[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JM4Z03CZP7FSHRW6TT6YBB":3,"skill-same:01M6JM4Z03CZP7FSHRW6TT6YBB":74},{"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":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":41,"repositoryHtmlUrl":42,"repositoryHomepageUrl":6,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M6JM4Z03CZP7FSHRW6TT6YBB","seaborn",null,"Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.","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\u002Fseaborn\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],"visualization","correlation","analysis","box","regression","pair","matrices","figures","for","2026-11-10 10:12:30",false,"---\nname: seaborn\ndescription: \"Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.\"\n---\n\n# Seaborn Statistical Visualization\n\n## Overview\n\nSeaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.\n\n## Design Philosophy\n\nSeaborn follows these core principles:\n\n1. **Dataset-oriented**: Work directly with DataFrames and named variables rather than abstract coordinates\n2. **Semantic mapping**: Automatically translate data values into visual properties (colors, sizes, styles)\n3. **Statistical awareness**: Built-in aggregation, error estimation, and confidence intervals\n4. **Aesthetic defaults**: Publication-ready themes and color palettes out of the box\n5. **Matplotlib integration**: Full compatibility with matplotlib customization when needed\n\n## Quick Start\n\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Load example dataset\ndf = sns.load_dataset('tips')\n\n# Create a simple visualization\nsns.scatterplot(data=df, x='total_bill', y='tip', hue='day')\nplt.show()\n```\n\n## Core Plotting Interfaces\n\n### Function Interface (Traditional)\n\nThe function interface provides specialized plotting functions organized by visualization type. Each category has **axes-level** functions (plot to single axes) and **figure-level** functions (manage entire figure with faceting).\n\n**When to use:**\n- Quick exploratory analysis\n- Single-purpose visualizations\n- When you need a specific plot type\n\n### Objects Interface (Modern)\n\nThe `seaborn.objects` interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales.\n\n**When to use:**\n- Complex layered visualizations\n- When you need fine-grained control over transformations\n- Building custom plot types\n- Programmatic plot generation\n\n```python\nfrom seaborn import objects as so\n\n# Declarative syntax\n(\n    so.Plot(data=df, x='total_bill', y='tip')\n    .add(so.Dot(), color='day')\n    .add(so.Line(), so.PolyFit())\n)\n```\n\n## Plotting Functions by Category\n\n### Relational Plots (Relationships Between Variables)\n\n**Use for:** Exploring how two or more variables relate to each other\n\n- `scatterplot()` - Display individual observations as points\n- `lineplot()` - Show trends and changes (automatically aggregates and computes CI)\n- `relplot()` - Figure-level interface with automatic faceting\n\n**Key parameters:**\n- `x`, `y` - Primary variables\n- `hue` - Color encoding for additional categorical\u002Fcontinuous variable\n- `size` - Point\u002Fline size encoding\n- `style` - Marker\u002Fline style encoding\n- `col`, `row` - Facet into multiple subplots (figure-level only)\n\n```python\n# Scatter with multiple semantic mappings\nsns.scatterplot(data=df, x='total_bill', y='tip',\n                hue='time', size='size', style='sex')\n\n# Line plot with confidence intervals\nsns.lineplot(data=timeseries, x='date', y='value', hue='category')\n\n# Faceted relational plot\nsns.relplot(data=df, x='total_bill', y='tip',\n            col='time', row='sex', hue='smoker', kind='scatter')\n```\n\n### Distribution Plots (Single and Bivariate Distributions)\n\n**Use for:** Understanding data spread, shape, and probability density\n\n- `histplot()` - Bar-based frequency distributions with flexible binning\n- `kdeplot()` - Smooth density estimates using Gaussian kernels\n- `ecdfplot()` - Empirical cumulative distribution (no parameters to tune)\n- `rugplot()` - Individual observation tick marks\n- `displot()` - Figure-level interface for univariate and bivariate distributions\n- `jointplot()` - Bivariate plot with marginal distributions\n- `pairplot()` - Matrix of pairwise relationships across dataset\n\n**Key parameters:**\n- `x`, `y` - Variables (y optional for univariate)\n- `hue` - Separate distributions by category\n- `stat` - Normalization: \"count\", \"frequency\", \"probability\", \"density\"\n- `bins` \u002F `binwidth` - Histogram binning control\n- `bw_adjust` - KDE bandwidth multiplier (higher = smoother)\n- `fill` - Fill area under curve\n- `multiple` - How to handle hue: \"layer\", \"stack\", \"dodge\", \"fill\"\n\n```python\n# Histogram with density normalization\nsns.histplot(data=df, x='total_bill', hue='time',\n             stat='density', multiple='stack')\n\n# Bivariate KDE with contours\nsns.kdeplot(data=df, x='total_bill', y='tip',\n            fill=True, levels=5, thresh=0.1)\n\n# Joint plot with marginals\nsns.jointplot(data=df, x='total_bill', y='tip',\n              kind='scatter', hue='time')\n\n# Pairwise relationships\nsns.pairplot(data=df, hue='species', corner=True)\n```\n\n### Categorical Plots (Comparisons Across Categories)\n\n**Use for:** Comparing distributions or statistics across discrete categories\n\n**Categorical scatterplots:**\n- `stripplot()` - Points with jitter to show all observations\n- `swarmplot()` - Non-overlapping points (beeswarm algorithm)\n\n**Distribution comparisons:**\n- `boxplot()` - Quartiles and outliers\n- `violinplot()` - KDE + quartile information\n- `boxenplot()` - Enhanced boxplot for larger datasets\n\n**Statistical estimates:**\n- `barplot()` - Mean\u002Faggregate with confidence intervals\n- `pointplot()` - Point estimates with connecting lines\n- `countplot()` - Count of observations per category\n\n**Figure-level:**\n- `catplot()` - Faceted categorical plots (set `kind` parameter)\n\n**Key parameters:**\n- `x`, `y` - Variables (one typically categorical)\n- `hue` - Additional categorical grouping\n- `order`, `hue_order` - Control category ordering\n- `dodge` - Separate hue levels side-by-side\n- `orient` - \"v\" (vertical) or \"h\" (horizontal)\n- `kind` - Plot type for catplot: \"strip\", \"swarm\", \"box\", \"violin\", \"bar\", \"point\"\n\n```python\n# Swarm plot showing all points\nsns.swarmplot(data=df, x='day', y='total_bill', hue='sex')\n\n# Violin plot with split for comparison\nsns.violinplot(data=df, x='day', y='total_bill',\n               hue='sex', split=True)\n\n# Bar plot with error bars\nsns.barplot(data=df, x='day', y='total_bill',\n            hue='sex', estimator='mean', errorbar='ci')\n\n# Faceted categorical plot\nsns.catplot(data=df, x='day', y='total_bill',\n            col='time', kind='box')\n```\n\n### Regression Plots (Linear Relationships)\n\n**Use for:** Visualizing linear regressions and residuals\n\n- `regplot()` - Axes-level regression plot with scatter + fit line\n- `lmplot()` - Figure-level with faceting support\n- `residplot()` - Residual plot for assessing model fit\n\n**Key parameters:**\n- `x`, `y` - Variables to regress\n- `order` - Polynomial regression order\n- `logistic` - Fit logistic regression\n- `robust` - Use robust regression (less sensitive to outliers)\n- `ci` - Confidence interval width (default 95)\n- `scatter_kws`, `line_kws` - Customize scatter and line properties\n\n```python\n# Simple linear regression\nsns.regplot(data=df, x='total_bill', y='tip')\n\n# Polynomial regression with faceting\nsns.lmplot(data=df, x='total_bill', y='tip',\n           col='time', order=2, ci=95)\n\n# Check residuals\nsns.residplot(data=df, x='total_bill', y='tip')\n```\n\n### Matrix Plots (Rectangular Data)\n\n**Use for:** Visualizing matrices, correlations, and grid-structured data\n\n- `heatmap()` - Color-encoded matrix with annotations\n- `clustermap()` - Hierarchically-clustered heatmap\n\n**Key parameters:**\n- `data` - 2D rectangular dataset (DataFrame or array)\n- `annot` - Display values in cells\n- `fmt` - Format string for annotations (e.g., \".2f\")\n- `cmap` - Colormap name\n- `center` - Value at colormap center (for diverging colormaps)\n- `vmin`, `vmax` - Color scale limits\n- `square` - Force square cells\n- `linewidths` - Gap between cells\n\n```python\n# Correlation heatmap\ncorr = df.corr()\nsns.heatmap(corr, annot=True, fmt='.2f',\n            cmap='coolwarm', center=0, square=True)\n\n# Clustered heatmap\nsns.clustermap(data, cmap='viridis',\n               standard_scale=1, figsize=(10, 10))\n```\n\n## Multi-Plot Grids\n\nSeaborn provides grid objects for creating complex multi-panel figures:\n\n### FacetGrid\n\nCreate subplots based on categorical variables. Most useful when called through figure-level functions (`relplot`, `displot`, `catplot`), but can be used directly for custom plots.\n\n```python\ng = sns.FacetGrid(df, col='time', row='sex', hue='smoker')\ng.map(sns.scatterplot, 'total_bill', 'tip')\ng.add_legend()\n```\n\n### PairGrid\n\nShow pairwise relationships between all variables in a dataset.\n\n```python\ng = sns.PairGrid(df, hue='species')\ng.map_upper(sns.scatterplot)\ng.map_lower(sns.kdeplot)\ng.map_diag(sns.histplot)\ng.add_legend()\n```\n\n### JointGrid\n\nCombine bivariate plot with marginal distributions.\n\n```python\ng = sns.JointGrid(data=df, x='total_bill', y='tip')\ng.plot_joint(sns.scatterplot)\ng.plot_marginals(sns.histplot)\n```\n\n## Figure-Level vs Axes-Level Functions\n\nUnderstanding this distinction is crucial for effective seaborn usage:\n\n### Axes-Level Functions\n- Plot to a single matplotlib `Axes` object\n- Integrate easily into complex matplotlib figures\n- Accept `ax=` parameter for precise placement\n- Return `Axes` object\n- Examples: `scatterplot`, `histplot`, `boxplot`, `regplot`, `heatmap`\n\n**When to use:**\n- Building custom multi-plot layouts\n- Combining different plot types\n- Need matplotlib-level control\n- Integrating with existing matplotlib code\n\n```python\nfig, axes = plt.subplots(2, 2, figsize=(10, 10))\nsns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0])\nsns.histplot(data=df, x='x', ax=axes[0, 1])\nsns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0])\nsns.kdeplot(data=df, x='x', y='y', ax=axes[1, 1])\n```\n\n### Figure-Level Functions\n- Manage entire figure including all subplots\n- Built-in faceting via `col` and `row` parameters\n- Return `FacetGrid`, `JointGrid`, or `PairGrid` objects\n- Use `height` and `aspect` for sizing (per subplot)\n- Cannot be placed in existing figure\n- Examples: `relplot`, `displot`, `catplot`, `lmplot`, `jointplot`, `pairplot`\n\n**When to use:**\n- Faceted visualizations (small multiples)\n- Quick exploratory analysis\n- Consistent multi-panel layouts\n- Don't need to combine with other plot types\n\n```python\n# Automatic faceting\nsns.relplot(data=df, x='x', y='y', col='category', row='group',\n            hue='type', height=3, aspect=1.2)\n```\n\n## Data Structure Requirements\n\n### Long-Form Data (Preferred)\n\nEach variable is a column, each observation is a row. This \"tidy\" format provides maximum flexibility:\n\n```python\n# Long-form structure\n   subject  condition  measurement\n0        1    control         10.5\n1        1  treatment         12.3\n2        2    control          9.8\n3        2  treatment         13.1\n```\n\n**Advantages:**\n- Works with all seaborn functions\n- Easy to remap variables to visual properties\n- Supports arbitrary complexity\n- Natural for DataFrame operations\n\n### Wide-Form Data\n\nVariables are spread across columns. Useful for simple rectangular data:\n\n```python\n# Wide-form structure\n   control  treatment\n0     10.5       12.3\n1      9.8       13.1\n```\n\n**Use cases:**\n- Simple time series\n- Correlation matrices\n- Heatmaps\n- Quick plots of array data\n\n**Converting wide to long:**\n```python\ndf_long = df.melt(var_name='condition', value_name='measurement')\n```\n\n## Color Palettes\n\nSeaborn provides carefully designed color palettes for different data types:\n\n### Qualitative Palettes (Categorical Data)\n\nDistinguish categories through hue variation:\n- `\"deep\"` - Default, vivid colors\n- `\"muted\"` - Softer, less saturated\n- `\"pastel\"` - Light, desaturated\n- `\"bright\"` - Highly saturated\n- `\"dark\"` - Dark values\n- `\"colorblind\"` - Safe for color vision deficiency\n\n```python\nsns.set_palette(\"colorblind\")\nsns.color_palette(\"Set2\")\n```\n\n### Sequential Palettes (Ordered Data)\n\nShow progression from low to high values:\n- `\"rocket\"`, `\"mako\"` - Wide luminance range (good for heatmaps)\n- `\"flare\"`, `\"crest\"` - Restricted luminance (good for points\u002Flines)\n- `\"viridis\"`, `\"magma\"`, `\"plasma\"` - Matplotlib perceptually uniform\n\n```python\nsns.heatmap(data, cmap='rocket')\nsns.kdeplot(data=df, x='x', y='y', cmap='mako', fill=True)\n```\n\n### Diverging Palettes (Centered Data)\n\nEmphasize deviations from a midpoint:\n- `\"vlag\"` - Blue to red\n- `\"icefire\"` - Blue to orange\n- `\"coolwarm\"` - Cool to warm\n- `\"Spectral\"` - Rainbow diverging\n\n```python\nsns.heatmap(correlation_matrix, cmap='vlag', center=0)\n```\n\n### Custom Palettes\n\n```python\n# Create custom palette\ncustom = sns.color_palette(\"husl\", 8)\n\n# Light to dark gradient\npalette = sns.light_palette(\"seagreen\", as_cmap=True)\n\n# Diverging palette from hues\npalette = sns.diverging_palette(250, 10, as_cmap=True)\n```\n\n## Theming and Aesthetics\n\n### Set Theme\n\n`set_theme()` controls overall appearance:\n\n```python\n# Set complete theme\nsns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')\n\n# Reset to defaults\nsns.set_theme()\n```\n\n### Styles\n\nControl background and grid appearance:\n- `\"darkgrid\"` - Gray background with white grid (default)\n- `\"whitegrid\"` - White background with gray grid\n- `\"dark\"` - Gray background, no grid\n- `\"white\"` - White background, no grid\n- `\"ticks\"` - White background with axis ticks\n\n```python\nsns.set_style(\"whitegrid\")\n\n# Remove spines\nsns.despine(left=False, bottom=False, offset=10, trim=True)\n\n# Temporary style\nwith sns.axes_style(\"white\"):\n    sns.scatterplot(data=df, x='x', y='y')\n```\n\n### Contexts\n\nScale elements for different use cases:\n- `\"paper\"` - Smallest (default)\n- `\"notebook\"` - Slightly larger\n- `\"talk\"` - Presentation slides\n- `\"poster\"` - Large format\n\n```python\nsns.set_context(\"talk\", font_scale=1.2)\n\n# Temporary context\nwith sns.plotting_context(\"poster\"):\n    sns.barplot(data=df, x='category', y='value')\n```\n\n## Best Practices\n\n### 1. Data Preparation\n\nAlways use well-structured DataFrames with meaningful column names:\n\n```python\n# Good: Named columns in DataFrame\ndf = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days})\nsns.scatterplot(data=df, x='bill', y='tip', hue='day')\n\n# Avoid: Unnamed arrays\nsns.scatterplot(x=x_array, y=y_array)  # Loses axis labels\n```\n\n### 2. Choose the Right Plot Type\n\n**Continuous x, continuous y:** `scatterplot`, `lineplot`, `kdeplot`, `regplot`\n**Continuous x, categorical y:** `violinplot`, `boxplot`, `stripplot`, `swarmplot`\n**One continuous variable:** `histplot`, `kdeplot`, `ecdfplot`\n**Correlations\u002Fmatrices:** `heatmap`, `clustermap`\n**Pairwise relationships:** `pairplot`, `jointplot`\n\n### 3. Use Figure-Level Functions for Faceting\n\n```python\n# Instead of manual subplot creation\nsns.relplot(data=df, x='x', y='y', col='category', col_wrap=3)\n\n# Not: Creating subplots manually for simple faceting\n```\n\n### 4. Leverage Semantic Mappings\n\nUse `hue`, `size`, and `style` to encode additional dimensions:\n\n```python\nsns.scatterplot(data=df, x='x', y='y',\n                hue='category',      # Color by category\n                size='importance',    # Size by continuous variable\n                style='type')         # Marker style by type\n```\n\n### 5. Control Statistical Estimation\n\nMany functions compute statistics automatically. Understand and customize:\n\n```python\n# Lineplot computes mean and 95% CI by default\nsns.lineplot(data=df, x='time', y='value',\n             errorbar='sd')  # Use standard deviation instead\n\n# Barplot computes mean by default\nsns.barplot(data=df, x='category', y='value',\n            estimator='median',  # Use median instead\n            errorbar=('ci', 95))  # Bootstrapped CI\n```\n\n### 6. Combine with Matplotlib\n\nSeaborn integrates seamlessly with matplotlib for fine-tuning:\n\n```python\nax = sns.scatterplot(data=df, x='x', y='y')\nax.set(xlabel='Custom X Label', ylabel='Custom Y Label',\n       title='Custom Title')\nax.axhline(y=0, color='r', linestyle='--')\nplt.tight_layout()\n```\n\n### 7. Save High-Quality Figures\n\n```python\nfig = sns.relplot(data=df, x='x', y='y', col='group')\nfig.savefig('figure.png', dpi=300, bbox_inches='tight')\nfig.savefig('figure.pdf')  # Vector format for publications\n```\n\n## Common Patterns\n\n### Exploratory Data Analysis\n\n```python\n# Quick overview of all relationships\nsns.pairplot(data=df, hue='target', corner=True)\n\n# Distribution exploration\nsns.displot(data=df, x='variable', hue='group',\n            kind='kde', fill=True, col='category')\n\n# Correlation analysis\ncorr = df.corr()\nsns.heatmap(corr, annot=True, cmap='coolwarm', center=0)\n```\n\n### Publication-Quality Figures\n\n```python\nsns.set_theme(style='ticks', context='paper', font_scale=1.1)\n\ng = sns.catplot(data=df, x='treatment', y='response',\n                col='cell_line', kind='box', height=3, aspect=1.2)\ng.set_axis_labels('Treatment Condition', 'Response (μM)')\ng.set_titles('{col_name}')\nsns.despine(trim=True)\n\ng.savefig('figure.pdf', dpi=300, bbox_inches='tight')\n```\n\n### Complex Multi-Panel Figures\n\n```python\n# Using matplotlib subplots with seaborn\nfig, axes = plt.subplots(2, 2, figsize=(12, 10))\n\nsns.scatterplot(data=df, x='x1', y='y', hue='group', ax=axes[0, 0])\nsns.histplot(data=df, x='x1', hue='group', ax=axes[0, 1])\nsns.violinplot(data=df, x='group', y='y', ax=axes[1, 0])\nsns.heatmap(df.pivot_table(values='y', index='x1', columns='x2'),\n            ax=axes[1, 1], cmap='viridis')\n\nplt.tight_layout()\n```\n\n### Time Series with Confidence Bands\n\n```python\n# Lineplot automatically aggregates and shows CI\nsns.lineplot(data=timeseries, x='date', y='measurement',\n             hue='sensor', style='location', errorbar='sd')\n\n# For more control\ng = sns.relplot(data=timeseries, x='date', y='measurement',\n                col='location', hue='sensor', kind='line',\n                height=4, aspect=1.5, errorbar=('ci', 95))\ng.set_axis_labels('Date', 'Measurement (units)')\n```\n\n## Troubleshooting\n\n### Issue: Legend Outside Plot Area\n\nFigure-level functions place legends outside by default. To move inside:\n\n```python\ng = sns.relplot(data=df, x='x', y='y', hue='category')\ng._legend.set_bbox_to_anchor((0.9, 0.5))  # Adjust position\n```\n\n### Issue: Overlapping Labels\n\n```python\nplt.xticks(rotation=45, ha='right')\nplt.tight_layout()\n```\n\n### Issue: Figure Too Small\n\nFor figure-level functions:\n```python\nsns.relplot(data=df, x='x', y='y', height=6, aspect=1.5)\n```\n\nFor axes-level functions:\n```python\nfig, ax = plt.subplots(figsize=(10, 6))\nsns.scatterplot(data=df, x='x', y='y', ax=ax)\n```\n\n### Issue: Colors Not Distinct Enough\n\n```python\n# Use a different palette\nsns.set_palette(\"bright\")\n\n# Or specify number of colors\npalette = sns.color_palette(\"husl\", n_colors=len(df['category'].unique()))\nsns.scatterplot(data=df, x='x', y='y', hue='category', palette=palette)\n```\n\n### Issue: KDE Too Smooth or Jagged\n\n```python\n# Adjust bandwidth\nsns.kdeplot(data=df, x='x', bw_adjust=0.5)  # Less smooth\nsns.kdeplot(data=df, x='x', bw_adjust=2)    # More smooth\n```\n\n## Resources\n\nThis skill includes reference materials for deeper exploration:\n\n### references\u002F\n\n- `function_reference.md` - Comprehensive listing of all seaborn functions with parameters and examples\n- `objects_interface.md` - Detailed guide to the modern seaborn.objects API\n- `examples.md` - Common use cases and code patterns for different analysis scenarios\n\nLoad reference files as needed for detailed function signatures, advanced parameters, or specific examples.\n","# Seaborn Statistical Visualization\n\n## Overview\n\nSeaborn is a Python visualization library for creating publication-quality statistical graphics. Use this skill for dataset-oriented plotting, multivariate analysis, automatic statistical estimation, and complex multi-panel figures with minimal code.\n\n## Design Philosophy\n\nSeaborn follows these core principles:\n\n1. **Dataset-oriented**: Work directly with DataFrames and named variables rather than abstract coordinates\n2. **Semantic mapping**: Automatically translate data values into visual properties (colors, sizes, styles)\n3. **Statistical awareness**: Built-in aggregation, error estimation, and confidence intervals\n4. **Aesthetic defaults**: Publication-ready themes and color palettes out of the box\n5. **Matplotlib integration**: Full compatibility with matplotlib customization when needed\n\n## Quick Start\n\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Load example dataset\ndf = sns.load_dataset('tips')\n\n# Create a simple visualization\nsns.scatterplot(data=df, x='total_bill', y='tip', hue='day')\nplt.show()\n```\n\n## Core Plotting Interfaces\n\n### Function Interface (Traditional)\n\nThe function interface provides specialized plotting functions organized by visualization type. Each category has **axes-level** functions (plot to single axes) and **figure-level** functions (manage entire figure with faceting).\n\n**When to use:**\n- Quick exploratory analysis\n- Single-purpose visualizations\n- When you need a specific plot type\n\n### Objects Interface (Modern)\n\nThe `seaborn.objects` interface provides a declarative, composable API similar to ggplot2. Build visualizations by chaining methods to specify data mappings, marks, transformations, and scales.\n\n**When to use:**\n- Complex layered visualizations\n- When you need fine-grained control over transformations\n- Building custom plot types\n- Programmatic plot generation\n\n```python\nfrom seaborn import objects as so\n\n# Declarative syntax\n(\n    so.Plot(data=df, x='total_bill', y='tip')\n    .add(so.Dot(), color='day')\n    .add(so.Line(), so.PolyFit())\n)\n```\n\n## Plotting Functions by Category\n\n### Relational Plots (Relationships Between Variables)\n\n**Use for:** Exploring how two or more variables relate to each other\n\n- `scatterplot()` - Display individual observations as points\n- `lineplot()` - Show trends and changes (automatically aggregates and computes CI)\n- `relplot()` - Figure-level interface with automatic faceting\n\n**Key parameters:**\n- `x`, `y` - Primary variables\n- `hue` - Color encoding for additional categorical\u002Fcontinuous variable\n- `size` - Point\u002Fline size encoding\n- `style` - Marker\u002Fline style encoding\n- `col`, `row` - Facet into multiple subplots (figure-level only)\n\n```python\n# Scatter with multiple semantic mappings\nsns.scatterplot(data=df, x='total_bill', y='tip',\n                hue='time', size='size', style='sex')\n\n# Line plot with confidence intervals\nsns.lineplot(data=timeseries, x='date', y='value', hue='category')\n\n# Faceted relational plot\nsns.relplot(data=df, x='total_bill', y='tip',\n            col='time', row='sex', hue='smoker', kind='scatter')\n```\n\n### Distribution Plots (Single and Bivariate Distributions)\n\n**Use for:** Understanding data spread, shape, and probability density\n\n- `histplot()` - Bar-based frequency distributions with flexible binning\n- `kdeplot()` - Smooth density estimates using Gaussian kernels\n- `ecdfplot()` - Empirical cumulative distribution (no parameters to tune)\n- `rugplot()` - Individual observation tick marks\n- `displot()` - Figure-level interface for univariate and bivariate distributions\n- `jointplot()` - Bivariate plot with marginal distributions\n- `pairplot()` - Matrix of pairwise relationships across dataset\n\n**Key parameters:**\n- `x`, `y` - Variables (y optional for univariate)\n- `hue` - Separate distributions by category\n- `stat` - Normalization: \"count\", \"frequency\", \"probability\", \"density\"\n- `bins` \u002F `binwidth` - Histogram binning control\n- `bw_adjust` - KDE bandwidth multiplier (higher = smoother)\n- `fill` - Fill area under curve\n- `multiple` - How to handle hue: \"layer\", \"stack\", \"dodge\", \"fill\"\n\n```python\n# Histogram with density normalization\nsns.histplot(data=df, x='total_bill', hue='time',\n             stat='density', multiple='stack')\n\n# Bivariate KDE with contours\nsns.kdeplot(data=df, x='total_bill', y='tip',\n            fill=True, levels=5, thresh=0.1)\n\n# Joint plot with marginals\nsns.jointplot(data=df, x='total_bill', y='tip',\n              kind='scatter', hue='time')\n\n# Pairwise relationships\nsns.pairplot(data=df, hue='species', corner=True)\n```\n\n### Categorical Plots (Comparisons Across Categories)\n\n**Use for:** Comparing distributions or statistics across discrete categories\n\n**Categorical scatterplots:**\n- `stripplot()` - Points with jitter to show all observations\n- `swarmplot()` - Non-overlapping points (beeswarm algorithm)\n\n**Distribution comparisons:**\n- `boxplot()` - Quartiles and outliers\n- `violinplot()` - KDE + quartile information\n- `boxenplot()` - Enhanced boxplot for larger datasets\n\n**Statistical estimates:**\n- `barplot()` - Mean\u002Faggregate with confidence intervals\n- `pointplot()` - Point estimates with connecting lines\n- `countplot()` - Count of observations per category\n\n**Figure-level:**\n- `catplot()` - Faceted categorical plots (set `kind` parameter)\n\n**Key parameters:**\n- `x`, `y` - Variables (one typically categorical)\n- `hue` - Additional categorical grouping\n- `order`, `hue_order` - Control category ordering\n- `dodge` - Separate hue levels side-by-side\n- `orient` - \"v\" (vertical) or \"h\" (horizontal)\n- `kind` - Plot type for catplot: \"strip\", \"swarm\", \"box\", \"violin\", \"bar\", \"point\"\n\n```python\n# Swarm plot showing all points\nsns.swarmplot(data=df, x='day', y='total_bill', hue='sex')\n\n# Violin plot with split for comparison\nsns.violinplot(data=df, x='day', y='total_bill',\n               hue='sex', split=True)\n\n# Bar plot with error bars\nsns.barplot(data=df, x='day', y='total_bill',\n            hue='sex', estimator='mean', errorbar='ci')\n\n# Faceted categorical plot\nsns.catplot(data=df, x='day', y='total_bill',\n            col='time', kind='box')\n```\n\n### Regression Plots (Linear Relationships)\n\n**Use for:** Visualizing linear regressions and residuals\n\n- `regplot()` - Axes-level regression plot with scatter + fit line\n- `lmplot()` - Figure-level with faceting support\n- `residplot()` - Residual plot for assessing model fit\n\n**Key parameters:**\n- `x`, `y` - Variables to regress\n- `order` - Polynomial regression order\n- `logistic` - Fit logistic regression\n- `robust` - Use robust regression (less sensitive to outliers)\n- `ci` - Confidence interval width (default 95)\n- `scatter_kws`, `line_kws` - Customize scatter and line properties\n\n```python\n# Simple linear regression\nsns.regplot(data=df, x='total_bill', y='tip')\n\n# Polynomial regression with faceting\nsns.lmplot(data=df, x='total_bill', y='tip',\n           col='time', order=2, ci=95)\n\n# Check residuals\nsns.residplot(data=df, x='total_bill', y='tip')\n```\n\n### Matrix Plots (Rectangular Data)\n\n**Use for:** Visualizing matrices, correlations, and grid-structured data\n\n- `heatmap()` - Color-encoded matrix with annotations\n- `clustermap()` - Hierarchically-clustered heatmap\n\n**Key parameters:**\n- `data` - 2D rectangular dataset (DataFrame or array)\n- `annot` - Display values in cells\n- `fmt` - Format string for annotations (e.g., \".2f\")\n- `cmap` - Colormap name\n- `center` - Value at colormap center (for diverging colormaps)\n- `vmin`, `vmax` - Color scale limits\n- `square` - Force square cells\n- `linewidths` - Gap between cells\n\n```python\n# Correlation heatmap\ncorr = df.corr()\nsns.heatmap(corr, annot=True, fmt='.2f',\n            cmap='coolwarm', center=0, square=True)\n\n# Clustered heatmap\nsns.clustermap(data, cmap='viridis',\n               standard_scale=1, figsize=(10, 10))\n```\n\n## Multi-Plot Grids\n\nSeaborn provides grid objects for creating complex multi-panel figures:\n\n### FacetGrid\n\nCreate subplots based on categorical variables. Most useful when called through figure-level functions (`relplot`, `displot`, `catplot`), but can be used directly for custom plots.\n\n```python\ng = sns.FacetGrid(df, col='time', row='sex', hue='smoker')\ng.map(sns.scatterplot, 'total_bill', 'tip')\ng.add_legend()\n```\n\n### PairGrid\n\nShow pairwise relationships between all variables in a dataset.\n\n```python\ng = sns.PairGrid(df, hue='species')\ng.map_upper(sns.scatterplot)\ng.map_lower(sns.kdeplot)\ng.map_diag(sns.histplot)\ng.add_legend()\n```\n\n### JointGrid\n\nCombine bivariate plot with marginal distributions.\n\n```python\ng = sns.JointGrid(data=df, x='total_bill', y='tip')\ng.plot_joint(sns.scatterplot)\ng.plot_marginals(sns.histplot)\n```\n\n## Figure-Level vs Axes-Level Functions\n\nUnderstanding this distinction is crucial for effective seaborn usage:\n\n### Axes-Level Functions\n- Plot to a single matplotlib `Axes` object\n- Integrate easily into complex matplotlib figures\n- Accept `ax=` parameter for precise placement\n- Return `Axes` object\n- Examples: `scatterplot`, `histplot`, `boxplot`, `regplot`, `heatmap`\n\n**When to use:**\n- Building custom multi-plot layouts\n- Combining different plot types\n- Need matplotlib-level control\n- Integrating with existing matplotlib code\n\n```python\nfig, axes = plt.subplots(2, 2, figsize=(10, 10))\nsns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0])\nsns.histplot(data=df, x='x', ax=axes[0, 1])\nsns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0])\nsns.kdeplot(data=df, x='x', y='y', ax=axes[1, 1])\n```\n\n### Figure-Level Functions\n- Manage entire figure including all subplots\n- Built-in faceting via `col` and `row` parameters\n- Return `FacetGrid`, `JointGrid`, or `PairGrid` objects\n- Use `height` and `aspect` for sizing (per subplot)\n- Cannot be placed in existing figure\n- Examples: `relplot`, `displot`, `catplot`, `lmplot`, `jointplot`, `pairplot`\n\n**When to use:**\n- Faceted visualizations (small multiples)\n- Quick exploratory analysis\n- Consistent multi-panel layouts\n- Don't need to combine with other plot types\n\n```python\n# Automatic faceting\nsns.relplot(data=df, x='x', y='y', col='category', row='group',\n            hue='type', height=3, aspect=1.2)\n```\n\n## Data Structure Requirements\n\n### Long-Form Data (Preferred)\n\nEach variable is a column, each observation is a row. This \"tidy\" format provides maximum flexibility:\n\n```python\n# Long-form structure\n   subject  condition  measurement\n0        1    control         10.5\n1        1  treatment         12.3\n2        2    control          9.8\n3        2  treatment         13.1\n```\n\n**Advantages:**\n- Works with all seaborn functions\n- Easy to remap variables to visual properties\n- Supports arbitrary complexity\n- Natural for DataFrame operations\n\n### Wide-Form Data\n\nVariables are spread across columns. Useful for simple rectangular data:\n\n```python\n# Wide-form structure\n   control  treatment\n0     10.5       12.3\n1      9.8       13.1\n```\n\n**Use cases:**\n- Simple time series\n- Correlation matrices\n- Heatmaps\n- Quick plots of array data\n\n**Converting wide to long:**\n```python\ndf_long = df.melt(var_name='condition', value_name='measurement')\n```\n\n## Color Palettes\n\nSeaborn provides carefully designed color palettes for different data types:\n\n### Qualitative Palettes (Categorical Data)\n\nDistinguish categories through hue variation:\n- `\"deep\"` - Default, vivid colors\n- `\"muted\"` - Softer, less saturated\n- `\"pastel\"` - Light, desaturated\n- `\"bright\"` - Highly saturated\n- `\"dark\"` - Dark values\n- `\"colorblind\"` - Safe for color vision deficiency\n\n```python\nsns.set_palette(\"colorblind\")\nsns.color_palette(\"Set2\")\n```\n\n### Sequential Palettes (Ordered Data)\n\nShow progression from low to high values:\n- `\"rocket\"`, `\"mako\"` - Wide luminance range (good for heatmaps)\n- `\"flare\"`, `\"crest\"` - Restricted luminance (good for points\u002Flines)\n- `\"viridis\"`, `\"magma\"`, `\"plasma\"` - Matplotlib perceptually uniform\n\n```python\nsns.heatmap(data, cmap='rocket')\nsns.kdeplot(data=df, x='x', y='y', cmap='mako', fill=True)\n```\n\n### Diverging Palettes (Centered Data)\n\nEmphasize deviations from a midpoint:\n- `\"vlag\"` - Blue to red\n- `\"icefire\"` - Blue to orange\n- `\"coolwarm\"` - Cool to warm\n- `\"Spectral\"` - Rainbow diverging\n\n```python\nsns.heatmap(correlation_matrix, cmap='vlag', center=0)\n```\n\n### Custom Palettes\n\n```python\n# Create custom palette\ncustom = sns.color_palette(\"husl\", 8)\n\n# Light to dark gradient\npalette = sns.light_palette(\"seagreen\", as_cmap=True)\n\n# Diverging palette from hues\npalette = sns.diverging_palette(250, 10, as_cmap=True)\n```\n\n## Theming and Aesthetics\n\n### Set Theme\n\n`set_theme()` controls overall appearance:\n\n```python\n# Set complete theme\nsns.set_theme(style='whitegrid', palette='pastel', font='sans-serif')\n\n# Reset to defaults\nsns.set_theme()\n```\n\n### Styles\n\nControl background and grid appearance:\n- `\"darkgrid\"` - Gray background with white grid (default)\n- `\"whitegrid\"` - White background with gray grid\n- `\"dark\"` - Gray background, no grid\n- `\"white\"` - White background, no grid\n- `\"ticks\"` - White background with axis ticks\n\n```python\nsns.set_style(\"whitegrid\")\n\n# Remove spines\nsns.despine(left=False, bottom=False, offset=10, trim=True)\n\n# Temporary style\nwith sns.axes_style(\"white\"):\n    sns.scatterplot(data=df, x='x', y='y')\n```\n\n### Contexts\n\nScale elements for different use cases:\n- `\"paper\"` - Smallest (default)\n- `\"notebook\"` - Slightly larger\n- `\"talk\"` - Presentation slides\n- `\"poster\"` - Large format\n\n```python\nsns.set_context(\"talk\", font_scale=1.2)\n\n# Temporary context\nwith sns.plotting_context(\"poster\"):\n    sns.barplot(data=df, x='category', y='value')\n```\n\n## Best Practices\n\n### 1. Data Preparation\n\nAlways use well-structured DataFrames with meaningful column names:\n\n```python\n# Good: Named columns in DataFrame\ndf = pd.DataFrame({'bill': bills, 'tip': tips, 'day': days})\nsns.scatterplot(data=df, x='bill', y='tip', hue='day')\n\n# Avoid: Unnamed arrays\nsns.scatterplot(x=x_array, y=y_array)  # Loses axis labels\n```\n\n### 2. Choose the Right Plot Type\n\n**Continuous x, continuous y:** `scatterplot`, `lineplot`, `kdeplot`, `regplot`\n**Continuous x, categorical y:** `violinplot`, `boxplot`, `stripplot`, `swarmplot`\n**One continuous variable:** `histplot`, `kdeplot`, `ecdfplot`\n**Correlations\u002Fmatrices:** `heatmap`, `clustermap`\n**Pairwise relationships:** `pairplot`, `jointplot`\n\n### 3. Use Figure-Level Functions for Faceting\n\n```python\n# Instead of manual subplot creation\nsns.relplot(data=df, x='x', y='y', col='category', col_wrap=3)\n\n# Not: Creating subplots manually for simple faceting\n```\n\n### 4. Leverage Semantic Mappings\n\nUse `hue`, `size`, and `style` to encode additional dimensions:\n\n```python\nsns.scatterplot(data=df, x='x', y='y',\n                hue='category',      # Color by category\n                size='importance',    # Size by continuous variable\n                style='type')         # Marker style by type\n```\n\n### 5. Control Statistical Estimation\n\nMany functions compute statistics automatically. Understand and customize:\n\n```python\n# Lineplot computes mean and 95% CI by default\nsns.lineplot(data=df, x='time', y='value',\n             errorbar='sd')  # Use standard deviation instead\n\n# Barplot computes mean by default\nsns.barplot(data=df, x='category', y='value',\n            estimator='median',  # Use median instead\n            errorbar=('ci', 95))  # Bootstrapped CI\n```\n\n### 6. Combine with Matplotlib\n\nSeaborn integrates seamlessly with matplotlib for fine-tuning:\n\n```python\nax = sns.scatterplot(data=df, x='x', y='y')\nax.set(xlabel='Custom X Label', ylabel='Custom Y Label',\n       title='Custom Title')\nax.axhline(y=0, color='r', linestyle='--')\nplt.tight_layout()\n```\n\n### 7. Save High-Quality Figures\n\n```python\nfig = sns.relplot(data=df, x='x', y='y', col='group')\nfig.savefig('figure.png', dpi=300, bbox_inches='tight')\nfig.savefig('figure.pdf')  # Vector format for publications\n```\n\n## Common Patterns\n\n### Exploratory Data Analysis\n\n```python\n# Quick overview of all relationships\nsns.pairplot(data=df, hue='target', corner=True)\n\n# Distribution exploration\nsns.displot(data=df, x='variable', hue='group',\n            kind='kde', fill=True, col='category')\n\n# Correlation analysis\ncorr = df.corr()\nsns.heatmap(corr, annot=True, cmap='coolwarm', center=0)\n```\n\n### Publication-Quality Figures\n\n```python\nsns.set_theme(style='ticks', context='paper', font_scale=1.1)\n\ng = sns.catplot(data=df, x='treatment', y='response',\n                col='cell_line', kind='box', height=3, aspect=1.2)\ng.set_axis_labels('Treatment Condition', 'Response (μM)')\ng.set_titles('{col_name}')\nsns.despine(trim=True)\n\ng.savefig('figure.pdf', dpi=300, bbox_inches='tight')\n```\n\n### Complex Multi-Panel Figures\n\n```python\n# Using matplotlib subplots with seaborn\nfig, axes = plt.subplots(2, 2, figsize=(12, 10))\n\nsns.scatterplot(data=df, x='x1', y='y', hue='group', ax=axes[0, 0])\nsns.histplot(data=df, x='x1', hue='group', ax=axes[0, 1])\nsns.violinplot(data=df, x='group', y='y', ax=axes[1, 0])\nsns.heatmap(df.pivot_table(values='y', index='x1', columns='x2'),\n            ax=axes[1, 1], cmap='viridis')\n\nplt.tight_layout()\n```\n\n### Time Series with Confidence Bands\n\n```python\n# Lineplot automatically aggregates and shows CI\nsns.lineplot(data=timeseries, x='date', y='measurement',\n             hue='sensor', style='location', errorbar='sd')\n\n# For more control\ng = sns.relplot(data=timeseries, x='date', y='measurement',\n                col='location', hue='sensor', kind='line',\n                height=4, aspect=1.5, errorbar=('ci', 95))\ng.set_axis_labels('Date', 'Measurement (units)')\n```\n\n## Troubleshooting\n\n### Issue: Legend Outside Plot Area\n\nFigure-level functions place legends outside by default. To move inside:\n\n```python\ng = sns.relplot(data=df, x='x', y='y', hue='category')\ng._legend.set_bbox_to_anchor((0.9, 0.5))  # Adjust position\n```\n\n### Issue: Overlapping Labels\n\n```python\nplt.xticks(rotation=45, ha='right')\nplt.tight_layout()\n```\n\n### Issue: Figure Too Small\n\nFor figure-level functions:\n```python\nsns.relplot(data=df, x='x', y='y', height=6, aspect=1.5)\n```\n\nFor axes-level functions:\n```python\nfig, ax = plt.subplots(figsize=(10, 6))\nsns.scatterplot(data=df, x='x', y='y', ax=ax)\n```\n\n### Issue: Colors Not Distinct Enough\n\n```python\n# Use a different palette\nsns.set_palette(\"bright\")\n\n# Or specify number of colors\npalette = sns.color_palette(\"husl\", n_colors=len(df['category'].unique()))\nsns.scatterplot(data=df, x='x', y='y', hue='category', palette=palette)\n```\n\n### Issue: KDE Too Smooth or Jagged\n\n```python\n# Adjust bandwidth\nsns.kdeplot(data=df, x='x', bw_adjust=0.5)  # Less smooth\nsns.kdeplot(data=df, x='x', bw_adjust=2)    # More smooth\n```\n\n## Resources\n\nThis skill includes reference materials for deeper exploration:\n\n### references\u002F\n\n- `function_reference.md` - Comprehensive listing of all seaborn functions with parameters and examples\n- `objects_interface.md` - Detailed guide to the modern seaborn.objects API\n- `examples.md` - Common use cases and code patterns for different analysis scenarios\n\nLoad reference files as needed for detailed function signatures, advanced parameters, or specific examples.\n",{"name":5,"description":7},"main","34fa6acf794b9a6310d298cbb8fb8c9420a5edcf","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fseaborn",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":47,"prompt":57,"zipAvailable":58},[48,51,54],{"tool":49,"command":50},"npx","npx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill seaborn",{"tool":52,"command":53},"bunx","bunx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill seaborn",{"tool":55,"command":56},"pnpm","pnpm dlx skills add transaurus\u002Fstaging-davila7-claude-code-templates --skill seaborn","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\u002Fseaborn\u002FSKILL.md",true,[60,66,70],{"path":61,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":64,"sourceHtmlUrl":65},"references\u002Fexamples.md","REFERENCE","参考文档",17624,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fseaborn\u002Freferences\u002Fexamples.md",{"path":67,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":68,"sourceHtmlUrl":69},"references\u002Ffunction_reference.md",24615,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fseaborn\u002Freferences\u002Ffunction_reference.md",{"path":71,"resourceType":62,"resourceTypeDesc":63,"sizeBytes":72,"sourceHtmlUrl":73},"references\u002Fobjects_interface.md",20801,"https:\u002F\u002Fgithub.com\u002Ftransaurus\u002Fstaging-davila7-claude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fseaborn\u002Freferences\u002Fobjects_interface.md",[75,104,125,149,169,195],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":84,"publishedAt":33},"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",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,32,103],"zapier make patterns","patterns","make","zapier","writing","workflow","automation","code","integrations","business","use","power","complexity","platform","apps","how to","No-Code","which","spawner",{"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":110,"publishedAt":33},"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",[],[106,111,112,113,114,115,116,117,118,119,120,121,95,122,123,124],"cli","github","commit","pull request","openai","pull","push","flow","open","gh","user","only","stage","github cli",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":132,"publishedAt":33},"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",[131],{"categoryNo":82,"categoryName":83,"taxonomyType":21,"taxonomyTypeDesc":22},[90,91,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,32,103],"Workflow Automation","ai agents","accessibility","workflows","apache","execution","inngest","parallel","orchestrator","resume","n8n","payment","worker","scripts","temporal","with",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":155,"publishedAt":33},"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",[],[156,157,158,159,160,136,161,137,138,162,95,163,164,165,166,167,168,148,32,103],"dev","trigger dev","trigger","design","typescript","async execution","first","async","developer experience","expert","task","background jobs","long",{"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":178,"publishedAt":33},"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",[175],{"categoryNo":176,"categoryName":177,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",[179,180,181,148,182,90,183,184,185,186,187,188,95,189,190,191,192,193,194,32],"planning with files","planning","files","with files","storage","tasks","markdown","knowledge","projects","work","manus","structured output","progress","transforms","multi","output",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":205,"publishedAt":33},"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",[201,204],{"categoryNo":202,"categoryName":203,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":82,"categoryName":83,"taxonomyType":21,"taxonomyTypeDesc":22},[90,206,143,207,86,208,209,136,210,184,180,211,212,213,214,215,216,217,218,219],"n8n workflow patterns","workflow patterns","n8n workflow","ai agent","architecture","database","api","planning workflow","structure","database operations","webhook","processing","http api","scheduled"]