[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFTRNF6N6BCXBWJ5F7XHBZ":3,"skill-same:01M6XFTRNF6N6BCXBWJ5F7XHBZ":103},{"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":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":14,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":62,"install":63,"resources":76},"01M6XFTRNF6N6BCXBWJ5F7XHBZ","vaex",null,"Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV\u002FHDF5\u002FArrow\u002FParquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\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":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[5,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"work","when","lazy","at","ML Pipelines","processing","synthetic","statistics","do","files","memory","visualizations","in memory","data","analyzing","Evaluation","dataframe","visualization","create","2026-11-10 10:12:30",false,"---\nname: vaex\ndescription: Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV\u002FHDF5\u002FArrow\u002FParquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.\ncategory: data-engineering\nlicense: MIT license\nmetadata:\n    skill-author: Synthetic Sciences\n---\n\n# Vaex\n\n## Overview\n\nVaex is a high-performance Python library designed for lazy, out-of-core DataFrames to process and visualize tabular datasets that are too large to fit into RAM. Vaex can process over a billion rows per second, enabling interactive data exploration and analysis on datasets with billions of rows.\n\n## When to Use This Skill\n\nUse Vaex when:\n- Processing tabular datasets larger than available RAM (gigabytes to terabytes)\n- Performing fast statistical aggregations on massive datasets\n- Creating visualizations and heatmaps of large datasets\n- Building machine learning pipelines on big data\n- Converting between data formats (CSV, HDF5, Arrow, Parquet)\n- Needing lazy evaluation and virtual columns to avoid memory overhead\n- Working with astronomical data, financial time series, or other large-scale scientific datasets\n\n## Core Capabilities\n\nVaex provides six primary capability areas, each documented in detail in the references directory:\n\n### 1. DataFrames and Data Loading\n\nLoad and create Vaex DataFrames from various sources including files (HDF5, CSV, Arrow, Parquet), pandas DataFrames, NumPy arrays, and dictionaries. Reference `references\u002Fcore_dataframes.md` for:\n- Opening large files efficiently\n- Converting from pandas\u002FNumPy\u002FArrow\n- Working with example datasets\n- Understanding DataFrame structure\n\n### 2. Data Processing and Manipulation\n\nPerform filtering, create virtual columns, use expressions, and aggregate data without loading everything into memory. Reference `references\u002Fdata_processing.md` for:\n- Filtering and selections\n- Virtual columns and expressions\n- Groupby operations and aggregations\n- String operations and datetime handling\n- Working with missing data\n\n### 3. Performance and Optimization\n\nLeverage Vaex's lazy evaluation, caching strategies, and memory-efficient operations. Reference `references\u002Fperformance.md` for:\n- Understanding lazy evaluation\n- Using `delay=True` for batching operations\n- Materializing columns when needed\n- Caching strategies\n- Asynchronous operations\n\n### 4. Data Visualization\n\nCreate interactive visualizations of large datasets including heatmaps, histograms, and scatter plots. Reference `references\u002Fvisualization.md` for:\n- Creating 1D and 2D plots\n- Heatmap visualizations\n- Working with selections\n- Customizing plots and subplots\n\n### 5. Machine Learning Integration\n\nBuild ML pipelines with transformers, encoders, and integration with scikit-learn, XGBoost, and other frameworks. Reference `references\u002Fmachine_learning.md` for:\n- Feature scaling and encoding\n- PCA and dimensionality reduction\n- K-means clustering\n- Integration with scikit-learn\u002FXGBoost\u002FCatBoost\n- Model serialization and deployment\n\n### 6. I\u002FO Operations\n\nEfficiently read and write data in various formats with optimal performance. Reference `references\u002Fio_operations.md` for:\n- File format recommendations\n- Export strategies\n- Working with Apache Arrow\n- CSV handling for large files\n- Server and remote data access\n\n## Quick Start Pattern\n\nFor most Vaex tasks, follow this pattern:\n\n```python\nimport vaex\n\n# 1. Open or create DataFrame\ndf = vaex.open('large_file.hdf5')  # or .csv, .arrow, .parquet\n# OR\ndf = vaex.from_pandas(pandas_df)\n\n# 2. Explore the data\nprint(df)  # Shows first\u002Flast rows and column info\ndf.describe()  # Statistical summary\n\n# 3. Create virtual columns (no memory overhead)\ndf['new_column'] = df.x ** 2 + df.y\n\n# 4. Filter with selections\ndf_filtered = df[df.age > 25]\n\n# 5. Compute statistics (fast, lazy evaluation)\nmean_val = df.x.mean()\nstats = df.groupby('category').agg({'value': 'sum'})\n\n# 6. Visualize\ndf.plot1d(df.x, limits=[0, 100])\ndf.plot(df.x, df.y, limits='99.7%')\n\n# 7. Export if needed\ndf.export_hdf5('output.hdf5')\n```\n\n## Working with References\n\nThe reference files contain detailed information about each capability area. Load references into context based on the specific task:\n\n- **Basic operations**: Start with `references\u002Fcore_dataframes.md` and `references\u002Fdata_processing.md`\n- **Performance issues**: Check `references\u002Fperformance.md`\n- **Visualization tasks**: Use `references\u002Fvisualization.md`\n- **ML pipelines**: Reference `references\u002Fmachine_learning.md`\n- **File I\u002FO**: Consult `references\u002Fio_operations.md`\n\n## Best Practices\n\n1. **Use HDF5 or Apache Arrow formats** for optimal performance with large datasets\n2. **Leverage virtual columns** instead of materializing data to save memory\n3. **Batch operations** using `delay=True` when performing multiple calculations\n4. **Export to efficient formats** rather than keeping data in CSV\n5. **Use expressions** for complex calculations without intermediate storage\n6. **Profile with `df.stat()`** to understand memory usage and optimize operations\n\n## Common Patterns\n\n### Pattern: Converting Large CSV to HDF5\n```python\nimport vaex\n\n# Open large CSV (processes in chunks automatically)\ndf = vaex.from_csv('large_file.csv')\n\n# Export to HDF5 for faster future access\ndf.export_hdf5('large_file.hdf5')\n\n# Future loads are instant\ndf = vaex.open('large_file.hdf5')\n```\n\n### Pattern: Efficient Aggregations\n```python\n# Use delay=True to batch multiple operations\nmean_x = df.x.mean(delay=True)\nstd_y = df.y.std(delay=True)\nsum_z = df.z.sum(delay=True)\n\n# Execute all at once\nresults = vaex.execute([mean_x, std_y, sum_z])\n```\n\n### Pattern: Virtual Columns for Feature Engineering\n```python\n# No memory overhead - computed on the fly\ndf['age_squared'] = df.age ** 2\ndf['full_name'] = df.first_name + ' ' + df.last_name\ndf['is_adult'] = df.age >= 18\n```\n\n## Resources\n\nThis skill includes reference documentation in the `references\u002F` directory:\n\n- `core_dataframes.md` - DataFrame creation, loading, and basic structure\n- `data_processing.md` - Filtering, expressions, aggregations, and transformations\n- `performance.md` - Optimization strategies and lazy evaluation\n- `visualization.md` - Plotting and interactive visualizations\n- `machine_learning.md` - ML pipelines and model integration\n- `io_operations.md` - File formats and data import\u002Fexport\n\n","# Vaex\n\n## Overview\n\nVaex is a high-performance Python library designed for lazy, out-of-core DataFrames to process and visualize tabular datasets that are too large to fit into RAM. Vaex can process over a billion rows per second, enabling interactive data exploration and analysis on datasets with billions of rows.\n\n## When to Use This Skill\n\nUse Vaex when:\n- Processing tabular datasets larger than available RAM (gigabytes to terabytes)\n- Performing fast statistical aggregations on massive datasets\n- Creating visualizations and heatmaps of large datasets\n- Building machine learning pipelines on big data\n- Converting between data formats (CSV, HDF5, Arrow, Parquet)\n- Needing lazy evaluation and virtual columns to avoid memory overhead\n- Working with astronomical data, financial time series, or other large-scale scientific datasets\n\n## Core Capabilities\n\nVaex provides six primary capability areas, each documented in detail in the references directory:\n\n### 1. DataFrames and Data Loading\n\nLoad and create Vaex DataFrames from various sources including files (HDF5, CSV, Arrow, Parquet), pandas DataFrames, NumPy arrays, and dictionaries. Reference `references\u002Fcore_dataframes.md` for:\n- Opening large files efficiently\n- Converting from pandas\u002FNumPy\u002FArrow\n- Working with example datasets\n- Understanding DataFrame structure\n\n### 2. Data Processing and Manipulation\n\nPerform filtering, create virtual columns, use expressions, and aggregate data without loading everything into memory. Reference `references\u002Fdata_processing.md` for:\n- Filtering and selections\n- Virtual columns and expressions\n- Groupby operations and aggregations\n- String operations and datetime handling\n- Working with missing data\n\n### 3. Performance and Optimization\n\nLeverage Vaex's lazy evaluation, caching strategies, and memory-efficient operations. Reference `references\u002Fperformance.md` for:\n- Understanding lazy evaluation\n- Using `delay=True` for batching operations\n- Materializing columns when needed\n- Caching strategies\n- Asynchronous operations\n\n### 4. Data Visualization\n\nCreate interactive visualizations of large datasets including heatmaps, histograms, and scatter plots. Reference `references\u002Fvisualization.md` for:\n- Creating 1D and 2D plots\n- Heatmap visualizations\n- Working with selections\n- Customizing plots and subplots\n\n### 5. Machine Learning Integration\n\nBuild ML pipelines with transformers, encoders, and integration with scikit-learn, XGBoost, and other frameworks. Reference `references\u002Fmachine_learning.md` for:\n- Feature scaling and encoding\n- PCA and dimensionality reduction\n- K-means clustering\n- Integration with scikit-learn\u002FXGBoost\u002FCatBoost\n- Model serialization and deployment\n\n### 6. I\u002FO Operations\n\nEfficiently read and write data in various formats with optimal performance. Reference `references\u002Fio_operations.md` for:\n- File format recommendations\n- Export strategies\n- Working with Apache Arrow\n- CSV handling for large files\n- Server and remote data access\n\n## Quick Start Pattern\n\nFor most Vaex tasks, follow this pattern:\n\n```python\nimport vaex\n\n# 1. Open or create DataFrame\ndf = vaex.open('large_file.hdf5')  # or .csv, .arrow, .parquet\n# OR\ndf = vaex.from_pandas(pandas_df)\n\n# 2. Explore the data\nprint(df)  # Shows first\u002Flast rows and column info\ndf.describe()  # Statistical summary\n\n# 3. Create virtual columns (no memory overhead)\ndf['new_column'] = df.x ** 2 + df.y\n\n# 4. Filter with selections\ndf_filtered = df[df.age > 25]\n\n# 5. Compute statistics (fast, lazy evaluation)\nmean_val = df.x.mean()\nstats = df.groupby('category').agg({'value': 'sum'})\n\n# 6. Visualize\ndf.plot1d(df.x, limits=[0, 100])\ndf.plot(df.x, df.y, limits='99.7%')\n\n# 7. Export if needed\ndf.export_hdf5('output.hdf5')\n```\n\n## Working with References\n\nThe reference files contain detailed information about each capability area. Load references into context based on the specific task:\n\n- **Basic operations**: Start with `references\u002Fcore_dataframes.md` and `references\u002Fdata_processing.md`\n- **Performance issues**: Check `references\u002Fperformance.md`\n- **Visualization tasks**: Use `references\u002Fvisualization.md`\n- **ML pipelines**: Reference `references\u002Fmachine_learning.md`\n- **File I\u002FO**: Consult `references\u002Fio_operations.md`\n\n## Best Practices\n\n1. **Use HDF5 or Apache Arrow formats** for optimal performance with large datasets\n2. **Leverage virtual columns** instead of materializing data to save memory\n3. **Batch operations** using `delay=True` when performing multiple calculations\n4. **Export to efficient formats** rather than keeping data in CSV\n5. **Use expressions** for complex calculations without intermediate storage\n6. **Profile with `df.stat()`** to understand memory usage and optimize operations\n\n## Common Patterns\n\n### Pattern: Converting Large CSV to HDF5\n```python\nimport vaex\n\n# Open large CSV (processes in chunks automatically)\ndf = vaex.from_csv('large_file.csv')\n\n# Export to HDF5 for faster future access\ndf.export_hdf5('large_file.hdf5')\n\n# Future loads are instant\ndf = vaex.open('large_file.hdf5')\n```\n\n### Pattern: Efficient Aggregations\n```python\n# Use delay=True to batch multiple operations\nmean_x = df.x.mean(delay=True)\nstd_y = df.y.std(delay=True)\nsum_z = df.z.sum(delay=True)\n\n# Execute all at once\nresults = vaex.execute([mean_x, std_y, sum_z])\n```\n\n### Pattern: Virtual Columns for Feature Engineering\n```python\n# No memory overhead - computed on the fly\ndf['age_squared'] = df.age ** 2\ndf['full_name'] = df.first_name + ' ' + df.last_name\ndf['is_adult'] = df.age >= 18\n```\n\n## Resources\n\nThis skill includes reference documentation in the `references\u002F` directory:\n\n- `core_dataframes.md` - DataFrame creation, loading, and basic structure\n- `data_processing.md` - Filtering, expressions, aggregations, and transformations\n- `performance.md` - Optimization strategies and lazy evaluation\n- `visualization.md` - Plotting and interactive visualizations\n- `machine_learning.md` - ML pipelines and model integration\n- `io_operations.md` - File formats and data import\u002Fexport\n\n",{"name":5,"license":53,"category":54,"metadata":55,"description":7},"MIT license","data-engineering",{"skill-author":56},"Synthetic Sciences","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add fadrienne\u002FMyGitHub --skill vaex",{"tool":69,"command":70},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill vaex",{"tool":72,"command":73},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill vaex","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\u002FSKILL.md",true,[77,83,87,91,95,99],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fcore_dataframes.md","REFERENCE","参考文档",7878,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\u002Freferences\u002Fcore_dataframes.md",{"path":84,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fdata_processing.md",12451,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\u002Freferences\u002Fdata_processing.md",{"path":88,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fio_operations.md",14989,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\u002Freferences\u002Fio_operations.md",{"path":92,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fmachine_learning.md",15941,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\u002Freferences\u002Fmachine_learning.md",{"path":96,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fperformance.md",13705,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\u002Freferences\u002Fperformance.md",{"path":100,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fvisualization.md",13188,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fdata-engineering\u002Fvaex\u002Freferences\u002Fvisualization.md",[104,133,159,191,215,241],{"skillNo":105,"name":106,"nameZh":6,"description":107,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":108,"categories":109,"keywords":113,"publishedAt":48},"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).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[110],{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[114,115,116,117,118,119,120,121,30,122,123,124,125,126,127,128,129,130,131,132],"power","statistical power","effect","scipy","regression","choose","design","uv","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":142,"publishedAt":48},"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",[139],{"categoryNo":140,"categoryName":141,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[124,143,114,144,145,146,30,147,148,42,149,150,151,152,153,154,155,156,157,158],"statistical analysis","academic research","implementing","selection","you","reporting","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":172,"publishedAt":48},"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",[165,168,169],{"categoryNo":166,"categoryName":167,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":170,"categoryName":171,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[173,174,175,176,177,178,179,180,181,182,122,183,184,185,186,187,188,189,148,190],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"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":199,"publishedAt":48},"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",[197,198],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},[46,174,200,201,202,203,204,205,206,181,30,207,208,209,210,211,212,213,149,214],"scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":216,"name":217,"nameZh":6,"description":218,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":219,"categories":220,"keywords":227,"publishedAt":48},"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",[221,224],{"categoryNo":222,"categoryName":223,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":225,"categoryName":226,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师",[228,229,154,230,39,231,232,121,233,30,122,124,234,235,236,237,238,239,189,240],"code","research code","which","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":242,"name":243,"nameZh":6,"description":244,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":245,"licenseFileUrl":6,"sourceHtmlUrl":246,"categories":247,"keywords":254,"publishedAt":48},"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",[248,251,252,253],{"categoryNo":249,"categoryName":250,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":166,"categoryName":167,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":111,"categoryName":112,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":170,"categoryName":171,"taxonomyType":26,"taxonomyTypeDesc":27},[173,255,256,257,258,259,46,260,261,30,122,124,262,263,264,265,266,267,174,189],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]