002Fs)\n5. Respect NCBI rate limits; add delays between batch requests\n\n### Phylogenetics (Bio.Phylo)\n1. Read trees: `from Bio import Phylo; tree = Phylo.read(\"tree.nwk\", \"newick\")`\n2. Draw trees: `Phylo.draw(tree)` or `Phylo.draw_ascii(tree)`\n3. Supported formats: newick, nexus, phyloxml\n4. Traverse clades: `for clade in tree.find_clades(): ...`\n5. Calculate distances: `tree.distance(clade1, clade2)`\n\n### Structure Analysis (Bio.PDB)\n1. Parse PDB: `parser = PDBParser(); structure = parser.get_structure(\"id\", \"file.pdb\")`\n2. Hierarchy: Structure > Model > Chain > Residue > Atom\n3. Get atoms: iterate through `structure.get_atoms()`\n4. Calculate distances: use atom coordinate vectors\n5. For mmCIF files: use `MMCIFParser()` instead of `PDBParser()`\n\n### Common Pitfalls\n1. Always handle `SeqIO.parse` as an iterator — it exhausts after one pass\n2. Check sequence alphabet compatibility before operations\n3. Large files: use `SeqIO.index()` not `SeqIO.to_dict()` to avoid memory issues\n4. Set proper timeout for remote BLAST queries (can take minutes)\n5. Validate parsed data — missing annotations are common in public databases\n",{"name":5,"metadata":44,"description":7},{"author":26,"version":45,"category":35,"priority":46,"references":47,"trigger-keywords":48,"applicable-stages":49},"1.0","4","adapted from K-Dense-AI\u002Fclaude-scientific-skills","sequence,FASTA,genome,protein,BLAST,phylogenetic,biopython,bioinformatics,gene,DNA,RNA","9,10,12","main","bfe4e4653cba696a8dc20f70c6006651b997fdef","migrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fbiology-biopython",0,"https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital","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 10:49:15",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add enuno\u002Fcrewless-capital --skill biology-biopython",{"tool":65,"command":66},"bunx","bunx skills add enuno\u002Fcrewless-capital --skill biology-biopython",{"tool":68,"command":69},"pnpm","pnpm dlx skills add enuno\u002Fcrewless-capital --skill biology-biopython","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fbiology-biopython\u002FSKILL.md",true,[],[74,103,130,155,180,200],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":85,"publishedAt":39},"01M6X94YFR1XNANZ321FS661NG","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsupermemory\u002FSKILL.md",[80],{"categoryNo":81,"categoryName":82,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[76,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,28,101,102,24],"ai agents","memory","rag","semantic search","infrastructure","search","applications","api","knowledge","context","Art","personalization","across","learned","retention","persistent memory","static",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":107,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":110,"publishedAt":39},"01M6X94S3K97CSK7VFF5H85ED2","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwolverine\u002FSKILL.md",[],[111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,28,127,128,129],"strategy","wolverine strategy","architecture","alpha","Volatility","consensus","only","trading","scanner","manage","max","hyperliquid","runtime","leverage","hunter","at","managed","dsl","phase",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":107,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":139,"publishedAt":39},"01M6X94HN3CHBM37GM2C84KT26","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwolf-strategy\u002FSKILL.md",[136],{"categoryNo":137,"categoryName":138,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000023","Skill与智能体开发",[111,140,141,142,113,143,144,145,146,147,148,149,150,124,151,152,126,153,154,28],"wolf strategy","MCP","openclaw","health","system","python3","check","health check","stop","strategies","shared","connection","wallets","stop-loss","hyperliquid perps",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":107,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":162,"publishedAt":39},"01M6X949FBGWWGY3YZ8N3WB3CP","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwolf-howl\u002FSKILL.md",[161],{"categoryNo":137,"categoryName":138,"taxonomyType":83,"taxonomyTypeDesc":84},[163,141,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,128,179,24],"wolf howl","review","performance","learn","analysis","loop","analyze","short","autonomous","up","optimize","drift","self","factor","loops","profit","fdr",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":186,"publishedAt":39},"01M6X9437CQ55N5AYHCPF71JWG","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.","https:\u002F\u002Fgithub.com\u002Fenuno\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fwhale-index\u002FSKILL.md",[],[187,188,189,165,190,191,192,37,193,172,194,195,149,29,196,197,198,28,199,24],"index","whale","whale index","auth","monitoring","discovery","auto","daily","time","consistency","top","mirror","pnl",{"skillNo":201,"name":202,"nameZh":6,"description":203,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":107,"licenseFileUrl":6,"sourceHtmlUrl":204,"categories":205,"keywords":206,"publishedAt":39},"01M6X93YTRGNPYNHZRTW9XNH1E","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\u002Fcrewless-capital\u002Fblob\u002Fmain\u002Fmigrate-hyperliquid-firm\u002Fskills\u002Fsenpi-skills\u002Fvixen\u002FSKILL.md",[],[111,207,208,209,210,211,119,212,213,214,29,215,216,217,218,219,220,221,128,129],"vixen strategy","roi","data","first","confirmation","volume","filter","asset","explosion","mode","high","v1","modes","emerging movers","entry"]