[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X8D55ND5PYM0HEM8SC1G19":3,"skill-same:01M6X8D55ND5PYM0HEM8SC1G19":79},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6X8D55ND5PYM0HEM8SC1G19","literature-search",null,"Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.","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\u002Fliterature-search\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"search","literature","literature search","systematic","when","researchclaw","reviews","scientific","pubmed","search strategy","systematic literature review","writing","prisma","arxiv","use","strategy","literature review","methodology","database","review","2026-11-10 10:12:30",false,"---\nname: literature-search\ndescription: Systematic literature review methodology including search strategy, screening, and synthesis. Use when conducting literature reviews or writing background sections.\nmetadata:\n  category: experiment\n  trigger-keywords: \"literature,review,systematic,PRISMA,search,database,PubMed,arXiv,citation\"\n  applicable-stages: \"3,4,5,6\"\n  priority: \"2\"\n  version: \"1.0\"\n  author: researchclaw\n  references: \"adapted from K-Dense-AI\u002Fclaude-scientific-skills\"\n---\n\n## Literature Search Best Practice\n\n### Search Strategy Design\n1. Define research question using PICO framework (Population, Intervention, Comparison, Outcome)\n2. Identify 2-4 core concepts from the research question\n3. List synonyms, abbreviations, and related terms for each concept\n4. Combine terms with Boolean operators: AND (between concepts), OR (within synonyms)\n5. Select at least 3 complementary databases relevant to the domain:\n   - Biomedical: PubMed, Scopus, Web of Science\n   - Computer science: arXiv, Semantic Scholar, DBLP, ACL Anthology\n   - Interdisciplinary: Google Scholar, OpenAlex\n6. Document exact search strings for reproducibility\n\n### Inclusion and Exclusion Criteria\n1. Define date range (e.g., last 5-10 years for rapidly evolving fields)\n2. Specify language restrictions (typically English)\n3. Specify publication types (peer-reviewed, preprints, conference papers)\n4. Define study design requirements (RCTs, observational, computational)\n5. Set domain-specific filters (species, methodology, sample size)\n6. Document all criteria BEFORE screening begins\n\n### PRISMA Methodology\n1. Record total hits from each database before deduplication\n2. Remove duplicates and record count\n3. Screen titles and abstracts against inclusion criteria (record excluded count)\n4. Full-text review of remaining papers (record excluded with reasons)\n5. Report final included studies with PRISMA flow diagram\n6. For scoping reviews, use PRISMA-ScR extension\n\n### Screening and Quality Assessment\n1. Use two-pass screening: title\u002Fabstract first, then full text\n2. Apply quality assessment tools appropriate to study type:\n   - RCTs: Cochrane Risk of Bias tool\n   - Observational: Newcastle-Ottawa Scale\n   - ML papers: check reproducibility, dataset validity, statistical rigor\n3. Extract data systematically using a predefined extraction form\n\n### Synthesis Approaches\n1. **Narrative synthesis**: Organize findings thematically, identify patterns and contradictions\n2. **Meta-analysis**: Pool quantitative results when studies are sufficiently homogeneous\n3. **Gap analysis**: Explicitly identify what is NOT covered in the literature\n4. Summarize key findings per theme with supporting citation counts\n5. Highlight conflicting results and possible explanations\n6. End with clear statement of research gaps that motivate your study\n","## Literature Search Best Practice\n\n### Search Strategy Design\n1. Define research question using PICO framework (Population, Intervention, Comparison, Outcome)\n2. Identify 2-4 core concepts from the research question\n3. List synonyms, abbreviations, and related terms for each concept\n4. Combine terms with Boolean operators: AND (between concepts), OR (within synonyms)\n5. Select at least 3 complementary databases relevant to the domain:\n   - Biomedical: PubMed, Scopus, Web of Science\n   - Computer science: arXiv, Semantic Scholar, DBLP, ACL Anthology\n   - Interdisciplinary: Google Scholar, OpenAlex\n6. Document exact search strings for reproducibility\n\n### Inclusion and Exclusion Criteria\n1. Define date range (e.g., last 5-10 years for rapidly evolving fields)\n2. Specify language restrictions (typically English)\n3. Specify publication types (peer-reviewed, preprints, conference papers)\n4. Define study design requirements (RCTs, observational, computational)\n5. Set domain-specific filters (species, methodology, sample size)\n6. Document all criteria BEFORE screening begins\n\n### PRISMA Methodology\n1. Record total hits from each database before deduplication\n2. Remove duplicates and record count\n3. Screen titles and abstracts against inclusion criteria (record excluded count)\n4. Full-text review of remaining papers (record excluded with reasons)\n5. Report final included studies with PRISMA flow diagram\n6. For scoping reviews, use PRISMA-ScR extension\n\n### Screening and Quality Assessment\n1. Use two-pass screening: title\u002Fabstract first, then full text\n2. Apply quality assessment tools appropriate to study type:\n   - RCTs: Cochrane Risk of Bias tool\n   - Observational: Newcastle-Ottawa Scale\n   - ML papers: check reproducibility, dataset validity, statistical rigor\n3. Extract data systematically using a predefined extraction form\n\n### Synthesis Approaches\n1. **Narrative synthesis**: Organize findings thematically, identify patterns and contradictions\n2. **Meta-analysis**: Pool quantitative results when studies are sufficiently homogeneous\n3. **Gap analysis**: Explicitly identify what is NOT covered in the literature\n4. Summarize key findings per theme with supporting citation counts\n5. Highlight conflicting results and possible explanations\n6. End with clear statement of research gaps that motivate your study\n",{"name":5,"metadata":49,"description":7},{"author":29,"version":50,"category":51,"priority":52,"references":53,"trigger-keywords":54,"applicable-stages":55},"1.0","experiment","2","adapted from K-Dense-AI\u002Fclaude-scientific-skills","literature,review,systematic,PRISMA,search,database,PubMed,arXiv,citation","3,4,5,6","main","bfe4e4653cba696a8dc20f70c6006651b997fdef","migrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fliterature-search",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":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add enuno\u002Fcrewless-capital --skill literature-search",{"tool":71,"command":72},"bunx","bunx skills add enuno\u002Fcrewless-capital --skill literature-search",{"tool":74,"command":75},"pnpm","pnpm dlx skills add enuno\u002Fcrewless-capital --skill literature-search","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\u002Fliterature-search\u002FSKILL.md",true,[],[80,107,133,158,182,203],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":89,"publishedAt":44},"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",[86],{"categoryNo":87,"categoryName":88,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[82,90,91,92,93,94,24,95,96,97,98,99,100,101,102,103,104,105,106,28],"ai agents","memory","rag","semantic search","infrastructure","applications","api","knowledge","context","Art","personalization","across","learned","retention","for","persistent memory","static",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":44},"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",[],[39,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,104,130,131,132],"wolverine strategy","architecture","alpha","Volatility","consensus","only","trading","scanner","manage","max","hyperliquid","runtime","leverage","hunter","at","managed","dsl","phase",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":142,"publishedAt":44},"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",[139],{"categoryNo":140,"categoryName":141,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[39,143,144,145,116,146,147,148,149,150,151,152,153,127,154,155,129,156,157,104],"wolf strategy","MCP","openclaw","health","system","python3","check","health check","stop","strategies","shared","connection","wallets","stop-loss","hyperliquid perps",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":165,"publishedAt":44},"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",[164],{"categoryNo":140,"categoryName":141,"taxonomyType":21,"taxonomyTypeDesc":22},[166,144,43,167,168,169,170,171,172,173,174,175,176,177,178,179,180,131,181,28],"wolf howl","performance","learn","analysis","loop","analyze","short","autonomous","up","optimize","drift","self","factor","loops","profit","fdr",{"skillNo":183,"name":184,"nameZh":6,"description":185,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":186,"categories":187,"keywords":188,"publishedAt":44},"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",[],[189,190,191,167,192,193,194,38,195,174,196,197,152,198,199,200,201,104,202,28],"index","whale","whale index","auth","monitoring","discovery","auto","daily","time","with","consistency","top","mirror","pnl",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":209,"publishedAt":44},"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",[],[39,210,211,212,213,214,122,215,216,217,198,218,219,220,221,222,223,224,131,132],"vixen strategy","roi","data","first","confirmation","volume","filter","asset","explosion","mode","high","v1","modes","emerging movers","entry"]