[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X8DFW60FDNTC6QXNFJGN3B":3,"skill-same:01M6X8DFW60FDNTC6QXNFJGN3B":85},{"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":31,"publishedAt":51,"linkOnly":52,"markdownContent":53,"markdownBody":54,"frontMatter":55,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":6,"ownerHtmlUrl":67,"ownerBlogUrl":68,"ownerDescription":69,"updatedAt":70,"install":71,"resources":84},"01M6X8DFW60FDNTC6QXNFJGN3B","scientific-writing",null,"Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.","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-writing\u002FSKILL.md",[18,23,26],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":27,"categoryName":28,"taxonomyType":29,"taxonomyTypeDesc":30},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50],"scientific writing","scientific","writing","Academic","when","researchclaw","manuscript","papers","with","paper","formatting","Citation","structure","methods","use","results","reporting","guidelines","research","2026-11-10 10:12:30",false,"---\nname: scientific-writing\ndescription: Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.\nmetadata:\n  category: writing\n  trigger-keywords: \"paper,manuscript,writing,IMRAD,citation,abstract,introduction,methods,results,discussion\"\n  applicable-stages: \"16,17,19\"\n  priority: \"2\"\n  version: \"1.0\"\n  author: researchclaw\n  references: \"adapted from K-Dense-AI\u002Fclaude-scientific-skills\"\n---\n\n## Scientific Writing Best Practice\n\n### IMRAD Structure\n1. **Abstract**: State objective, methods, key results, and conclusion in 150-300 words\n2. **Introduction**: Move from broad context to specific gap to your contribution (funnel structure)\n3. **Methods**: Sufficient detail for replication; use past tense, passive voice\n4. **Results**: Present findings without interpretation; pair text with figures\u002Ftables\n5. **Discussion**: Interpret results, compare with literature, acknowledge limitations, state implications\n\n### Paragraph-Level Guidance\n1. Each paragraph should convey ONE main idea\n2. Open with a topic sentence; close with a transition to the next paragraph\n3. Write in full flowing prose — never submit bullet points as final manuscript text\n4. Use active voice for clarity: \"We measured...\" not \"Measurements were taken...\"\n5. Vary sentence length; aim for average 15-25 words per sentence\n\n### Citation Best Practices\n1. Cite primary sources over reviews when making specific claims\n2. Use citation styles consistently (APA, Vancouver, IEEE) per target journal\n3. Every factual claim needs a citation unless it is common knowledge in the field\n4. Avoid citation strings of 5+ references — select the most relevant 2-3\n5. Self-citations should be limited to genuinely relevant prior work\n\n### Common Writing Pitfalls\n1. Avoid hedge-stacking: \"It might possibly suggest...\" — choose one hedge\n2. Do not start sentences with \"It is well known that\" — cite or remove\n3. Distinguish \"significant\" (statistical) from \"substantial\" (practical)\n4. Ensure figures\u002Ftables are referenced in text BEFORE they appear\n5. Keep abbreviations to a minimum; define each on first use\n\n### Reporting Guidelines\n1. Randomized trials: follow CONSORT checklist\n2. Observational studies: follow STROBE checklist\n3. Systematic reviews: follow PRISMA checklist\n4. Diagnostic accuracy: follow STARD checklist\n5. Always check target journal's author guidelines for specific requirements\n\n### Revision Checklist\n1. Verify all figures\u002Ftables are cited in text and numbered sequentially\n2. Confirm reference list matches in-text citations exactly\n3. Check that abstract accurately reflects the final manuscript content\n4. Ensure methods section enables independent replication\n5. Read aloud to catch awkward phrasing and run-on sentences\n","## Scientific Writing Best Practice\n\n### IMRAD Structure\n1. **Abstract**: State objective, methods, key results, and conclusion in 150-300 words\n2. **Introduction**: Move from broad context to specific gap to your contribution (funnel structure)\n3. **Methods**: Sufficient detail for replication; use past tense, passive voice\n4. **Results**: Present findings without interpretation; pair text with figures\u002Ftables\n5. **Discussion**: Interpret results, compare with literature, acknowledge limitations, state implications\n\n### Paragraph-Level Guidance\n1. Each paragraph should convey ONE main idea\n2. Open with a topic sentence; close with a transition to the next paragraph\n3. Write in full flowing prose — never submit bullet points as final manuscript text\n4. Use active voice for clarity: \"We measured...\" not \"Measurements were taken...\"\n5. Vary sentence length; aim for average 15-25 words per sentence\n\n### Citation Best Practices\n1. Cite primary sources over reviews when making specific claims\n2. Use citation styles consistently (APA, Vancouver, IEEE) per target journal\n3. Every factual claim needs a citation unless it is common knowledge in the field\n4. Avoid citation strings of 5+ references — select the most relevant 2-3\n5. Self-citations should be limited to genuinely relevant prior work\n\n### Common Writing Pitfalls\n1. Avoid hedge-stacking: \"It might possibly suggest...\" — choose one hedge\n2. Do not start sentences with \"It is well known that\" — cite or remove\n3. Distinguish \"significant\" (statistical) from \"substantial\" (practical)\n4. Ensure figures\u002Ftables are referenced in text BEFORE they appear\n5. Keep abbreviations to a minimum; define each on first use\n\n### Reporting Guidelines\n1. Randomized trials: follow CONSORT checklist\n2. Observational studies: follow STROBE checklist\n3. Systematic reviews: follow PRISMA checklist\n4. Diagnostic accuracy: follow STARD checklist\n5. Always check target journal's author guidelines for specific requirements\n\n### Revision Checklist\n1. Verify all figures\u002Ftables are cited in text and numbered sequentially\n2. Confirm reference list matches in-text citations exactly\n3. Check that abstract accurately reflects the final manuscript content\n4. Ensure methods section enables independent replication\n5. Read aloud to catch awkward phrasing and run-on sentences\n",{"name":5,"metadata":56,"description":7},{"author":37,"version":57,"category":34,"priority":58,"references":59,"trigger-keywords":60,"applicable-stages":61},"1.0","2","adapted from K-Dense-AI\u002Fclaude-scientific-skills","paper,manuscript,writing,IMRAD,citation,abstract,introduction,methods,results,discussion","16,17,19","main","bfe4e4653cba696a8dc20f70c6006651b997fdef","migrate-hyperliquid-firm\u002Fmulticlaw\u002FAutoResearchClaw\u002F.claude\u002Fskills\u002Fscientific-writing",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":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add enuno\u002Fcrewless-capital --skill scientific-writing",{"tool":77,"command":78},"bunx","bunx skills add enuno\u002Fcrewless-capital --skill scientific-writing",{"tool":80,"command":81},"pnpm","pnpm dlx skills add enuno\u002Fcrewless-capital --skill scientific-writing","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-writing\u002FSKILL.md",true,[],[86,114,141,166,191,211],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":95,"publishedAt":51},"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",[92],{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",[88,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,36],"ai agents","memory","rag","semantic search","infrastructure","search","applications","api","knowledge","context","Art","personalization","across","learned","retention","for","persistent memory","static",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":118,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":121,"publishedAt":51},"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",[],[122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,111,138,139,140],"strategy","wolverine strategy","architecture","alpha","Volatility","consensus","only","trading","scanner","manage","max","hyperliquid","runtime","leverage","hunter","at","managed","dsl","phase",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":118,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":150,"publishedAt":51},"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",[147],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[122,151,152,153,124,154,155,156,157,158,159,160,161,135,162,163,137,164,165,111],"wolf strategy","MCP","openclaw","health","system","python3","check","health check","stop","strategies","shared","connection","wallets","stop-loss","hyperliquid perps",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":118,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":173,"publishedAt":51},"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",[172],{"categoryNo":148,"categoryName":149,"taxonomyType":21,"taxonomyTypeDesc":22},[174,152,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,139,190,36],"wolf howl","review","performance","learn","analysis","loop","analyze","short","autonomous","up","optimize","drift","self","factor","loops","profit","fdr",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":195,"categories":196,"keywords":197,"publishedAt":51},"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",[],[198,199,200,176,201,202,203,46,204,183,205,206,160,40,207,208,209,111,210,36],"index","whale","whale index","auth","monitoring","discovery","auto","daily","time","consistency","top","mirror","pnl",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":118,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":217,"publishedAt":51},"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",[],[122,218,219,220,221,222,130,223,224,225,40,226,227,228,229,230,231,232,139,140],"vixen strategy","roi","data","first","confirmation","volume","filter","asset","explosion","mode","high","v1","modes","emerging movers","entry"]