[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X8DC1X5EQD7Q07VGDPNF67":3,"skill-same:01M6X8DC1X5EQD7Q07VGDPNF67":80},{"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":26,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":6,"ownerHtmlUrl":62,"ownerBlogUrl":63,"ownerDescription":64,"updatedAt":65,"install":66,"resources":79},"01M6X8DC1X5EQD7Q07VGDPNF67","scientific-visualization",null,"Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.","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\u002Fscientific-visualization\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"visualization","scientific visualization","scientific","ready","when","submission","researchclaw","figures","with","chart","journal","formatting","design","use","annotations","seaborn","matplotlib","accessibility","writing","2026-11-10 10:12:30",false,"---\nname: scientific-visualization\ndescription: Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.\nmetadata:\n  category: writing\n  trigger-keywords: \"figure,plot,chart,visualization,matplotlib,seaborn,colorblind,publication\"\n  applicable-stages: \"14,17,22\"\n  priority: \"3\"\n  version: \"1.0\"\n  author: researchclaw\n  references: \"adapted from K-Dense-AI\u002Fclaude-scientific-skills\"\n---\n\n## Scientific Visualization Best Practice\n\n### Figure Design Principles\n1. Every figure must have a clear, self-contained message\n2. Minimize chartjunk: remove gridlines, background shading, and 3D effects\n3. Use direct labeling instead of legends when possible\n4. Remove top and right spines for cleaner appearance\n5. Ensure all text is readable at final print size (minimum 6pt font)\n\n### Journal Figure Sizing\n1. **Single column**: 3.3-3.5 inches (85-89 mm) wide\n2. **1.5 column**: 4.5-5.5 inches (114-140 mm) wide\n3. **Double column \u002F full width**: 6.5-7.1 inches (165-180 mm) wide\n4. Resolution: 300 DPI minimum for raster; prefer vector formats (PDF, EPS, SVG)\n5. Check target journal author guidelines for exact specifications\n\n### Colorblind-Safe Design\n1. Use colorblind-friendly palettes: seaborn \"colorblind\", Okabe-Ito, viridis, cividis\n2. NEVER rely on color alone — combine with shape, pattern, or line style\n3. Avoid red-green combinations; prefer blue-orange or blue-yellow contrasts\n4. Test figures with a colorblind simulator before submission\n5. Ensure figures work in grayscale for print journals\n\n### Multi-Panel Layouts\n1. Label panels with uppercase letters: (A), (B), (C) in bold, top-left corner\n2. Use consistent axis scales across panels when comparing related data\n3. Share axes where appropriate to reduce redundancy\n4. Maintain consistent font sizes and line widths across all panels\n5. Use `plt.subplots()` with `constrained_layout=True` for automatic spacing\n\n### Statistical Annotations on Figures\n1. Show individual data points alongside summary statistics (box + strip plots)\n2. Always include error bars; specify type in caption (SEM, SD, 95% CI)\n3. Use significance brackets with stars: * p\u003C.05, ** p\u003C.01, *** p\u003C.001\n4. Annotate effect sizes or key statistics directly on the figure when helpful\n5. Never use bar charts for small-n data — use dot plots or box plots instead\n\n### Export and Quality Checklist\n1. Save in vector format (PDF\u002FSVG) for line art; TIFF\u002FPNG for photographs\n2. Embed fonts or convert text to outlines for cross-platform consistency\n3. Verify axis labels include units in parentheses: \"Time (s)\", \"Force (N)\"\n4. Ensure figure caption fully explains all symbols, abbreviations, and panels\n5. Check that color-coded elements match between figure and caption\n","## Scientific Visualization Best Practice\n\n### Figure Design Principles\n1. Every figure must have a clear, self-contained message\n2. Minimize chartjunk: remove gridlines, background shading, and 3D effects\n3. Use direct labeling instead of legends when possible\n4. Remove top and right spines for cleaner appearance\n5. Ensure all text is readable at final print size (minimum 6pt font)\n\n### Journal Figure Sizing\n1. **Single column**: 3.3-3.5 inches (85-89 mm) wide\n2. **1.5 column**: 4.5-5.5 inches (114-140 mm) wide\n3. **Double column \u002F full width**: 6.5-7.1 inches (165-180 mm) wide\n4. Resolution: 300 DPI minimum for raster; prefer vector formats (PDF, EPS, SVG)\n5. Check target journal author guidelines for exact specifications\n\n### Colorblind-Safe Design\n1. Use colorblind-friendly palettes: seaborn \"colorblind\", Okabe-Ito, viridis, cividis\n2. NEVER rely on color alone — combine with shape, pattern, or line style\n3. Avoid red-green combinations; prefer blue-orange or blue-yellow contrasts\n4. Test figures with a colorblind simulator before submission\n5. Ensure figures work in grayscale for print journals\n\n### Multi-Panel Layouts\n1. Label panels with uppercase letters: (A), (B), (C) in bold, top-left corner\n2. Use consistent axis scales across panels when comparing related data\n3. Share axes where appropriate to reduce redundancy\n4. Maintain consistent font sizes and line widths across all panels\n5. Use `plt.subplots()` with `constrained_layout=True` for automatic spacing\n\n### Statistical Annotations on Figures\n1. Show individual data points alongside summary statistics (box + strip plots)\n2. Always include error bars; specify type in caption (SEM, SD, 95% CI)\n3. Use significance brackets with stars: * p\u003C.05, ** p\u003C.01, *** p\u003C.001\n4. Annotate effect sizes or key statistics directly on the figure when helpful\n5. Never use bar charts for small-n data — use dot plots or box plots instead\n\n### Export and Quality Checklist\n1. Save in vector format (PDF\u002FSVG) for line art; TIFF\u002FPNG for photographs\n2. Embed fonts or convert text to outlines for cross-platform consistency\n3. Verify axis labels include units in parentheses: \"Time (s)\", \"Force (N)\"\n4. Ensure figure caption fully explains all symbols, abbreviations, and panels\n5. Check that color-coded elements match between figure and caption\n",{"name":5,"metadata":51,"description":7},{"author":33,"version":52,"category":45,"priority":53,"references":54,"trigger-keywords":55,"applicable-stages":56},"1.0","3","adapted from K-Dense-AI\u002Fclaude-scientific-skills","figure,plot,chart,visualization,matplotlib,seaborn,colorblind,publication","14,17,22","main","bfe4e4653cba696a8dc20f70c6006651b997fdef","migrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fscientific-visualization",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":67,"prompt":77,"zipAvailable":78},[68,71,74],{"tool":69,"command":70},"npx","npx skills add enuno\u002Fcrewless-capital --skill scientific-visualization",{"tool":72,"command":73},"bunx","bunx skills add enuno\u002Fcrewless-capital --skill scientific-visualization",{"tool":75,"command":76},"pnpm","pnpm dlx skills add enuno\u002Fcrewless-capital --skill scientific-visualization","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\u002Fscientific-visualization\u002FSKILL.md",true,[],[81,109,136,161,186,206],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":90,"publishedAt":46},"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",[87],{"categoryNo":88,"categoryName":89,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[83,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,31],"ai agents","memory","rag","semantic search","infrastructure","search","applications","api","knowledge","context","Art","personalization","across","learned","retention","for","persistent memory","static",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":113,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":116,"publishedAt":46},"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",[],[117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,106,133,134,135],"strategy","wolverine strategy","architecture","alpha","Volatility","consensus","only","trading","scanner","manage","max","hyperliquid","runtime","leverage","hunter","at","managed","dsl","phase",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":113,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":145,"publishedAt":46},"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",[142],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[117,146,147,148,119,149,150,151,152,153,154,155,156,130,157,158,132,159,160,106],"wolf strategy","MCP","openclaw","health","system","python3","check","health check","stop","strategies","shared","connection","wallets","stop-loss","hyperliquid perps",{"skillNo":162,"name":163,"nameZh":6,"description":164,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":113,"licenseFileUrl":6,"sourceHtmlUrl":165,"categories":166,"keywords":168,"publishedAt":46},"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",[167],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},[169,147,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,134,185,31],"wolf howl","review","performance","learn","analysis","loop","analyze","short","autonomous","up","optimize","drift","self","factor","loops","profit","fdr",{"skillNo":187,"name":188,"nameZh":6,"description":189,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":190,"categories":191,"keywords":192,"publishedAt":46},"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",[],[193,194,195,171,196,197,198,40,199,178,200,201,155,35,202,203,204,106,205,31],"index","whale","whale index","auth","monitoring","discovery","auto","daily","time","consistency","top","mirror","pnl",{"skillNo":207,"name":208,"nameZh":6,"description":209,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":113,"licenseFileUrl":6,"sourceHtmlUrl":210,"categories":211,"keywords":212,"publishedAt":46},"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",[],[117,213,214,215,216,217,125,218,219,220,35,221,222,223,224,225,226,227,134,135],"vixen strategy","roi","data","first","confirmation","volume","filter","asset","explosion","mode","high","v1","modes","emerging movers","entry"]