[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFQT06H36W024BRHV1HMZR":3,"skill-same:01M6AFQT06H36W024BRHV1HMZR":115},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":57,"commitSha":58,"skillDirectory":59,"forksCount":60,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6AFQT06H36W024BRHV1HMZR","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.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\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":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[5,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"analyzing","when","lazy","at","ML Pipelines","processing","statistics","files","work","visualizations","in memory","data","memory","Evaluation","csv","fast","dataframe","parquet","arrow","visualization","create","core","ml","big data","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","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add davila7\u002Fclaude-code-templates --skill vaex",{"tool":73,"command":74},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill vaex",{"tool":76,"command":77},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill vaex","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002FSKILL.md",true,[81,87,91,95,99,103,107,111],{"path":82,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fapi-quick-ref.md","REFERENCE","参考文档",3220,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fsandbox-sdk\u002Freferences\u002Fapi-quick-ref.md",{"path":88,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fcore_dataframes.md",7878,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fcore_dataframes.md",{"path":92,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fdata_processing.md",12451,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fdata_processing.md",{"path":96,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fexamples.md",1570,"https:\u002F\u002Fgithub.com\u002Fshengdabai\u002FTony-Claude-Code-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fsandbox-sdk\u002Freferences\u002Fexamples.md",{"path":100,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fio_operations.md",14989,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fio_operations.md",{"path":104,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fmachine_learning.md",15941,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fmachine_learning.md",{"path":108,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":109,"sourceHtmlUrl":110},"references\u002Fperformance.md",13705,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fperformance.md",{"path":112,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":113,"sourceHtmlUrl":114},"references\u002Fvisualization.md",13188,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fvaex\u002Freferences\u002Fvisualization.md",[116,143,169,202,237,261],{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"publishedAt":52},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[122],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[124,125,126,127,128,129,130,131,132,28,133,134,135,136,137,138,139,140,141,142],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"publishedAt":52},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[149],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[151,152,153,154,155,156,157,158,159,160,161,28,162,163,35,164,165,166,167,168],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","plan","focus","signals","audit","dependency",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":184,"publishedAt":52},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[175,178,179],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":180,"categoryName":181,"taxonomyType":182,"taxonomyTypeDesc":183},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[185,186,187,188,189,190,191,129,192,193,194,195,196,197,198,132,199,200,28,201],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":206,"licenseFileUrl":207,"sourceHtmlUrl":208,"categories":209,"keywords":213,"publishedAt":52},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[210],{"categoryNo":211,"categoryName":212,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,129,236],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":238,"name":239,"nameZh":6,"description":240,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":206,"licenseFileUrl":241,"sourceHtmlUrl":242,"categories":243,"keywords":248,"publishedAt":52},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. 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