[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"creator:01M6X64V8VWCRSMFZ4EPS2BWXG":3,"creator-list:01M6X64V8VWCRSMFZ4EPS2BWXG:stars:1:":20},{"accountNo":4,"login":5,"displayName":6,"avatarUrl":7,"accountType":8,"accountTypeDesc":9,"profileDescription":10,"skillCount":11,"repoCount":12,"professionCount":13,"scenarioCount":14,"totalStars":15,"updatedAt":16,"htmlUrl":17,"blogUrl":10,"company":10,"location":18,"followersCount":15,"publicReposCount":19},"01M6X64V8VWCRSMFZ4EPS2BWXG","fadrienne","Francesca Adrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","USER","个人作者",null,217,1,8,7,0,"2026-10-28 00:43:09","https:\u002F\u002Fgithub.com\u002Ffadrienne","Seychelles",6,{"records":21,"total":11,"current":12,"size":482},[22,58,85,119,146,173,200,223,246,271,290,310,332,360,383,406,436,460],{"skillNo":23,"name":24,"nameZh":10,"description":25,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":29,"categories":30,"keywords":36,"publishedAt":57},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","fadrienne\u002FMyGitHub","01M6X64VD5MW4F9PHDWQKS7ZAF","MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[31],{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56],"power","statistical power","effect","scipy","regression","choose","design","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample","2026-11-10 10:12:30",{"skillNo":59,"name":60,"nameZh":10,"description":61,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":62,"categories":63,"keywords":67,"publishedAt":57},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000008","测试与质量",[48,68,37,69,70,71,45,72,73,74,75,76,77,78,79,80,81,82,83,84],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":86,"name":87,"nameZh":10,"description":88,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":89,"categories":90,"keywords":100,"publishedAt":57},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[91,94,95],{"categoryNo":92,"categoryName":93,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[101,102,103,104,105,106,107,108,109,110,46,111,112,113,114,115,116,117,73,118],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":120,"name":121,"nameZh":10,"description":122,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":123,"categories":124,"keywords":129,"publishedAt":57},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[125,128],{"categoryNo":126,"categoryName":127,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},[130,102,131,132,133,134,135,136,137,109,45,138,139,140,141,142,143,144,75,145],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":147,"name":148,"nameZh":10,"description":149,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":150,"categories":151,"keywords":158,"publishedAt":57},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[152,155],{"categoryNo":153,"categoryName":154,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":156,"categoryName":157,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000030","开发工程师",[159,160,80,161,162,163,164,44,165,45,46,48,166,167,168,169,170,171,117,172],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":174,"name":175,"nameZh":10,"description":176,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":177,"licenseFileUrl":10,"sourceHtmlUrl":178,"categories":179,"keywords":186,"publishedAt":57},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[180,183,184,185],{"categoryNo":181,"categoryName":182,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":92,"categoryName":93,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},[101,187,188,189,190,191,130,192,193,45,46,48,194,195,196,197,198,199,102,117],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram",{"skillNo":201,"name":202,"nameZh":10,"description":203,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":204,"categories":205,"keywords":214,"publishedAt":57},"01M716618J5EDTN4RW41JZ4W4Z","hypothesis-generation","Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fhypothesis-generation\u002FSKILL.md",[206,207,210,211],{"categoryNo":65,"categoryName":66,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":208,"categoryName":209,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":212,"categoryName":213,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000032","设计师",[215,216,217,218,219,43,110,45,220,72,46,111,114,102,221,117,118,172,222,74],"hypothesis generation","generation","hypothesis","testing","open","predictions","hypothesis formulation","edit",{"skillNo":224,"name":225,"nameZh":10,"description":226,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":227,"categories":228,"keywords":230,"publishedAt":57},"01M7165SZR4WQ9S2MR10H5YFVW","geomaster","Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fgeomaster\u002FSKILL.md",[229],{"categoryNo":156,"categoryName":157,"taxonomyType":98,"taxonomyTypeDesc":99},[225,231,232,233,234,235,236,48,159,237,238,102,239,240,241,74,242,243,244,245],"processing","rust","vector","domains","remote","workflows","statistics","Julia","native","modeling","network","go","sentinel","spatial statistics","computer",{"skillNo":247,"name":248,"nameZh":10,"description":249,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":250,"categories":251,"keywords":257,"publishedAt":57},"01M7165P0FVVXJFS1KK777EWB3","exploratory-data-analysis","Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexploratory-data-analysis\u002FSKILL.md",[252,253,254],{"categoryNo":126,"categoryName":127,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":255,"categoryName":256,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000033","数据分析师",[258,259,48,74,260,45,261,262,113,263,102,189,264,265,266,267,268,269,270,78],"exploratory data analysis","data analysis","files","across","reports","metabolomics","recommendations","analyzing","formats","metrics","general","file","type",{"skillNo":272,"name":273,"nameZh":10,"description":274,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":275,"categories":276,"keywords":279,"publishedAt":57},"01M7165HF6FPJR7BBYMSX4X2PV","experimental-design","Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects\u002Fsamples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster\u002Fgroup randomization, Latin squares, plate layouts, batch\u002Frun-order effects, replication vs. pseudoreplication, or sequential\u002Fadaptive\u002Fgroup-sequential designs. Trigger this even for informal phrasings like \"how should I set up this experiment\", \"how do I avoid confounding\", \"what's the best way to test these 6 factors\", or \"assign these mice to conditions\". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexperimental-design\u002FSKILL.md",[277,278],{"categoryNo":208,"categoryName":209,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":212,"categoryName":213,"taxonomyType":98,"taxonomyTypeDesc":99},[43,280,281,282,37,283,284,285,44,286,287,46,48,288,56,289,169,170,117,172],"experimental design","SET","pandas","up","design of experiments","how to","analyzing data","effects","batch","how",{"skillNo":291,"name":292,"nameZh":10,"description":293,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":294,"categories":295,"keywords":296,"publishedAt":57},"01M7165E7R73AWEFBNV8C1HVZH","experiment-tracking","Log, compare, and manage ML\u002Fresearch experiment runs using MLflow or Weights & Biases (W&B). Use when someone needs to track hyperparameters, metrics, and artifacts across multiple training runs; compare experiments; manage model versions; or reproduce a past run. Covers setup, run logging, experiment comparison, artifact management, hyperparameter sweeps, and model registry. Use MLflow for self-hosted\u002Fopen-source setups; use W&B when cloud collaboration and sweep management are priorities.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexperiment-tracking\u002FSKILL.md",[],[297,298,299,300,301,219,302,44,303,45,46,304,261,305,306,307,308,169,309,117],"Experiment Tracking","experiment","tracking","model","training","open source","free","log","Model Registry","self","track","artifact","past",{"skillNo":311,"name":312,"nameZh":10,"description":313,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":314,"categories":315,"keywords":317,"publishedAt":57},"01M7165BA88N1E3C4FCW2Q883G","exa-search","Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch\u002Fextract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fexa-search\u002FSKILL.md",[316],{"categoryNo":153,"categoryName":154,"taxonomyType":34,"taxonomyTypeDesc":35},[318,319,320,321,322,323,283,324,325,45,326,327,111,288,328,102,329,330,331,75],"search","exa search","exa","fetch","python sdk","web search","internet","access","URL","tasks","info","article","full","domain",{"skillNo":333,"name":334,"nameZh":10,"description":335,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":336,"categories":337,"keywords":344,"publishedAt":57},"01M71657EPT38WJ2RY9RXE1ZZP","database-lookup","Deterministically query 78 public scientific, biomedical, materials science, regulatory, finance, and demographics databases through documented REST APIs. Use for reproducible lookups of compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, structures, astronomy objects, environmental records, or database-backed scientific facts when endpoints, filters, pagination, and provenance need to be explicit.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fdatabase-lookup\u002FSKILL.md",[338,341],{"categoryNo":339,"categoryName":340,"taxonomyType":34,"taxonomyTypeDesc":35},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":342,"categoryName":343,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000040","财务",[345,346,347,348,349,350,351,352,353,354,45,355,46,356,102,117,357,75,358,359],"database","database lookup","lookup","databases","patents","proteins","objects","variants","clinical","filters","query","clinical trials","biomedical","Reproducible","records",{"skillNo":361,"name":362,"nameZh":10,"description":363,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":10,"sourceHtmlUrl":364,"categories":365,"keywords":370,"publishedAt":57},"01M716525QQ90YX5NV8PW5BE0A","citation-management","Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fcitation-management\u002FSKILL.md",[366,367,368,369],{"categoryNo":92,"categoryName":93,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":153,"categoryName":154,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":96,"categoryName":97,"taxonomyType":98,"taxonomyTypeDesc":99},[371,372,373,318,101,374,72,375,102,376,377,117,172,378,222,379,380,381,382,75],"citation management","management","Citation","information","pubmed","limits","generate","scholar","rate","google","NCBI","email",{"skillNo":384,"name":385,"nameZh":10,"description":386,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":28,"licenseFileUrl":387,"sourceHtmlUrl":388,"categories":389,"keywords":391,"publishedAt":57},"01M6XEN4M4CV1T7VQ5SVY0MPJ1","gstack","Fast headless browser for QA testing and site dogfooding. 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Use when the user wants to study a document through GitHub Copilot CLI, Amp, Claude Code, or Hermes Agent, apply an author's frameworks while working, or build a reusable knowledge base from a file.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fbook-to-skill\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fbook-to-skill\u002FSKILL.md",[413,414,415],{"categoryNo":181,"categoryName":182,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":32,"categoryName":33,"taxonomyType":34,"taxonomyTypeDesc":35},{"categoryNo":416,"categoryName":417,"taxonomyType":98,"taxonomyTypeDesc":99},"01JBSEEDCAT000000000000034","行政与办公人员",[419,420,421,422,423,424,196,425,426,427,428,429,77,430,431,432,269,433,434,435],"book to skill","book","docx","PDF","knowledge","documents","knowledge base","github copilot cli","anti-patterns","calibre","frameworks","document","reusable","apply","author","github","principles",{"skillNo":437,"name":438,"nameZh":10,"description":439,"descriptionZh":10,"repositoryFullName":26,"ownerLogin":5,"ownerAvatarUrl":7,"ownerAccountNo":4,"ownerDisplayName":6,"repositoryNo":27,"starsCount":15,"licenseSpdxId":177,"licenseFileUrl":440,"sourceHtmlUrl":441,"categories":442,"keywords":447,"publishedAt":57},"01M6XEGECK3F139Z3D3GCARM1T","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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