[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JM8353Y8J31WZQ30A4MSGC":3,"skill-same:01M6JM8353Y8J31WZQ30A4MSGC":96},{"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":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6JM8353Y8J31WZQ30A4MSGC","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 don't fit in memory.","transaurus\u002Fstaging-davila7-claude-code-templates","transaurus","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210882017?v=4","01M6JH1R6ZCRXGJCTDP5ZKZBE6","Page Turner Agent","01M6JH1RBBR2P6Y0XWDGM4B0G7",0,"MIT","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"in memory","lazy","for","at","ML Pipelines","processing","statistics","files","work","visualizations","memory","data","analyzing","Evaluation","dataframe","visualization","create","core","machine learning","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 don't fit in memory.\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","# 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",{"name":5,"description":7},"main","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex",2,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates","https:\u002F\u002Fgithub.com\u002Ftransaurus","Automated works of Page Turner","2026-03-28 10:12:59",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill vaex",{"tool":62,"command":63},"bunx","bunx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill vaex",{"tool":65,"command":66},"pnpm","pnpm dlx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill vaex","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002FSKILL.md",true,[70,76,80,84,88,92],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fcore_dataframes.md","REFERENCE","参考文档",7878,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fcore_dataframes.md",{"path":77,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fdata_processing.md",12451,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fdata_processing.md",{"path":81,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fio_operations.md",14989,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fio_operations.md",{"path":85,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fmachine_learning.md",15941,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fmachine_learning.md",{"path":89,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fperformance.md",13705,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fperformance.md",{"path":93,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":94,"sourceHtmlUrl":95},"references\u002Fvisualization.md",13188,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fvisualization.md",[97,126,147,171,191,215],{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":106,"publishedAt":43},"01M6JMRJR3BE0HGWFWDZSB853S","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fzapier-make-patterns\u002FSKILL.md",[103],{"categoryNo":104,"categoryName":105,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,26,125],"zapier make patterns","patterns","make","zapier","writing","workflow","automation","code","integrations","business","use","power","complexity","platform","apps","how to","No-Code","which","spawner",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":43},"01M6JMR8YK6SWPCTG1MCQTMN9S","yeet","Use only when the user explicitly asks to stage, commit, push, and open a GitHub pull request in one flow using the GitHub CLI (`gh`).","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fyeet\u002FSKILL.md",[],[128,133,134,135,136,137,138,139,140,141,142,143,117,144,145,146],"cli","github","commit","pull request","openai","pull","push","flow","open","gh","user","only","stage","github cli",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":154,"publishedAt":43},"01M6JMQNFE64ZASQK90HARRXW8","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off.  This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation.  Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fworkflow-automation\u002FSKILL.md",[153],{"categoryNo":104,"categoryName":105,"taxonomyType":21,"taxonomyTypeDesc":22},[112,113,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,26,125],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":177,"publishedAt":43},"01M6JMQDYJ0XETERPHGDR3EVGN","trigger-dev","Trigger.dev expert for background jobs, AI workflows, and reliable async execution with excellent developer experience and TypeScript-first design. Use when: trigger.dev, trigger dev, background task, ai background job, long running task.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Ftrigger-dev\u002FSKILL.md",[],[178,179,180,181,182,158,183,159,160,184,117,185,186,187,188,189,190,170,26,125],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"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":200,"publishedAt":43},"01M6JMQ5J6KK3GSX9FJCWWK6G6","planning-with-files","Transforms workflow to use Manus-style persistent markdown files for planning, progress tracking, and knowledge storage. Use when starting complex tasks, multi-step projects, research tasks, or when the user mentions planning, organizing work, tracking progress, or wants structured output.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fplanning-with-files\u002FSKILL.md",[197],{"categoryNo":198,"categoryName":199,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[201,202,31,170,203,112,204,205,206,207,208,32,117,209,210,211,212,213,214,26],"planning with files","planning","with files","storage","tasks","markdown","knowledge","projects","manus","structured output","progress","transforms","multi","output",{"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":225,"publishedAt":43},"01M6JMPWAT60RXXNEC4V9TRBRS","n8n-workflow-patterns","Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.","https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fworkflow-automation\u002Fn8n\u002Fn8n-workflow-patterns\u002FSKILL.md",[221,224],{"categoryNo":222,"categoryName":223,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":104,"categoryName":105,"taxonomyType":21,"taxonomyTypeDesc":22},[112,226,165,227,108,228,229,158,230,205,202,231,232,233,234,235,236,29,237,238],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","database","api","planning workflow","structure","database operations","webhook","http api","scheduled"]