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Select, filter, group_by, joins, lazy evaluation, CSV\u002FParquet I\u002FO, expression API, for high-performance data analysis workflows.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33286,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"Evaluation","lazy","for","high","apache arrow","high-performance","filter","expression","select","api","analysis","data","data analysis","csv","fast","library","dataframe","parquet","arrow","apache","workflows","performance","2026-11-10 10:12:30",false,"---\nname: polars\ndescription: \"Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV\u002FParquet I\u002FO, expression API, for high-performance data analysis workflows.\"\n---\n\n# Polars\n\n## Overview\n\nPolars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.\n\n## Quick Start\n\n### Installation and Basic Usage\n\nInstall Polars:\n```python\nuv pip install polars\n```\n\nBasic DataFrame creation and operations:\n```python\nimport polars as pl\n\n# Create DataFrame\ndf = pl.DataFrame({\n    \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n    \"age\": [25, 30, 35],\n    \"city\": [\"NY\", \"LA\", \"SF\"]\n})\n\n# Select columns\ndf.select(\"name\", \"age\")\n\n# Filter rows\ndf.filter(pl.col(\"age\") > 25)\n\n# Add computed columns\ndf.with_columns(\n    age_plus_10=pl.col(\"age\") + 10\n)\n```\n\n## Core Concepts\n\n### Expressions\n\nExpressions are the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized.\n\n**Key principles:**\n- Use `pl.col(\"column_name\")` to reference columns\n- Chain methods to build complex transformations\n- Expressions are lazy and only execute within contexts (select, with_columns, filter, group_by)\n\n**Example:**\n```python\n# Expression-based computation\ndf.select(\n    pl.col(\"name\"),\n    (pl.col(\"age\") * 12).alias(\"age_in_months\")\n)\n```\n\n### Lazy vs Eager Evaluation\n\n**Eager (DataFrame):** Operations execute immediately\n```python\ndf = pl.read_csv(\"file.csv\")  # Reads immediately\nresult = df.filter(pl.col(\"age\") > 25)  # Executes immediately\n```\n\n**Lazy (LazyFrame):** Operations build a query plan, optimized before execution\n```python\nlf = pl.scan_csv(\"file.csv\")  # Doesn't read yet\nresult = lf.filter(pl.col(\"age\") > 25).select(\"name\", \"age\")\ndf = result.collect()  # Now executes optimized query\n```\n\n**When to use lazy:**\n- Working with large datasets\n- Complex query pipelines\n- When only some columns\u002Frows are needed\n- Performance is critical\n\n**Benefits of lazy evaluation:**\n- Automatic query optimization\n- Predicate pushdown\n- Projection pushdown\n- Parallel execution\n\nFor detailed concepts, load `references\u002Fcore_concepts.md`.\n\n## Common Operations\n\n### Select\nSelect and manipulate columns:\n```python\n# Select specific columns\ndf.select(\"name\", \"age\")\n\n# Select with expressions\ndf.select(\n    pl.col(\"name\"),\n    (pl.col(\"age\") * 2).alias(\"double_age\")\n)\n\n# Select all columns matching a pattern\ndf.select(pl.col(\"^.*_id$\"))\n```\n\n### Filter\nFilter rows by conditions:\n```python\n# Single condition\ndf.filter(pl.col(\"age\") > 25)\n\n# Multiple conditions (cleaner than using &)\ndf.filter(\n    pl.col(\"age\") > 25,\n    pl.col(\"city\") == \"NY\"\n)\n\n# Complex conditions\ndf.filter(\n    (pl.col(\"age\") > 25) | (pl.col(\"city\") == \"LA\")\n)\n```\n\n### With Columns\nAdd or modify columns while preserving existing ones:\n```python\n# Add new columns\ndf.with_columns(\n    age_plus_10=pl.col(\"age\") + 10,\n    name_upper=pl.col(\"name\").str.to_uppercase()\n)\n\n# Parallel computation (all columns computed in parallel)\ndf.with_columns(\n    pl.col(\"value\") * 10,\n    pl.col(\"value\") * 100,\n)\n```\n\n### Group By and Aggregations\nGroup data and compute aggregations:\n```python\n# Basic grouping\ndf.group_by(\"city\").agg(\n    pl.col(\"age\").mean().alias(\"avg_age\"),\n    pl.len().alias(\"count\")\n)\n\n# Multiple group keys\ndf.group_by(\"city\", \"department\").agg(\n    pl.col(\"salary\").sum()\n)\n\n# Conditional aggregations\ndf.group_by(\"city\").agg(\n    (pl.col(\"age\") > 30).sum().alias(\"over_30\")\n)\n```\n\nFor detailed operation patterns, load `references\u002Foperations.md`.\n\n## Aggregations and Window Functions\n\n### Aggregation Functions\nCommon aggregations within `group_by` context:\n- `pl.len()` - count rows\n- `pl.col(\"x\").sum()` - sum values\n- `pl.col(\"x\").mean()` - average\n- `pl.col(\"x\").min()` \u002F `pl.col(\"x\").max()` - extremes\n- `pl.first()` \u002F `pl.last()` - first\u002Flast values\n\n### Window Functions with `over()`\nApply aggregations while preserving row count:\n```python\n# Add group statistics to each row\ndf.with_columns(\n    avg_age_by_city=pl.col(\"age\").mean().over(\"city\"),\n    rank_in_city=pl.col(\"salary\").rank().over(\"city\")\n)\n\n# Multiple grouping columns\ndf.with_columns(\n    group_avg=pl.col(\"value\").mean().over(\"category\", \"region\")\n)\n```\n\n**Mapping strategies:**\n- `group_to_rows` (default): Preserves original row order\n- `explode`: Faster but groups rows together\n- `join`: Creates list columns\n\n## Data I\u002FO\n\n### Supported Formats\nPolars supports reading and writing:\n- CSV, Parquet, JSON, Excel\n- Databases (via connectors)\n- Cloud storage (S3, Azure, GCS)\n- Google BigQuery\n- Multiple\u002Fpartitioned files\n\n### Common I\u002FO Operations\n\n**CSV:**\n```python\n# Eager\ndf = pl.read_csv(\"file.csv\")\ndf.write_csv(\"output.csv\")\n\n# Lazy (preferred for large files)\nlf = pl.scan_csv(\"file.csv\")\nresult = lf.filter(...).select(...).collect()\n```\n\n**Parquet (recommended for performance):**\n```python\ndf = pl.read_parquet(\"file.parquet\")\ndf.write_parquet(\"output.parquet\")\n```\n\n**JSON:**\n```python\ndf = pl.read_json(\"file.json\")\ndf.write_json(\"output.json\")\n```\n\nFor comprehensive I\u002FO documentation, load `references\u002Fio_guide.md`.\n\n## Transformations\n\n### Joins\nCombine DataFrames:\n```python\n# Inner join\ndf1.join(df2, on=\"id\", how=\"inner\")\n\n# Left join\ndf1.join(df2, on=\"id\", how=\"left\")\n\n# Join on different column names\ndf1.join(df2, left_on=\"user_id\", right_on=\"id\")\n```\n\n### Concatenation\nStack DataFrames:\n```python\n# Vertical (stack rows)\npl.concat([df1, df2], how=\"vertical\")\n\n# Horizontal (add columns)\npl.concat([df1, df2], how=\"horizontal\")\n\n# Diagonal (union with different schemas)\npl.concat([df1, df2], how=\"diagonal\")\n```\n\n### Pivot and Unpivot\nReshape data:\n```python\n# Pivot (wide format)\ndf.pivot(values=\"sales\", index=\"date\", columns=\"product\")\n\n# Unpivot (long format)\ndf.unpivot(index=\"id\", on=[\"col1\", \"col2\"])\n```\n\nFor detailed transformation examples, load `references\u002Ftransformations.md`.\n\n## Pandas Migration\n\nPolars offers significant performance improvements over pandas with a cleaner API. Key differences:\n\n### Conceptual Differences\n- **No index**: Polars uses integer positions only\n- **Strict typing**: No silent type conversions\n- **Lazy evaluation**: Available via LazyFrame\n- **Parallel by default**: Operations parallelized automatically\n\n### Common Operation Mappings\n\n| Operation | Pandas | Polars |\n|-----------|--------|--------|\n| Select column | `df[\"col\"]` | `df.select(\"col\")` |\n| Filter | `df[df[\"col\"] > 10]` | `df.filter(pl.col(\"col\") > 10)` |\n| Add column | `df.assign(x=...)` | `df.with_columns(x=...)` |\n| Group by | `df.groupby(\"col\").agg(...)` | `df.group_by(\"col\").agg(...)` |\n| Window | `df.groupby(\"col\").transform(...)` | `df.with_columns(...).over(\"col\")` |\n\n### Key Syntax Patterns\n\n**Pandas sequential (slow):**\n```python\ndf.assign(\n    col_a=lambda df_: df_.value * 10,\n    col_b=lambda df_: df_.value * 100\n)\n```\n\n**Polars parallel (fast):**\n```python\ndf.with_columns(\n    col_a=pl.col(\"value\") * 10,\n    col_b=pl.col(\"value\") * 100,\n)\n```\n\nFor comprehensive migration guide, load `references\u002Fpandas_migration.md`.\n\n## Best Practices\n\n### Performance Optimization\n\n1. **Use lazy evaluation for large datasets:**\n   ```python\n   lf = pl.scan_csv(\"large.csv\")  # Don't use read_csv\n   result = lf.filter(...).select(...).collect()\n   ```\n\n2. **Avoid Python functions in hot paths:**\n   - Stay within expression API for parallelization\n   - Use `.map_elements()` only when necessary\n   - Prefer native Polars operations\n\n3. **Use streaming for very large data:**\n   ```python\n   lf.collect(streaming=True)\n   ```\n\n4. **Select only needed columns early:**\n   ```python\n   # Good: Select columns early\n   lf.select(\"col1\", \"col2\").filter(...)\n\n   # Bad: Filter on all columns first\n   lf.filter(...).select(\"col1\", \"col2\")\n   ```\n\n5. **Use appropriate data types:**\n   - Categorical for low-cardinality strings\n   - Appropriate integer sizes (i32 vs i64)\n   - Date types for temporal data\n\n### Expression Patterns\n\n**Conditional operations:**\n```python\npl.when(condition).then(value).otherwise(other_value)\n```\n\n**Column operations across multiple columns:**\n```python\ndf.select(pl.col(\"^.*_value$\") * 2)  # Regex pattern\n```\n\n**Null handling:**\n```python\npl.col(\"x\").fill_null(0)\npl.col(\"x\").is_null()\npl.col(\"x\").drop_nulls()\n```\n\nFor additional best practices and patterns, load `references\u002Fbest_practices.md`.\n\n## Resources\n\nThis skill includes comprehensive reference documentation:\n\n### references\u002F\n- `core_concepts.md` - Detailed explanations of expressions, lazy evaluation, and type system\n- `operations.md` - Comprehensive guide to all common operations with examples\n- `pandas_migration.md` - Complete migration guide from pandas to Polars\n- `io_guide.md` - Data I\u002FO operations for all supported formats\n- `transformations.md` - Joins, concatenation, pivots, and reshaping operations\n- `best_practices.md` - Performance optimization tips and common patterns\n\nLoad these references as needed when users require detailed information about specific topics.\n","# Polars\n\n## Overview\n\nPolars is a lightning-fast DataFrame library for Python and Rust built on Apache Arrow. Work with Polars' expression-based API, lazy evaluation framework, and high-performance data manipulation capabilities for efficient data processing, pandas migration, and data pipeline optimization.\n\n## Quick Start\n\n### Installation and Basic Usage\n\nInstall Polars:\n```python\nuv pip install polars\n```\n\nBasic DataFrame creation and operations:\n```python\nimport polars as pl\n\n# Create DataFrame\ndf = pl.DataFrame({\n    \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n    \"age\": [25, 30, 35],\n    \"city\": [\"NY\", \"LA\", \"SF\"]\n})\n\n# Select columns\ndf.select(\"name\", \"age\")\n\n# Filter rows\ndf.filter(pl.col(\"age\") > 25)\n\n# Add computed columns\ndf.with_columns(\n    age_plus_10=pl.col(\"age\") + 10\n)\n```\n\n## Core Concepts\n\n### Expressions\n\nExpressions are the fundamental building blocks of Polars operations. They describe transformations on data and can be composed, reused, and optimized.\n\n**Key principles:**\n- Use `pl.col(\"column_name\")` to reference columns\n- Chain methods to build complex transformations\n- Expressions are lazy and only execute within contexts (select, with_columns, filter, group_by)\n\n**Example:**\n```python\n# Expression-based computation\ndf.select(\n    pl.col(\"name\"),\n    (pl.col(\"age\") * 12).alias(\"age_in_months\")\n)\n```\n\n### Lazy vs Eager Evaluation\n\n**Eager (DataFrame):** Operations execute immediately\n```python\ndf = pl.read_csv(\"file.csv\")  # Reads immediately\nresult = df.filter(pl.col(\"age\") > 25)  # Executes immediately\n```\n\n**Lazy (LazyFrame):** Operations build a query plan, optimized before execution\n```python\nlf = pl.scan_csv(\"file.csv\")  # Doesn't read yet\nresult = lf.filter(pl.col(\"age\") > 25).select(\"name\", \"age\")\ndf = result.collect()  # Now executes optimized query\n```\n\n**When to use lazy:**\n- Working with large datasets\n- Complex query pipelines\n- When only some columns\u002Frows are needed\n- Performance is critical\n\n**Benefits of lazy evaluation:**\n- Automatic query optimization\n- Predicate pushdown\n- Projection pushdown\n- Parallel execution\n\nFor detailed concepts, load `references\u002Fcore_concepts.md`.\n\n## Common Operations\n\n### Select\nSelect and manipulate columns:\n```python\n# Select specific columns\ndf.select(\"name\", \"age\")\n\n# Select with expressions\ndf.select(\n    pl.col(\"name\"),\n    (pl.col(\"age\") * 2).alias(\"double_age\")\n)\n\n# Select all columns matching a pattern\ndf.select(pl.col(\"^.*_id$\"))\n```\n\n### Filter\nFilter rows by conditions:\n```python\n# Single condition\ndf.filter(pl.col(\"age\") > 25)\n\n# Multiple conditions (cleaner than using &)\ndf.filter(\n    pl.col(\"age\") > 25,\n    pl.col(\"city\") == \"NY\"\n)\n\n# Complex conditions\ndf.filter(\n    (pl.col(\"age\") > 25) | (pl.col(\"city\") == \"LA\")\n)\n```\n\n### With Columns\nAdd or modify columns while preserving existing ones:\n```python\n# Add new columns\ndf.with_columns(\n    age_plus_10=pl.col(\"age\") + 10,\n    name_upper=pl.col(\"name\").str.to_uppercase()\n)\n\n# Parallel computation (all columns computed in parallel)\ndf.with_columns(\n    pl.col(\"value\") * 10,\n    pl.col(\"value\") * 100,\n)\n```\n\n### Group By and Aggregations\nGroup data and compute aggregations:\n```python\n# Basic grouping\ndf.group_by(\"city\").agg(\n    pl.col(\"age\").mean().alias(\"avg_age\"),\n    pl.len().alias(\"count\")\n)\n\n# Multiple group keys\ndf.group_by(\"city\", \"department\").agg(\n    pl.col(\"salary\").sum()\n)\n\n# Conditional aggregations\ndf.group_by(\"city\").agg(\n    (pl.col(\"age\") > 30).sum().alias(\"over_30\")\n)\n```\n\nFor detailed operation patterns, load `references\u002Foperations.md`.\n\n## Aggregations and Window Functions\n\n### Aggregation Functions\nCommon aggregations within `group_by` context:\n- `pl.len()` - count rows\n- `pl.col(\"x\").sum()` - sum values\n- `pl.col(\"x\").mean()` - average\n- `pl.col(\"x\").min()` \u002F `pl.col(\"x\").max()` - extremes\n- `pl.first()` \u002F `pl.last()` - first\u002Flast values\n\n### Window Functions with `over()`\nApply aggregations while preserving row count:\n```python\n# Add group statistics to each row\ndf.with_columns(\n    avg_age_by_city=pl.col(\"age\").mean().over(\"city\"),\n    rank_in_city=pl.col(\"salary\").rank().over(\"city\")\n)\n\n# Multiple grouping columns\ndf.with_columns(\n    group_avg=pl.col(\"value\").mean().over(\"category\", \"region\")\n)\n```\n\n**Mapping strategies:**\n- `group_to_rows` (default): Preserves original row order\n- `explode`: Faster but groups rows together\n- `join`: Creates list columns\n\n## Data I\u002FO\n\n### Supported Formats\nPolars supports reading and writing:\n- CSV, Parquet, JSON, Excel\n- Databases (via connectors)\n- Cloud storage (S3, Azure, GCS)\n- Google BigQuery\n- Multiple\u002Fpartitioned files\n\n### Common I\u002FO Operations\n\n**CSV:**\n```python\n# Eager\ndf = pl.read_csv(\"file.csv\")\ndf.write_csv(\"output.csv\")\n\n# Lazy (preferred for large files)\nlf = pl.scan_csv(\"file.csv\")\nresult = lf.filter(...).select(...).collect()\n```\n\n**Parquet (recommended for performance):**\n```python\ndf = pl.read_parquet(\"file.parquet\")\ndf.write_parquet(\"output.parquet\")\n```\n\n**JSON:**\n```python\ndf = pl.read_json(\"file.json\")\ndf.write_json(\"output.json\")\n```\n\nFor comprehensive I\u002FO documentation, load `references\u002Fio_guide.md`.\n\n## Transformations\n\n### Joins\nCombine DataFrames:\n```python\n# Inner join\ndf1.join(df2, on=\"id\", how=\"inner\")\n\n# Left join\ndf1.join(df2, on=\"id\", how=\"left\")\n\n# Join on different column names\ndf1.join(df2, left_on=\"user_id\", right_on=\"id\")\n```\n\n### Concatenation\nStack DataFrames:\n```python\n# Vertical (stack rows)\npl.concat([df1, df2], how=\"vertical\")\n\n# Horizontal (add columns)\npl.concat([df1, df2], how=\"horizontal\")\n\n# Diagonal (union with different schemas)\npl.concat([df1, df2], how=\"diagonal\")\n```\n\n### Pivot and Unpivot\nReshape data:\n```python\n# Pivot (wide format)\ndf.pivot(values=\"sales\", index=\"date\", columns=\"product\")\n\n# Unpivot (long format)\ndf.unpivot(index=\"id\", on=[\"col1\", \"col2\"])\n```\n\nFor detailed transformation examples, load `references\u002Ftransformations.md`.\n\n## Pandas Migration\n\nPolars offers significant performance improvements over pandas with a cleaner API. Key differences:\n\n### Conceptual Differences\n- **No index**: Polars uses integer positions only\n- **Strict typing**: No silent type conversions\n- **Lazy evaluation**: Available via LazyFrame\n- **Parallel by default**: Operations parallelized automatically\n\n### Common Operation Mappings\n\n| Operation | Pandas | Polars |\n|-----------|--------|--------|\n| Select column | `df[\"col\"]` | `df.select(\"col\")` |\n| Filter | `df[df[\"col\"] > 10]` | `df.filter(pl.col(\"col\") > 10)` |\n| Add column | `df.assign(x=...)` | `df.with_columns(x=...)` |\n| Group by | `df.groupby(\"col\").agg(...)` | `df.group_by(\"col\").agg(...)` |\n| Window | `df.groupby(\"col\").transform(...)` | `df.with_columns(...).over(\"col\")` |\n\n### Key Syntax Patterns\n\n**Pandas sequential (slow):**\n```python\ndf.assign(\n    col_a=lambda df_: df_.value * 10,\n    col_b=lambda df_: df_.value * 100\n)\n```\n\n**Polars parallel (fast):**\n```python\ndf.with_columns(\n    col_a=pl.col(\"value\") * 10,\n    col_b=pl.col(\"value\") * 100,\n)\n```\n\nFor comprehensive migration guide, load `references\u002Fpandas_migration.md`.\n\n## Best Practices\n\n### Performance Optimization\n\n1. **Use lazy evaluation for large datasets:**\n   ```python\n   lf = pl.scan_csv(\"large.csv\")  # Don't use read_csv\n   result = lf.filter(...).select(...).collect()\n   ```\n\n2. **Avoid Python functions in hot paths:**\n   - Stay within expression API for parallelization\n   - Use `.map_elements()` only when necessary\n   - Prefer native Polars operations\n\n3. **Use streaming for very large data:**\n   ```python\n   lf.collect(streaming=True)\n   ```\n\n4. **Select only needed columns early:**\n   ```python\n   # Good: Select columns early\n   lf.select(\"col1\", \"col2\").filter(...)\n\n   # Bad: Filter on all columns first\n   lf.filter(...).select(\"col1\", \"col2\")\n   ```\n\n5. **Use appropriate data types:**\n   - Categorical for low-cardinality strings\n   - Appropriate integer sizes (i32 vs i64)\n   - Date types for temporal data\n\n### Expression Patterns\n\n**Conditional operations:**\n```python\npl.when(condition).then(value).otherwise(other_value)\n```\n\n**Column operations across multiple columns:**\n```python\ndf.select(pl.col(\"^.*_value$\") * 2)  # Regex pattern\n```\n\n**Null handling:**\n```python\npl.col(\"x\").fill_null(0)\npl.col(\"x\").is_null()\npl.col(\"x\").drop_nulls()\n```\n\nFor additional best practices and patterns, load `references\u002Fbest_practices.md`.\n\n## Resources\n\nThis skill includes comprehensive reference documentation:\n\n### references\u002F\n- `core_concepts.md` - Detailed explanations of expressions, lazy evaluation, and type system\n- `operations.md` - Comprehensive guide to all common operations with examples\n- `pandas_migration.md` - Complete migration guide from pandas to Polars\n- `io_guide.md` - Data I\u002FO operations for all supported formats\n- `transformations.md` - Joins, concatenation, pivots, and reshaping operations\n- `best_practices.md` - Performance optimization tips and common patterns\n\nLoad these references as needed when users require detailed information about specific topics.\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars",3909,"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","2026-11-11 11:53:30",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add davila7\u002Fclaude-code-templates --skill polars",{"tool":67,"command":68},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill polars",{"tool":70,"command":71},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill polars","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002FSKILL.md",true,[75,81,85,89,93,97],{"path":76,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":79,"sourceHtmlUrl":80},"references\u002Fbest_practices.md","REFERENCE","参考文档",14181,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002Freferences\u002Fbest_practices.md",{"path":82,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":83,"sourceHtmlUrl":84},"references\u002Fcore_concepts.md",8348,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002Freferences\u002Fcore_concepts.md",{"path":86,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":87,"sourceHtmlUrl":88},"references\u002Fio_guide.md",11208,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002Freferences\u002Fio_guide.md",{"path":90,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Foperations.md",12080,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002Freferences\u002Foperations.md",{"path":94,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fpandas_migration.md",12353,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002Freferences\u002Fpandas_migration.md",{"path":98,"resourceType":77,"resourceTypeDesc":78,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Ftransformations.md",11183,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fpolars\u002Freferences\u002Ftransformations.md",[102,131,158,191,226,250],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":111,"publishedAt":46},"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",[108],{"categoryNo":109,"categoryName":110,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[112,113,114,115,116,26,117,118,119,120,121,122,123,124,125,126,127,128,129,130],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":138,"publishedAt":46},"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",[137],{"categoryNo":109,"categoryName":110,"taxonomyType":21,"taxonomyTypeDesc":22},[139,140,141,142,143,144,145,146,147,148,149,120,150,151,152,153,154,155,156,157],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":173,"publishedAt":46},"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",[164,167,168],{"categoryNo":165,"categoryName":166,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":109,"categoryName":110,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":169,"categoryName":170,"taxonomyType":171,"taxonomyTypeDesc":172},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[174,175,176,177,178,179,180,26,181,182,183,184,185,186,187,119,188,189,120,190],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"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":195,"licenseFileUrl":196,"sourceHtmlUrl":197,"categories":198,"keywords":202,"publishedAt":46},"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",[199],{"categoryNo":200,"categoryName":201,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,26,225],"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":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":195,"licenseFileUrl":230,"sourceHtmlUrl":231,"categories":232,"keywords":237,"publishedAt":46},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[233,236],{"categoryNo":234,"categoryName":235,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":200,"categoryName":201,"taxonomyType":21,"taxonomyTypeDesc":22},[238,239,240,241,242,204,243,213,214,244,245,246,247,26,248,122,249],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":251,"name":252,"nameZh":6,"description":253,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":195,"licenseFileUrl":254,"sourceHtmlUrl":255,"categories":256,"keywords":264,"publishedAt":46},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[257,260,261],{"categoryNo":258,"categoryName":259,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":109,"categoryName":110,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":262,"categoryName":263,"taxonomyType":171,"taxonomyTypeDesc":172},"01JBSEEDCAT000000000000036","内容创作者",[265,266,267,268,269,270,35,271,206,272,216,150,273,274,275,276,277,186,278,120,279,34,280,281,282,128,283],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]