[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6X0STXVX9VRJJ0025WXR7TW":3,"skill-same:01M6X0STXVX9VRJJ0025WXR7TW":70},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":13,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6X0STXVX9VRJJ0025WXR7TW","plotly",null,"Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.","UhdyIndy\u002Fantigravity-awesome-skills","UhdyIndy","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F210015129?v=4","01M6R8N8YNJ1AKKKZDGSFE7DXG","01M6R8N92JYB8DVF9CPJV52W1C",0,"MIT","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Fplotly\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",[5,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"community","when","static","unknown","for","you","figures","zoom","scientific","info","hover","use","interactive","dashboards","Charts","analysis","library","visualization","presentations","2026-11-10 10:12:30",false,"---\nname: plotly\ndescription: Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use matplotlib or scientific-visualization.\nlicense: MIT license\nmetadata:\n    skill-author: K-Dense Inc.\nrisk: unknown\nsource: community\n---\n\n# Plotly\n\nPython graphing library for creating interactive, publication-quality visualizations with 40+ chart types.\n\n## When to Use\n\n- You need interactive charts with hover, zoom, pan, or web embedding.\n- You are building dashboards, exploratory analysis notebooks, or presentations that benefit from rich interaction.\n- You want to choose between Plotly Express and Graph Objects for the same visualization task.\n\n## Quick Start\n\nInstall Plotly:\n```bash\nuv pip install plotly\n```\n\nBasic usage with Plotly Express (high-level API):\n```python\nimport plotly.express as px\nimport pandas as pd\n\ndf = pd.DataFrame({\n    'x': [1, 2, 3, 4],\n    'y': [10, 11, 12, 13]\n})\n\nfig = px.scatter(df, x='x', y='y', title='My First Plot')\nfig.show()\n```\n\n## Choosing Between APIs\n\n### Use Plotly Express (px)\nFor quick, standard visualizations with sensible defaults:\n- Working with pandas DataFrames\n- Creating common chart types (scatter, line, bar, histogram, etc.)\n- Need automatic color encoding and legends\n- Want minimal code (1-5 lines)\n\nSee reference\u002Fplotly-express.md for complete guide.\n\n### Use Graph Objects (go)\nFor fine-grained control and custom visualizations:\n- Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)\n- Building complex multi-trace figures from scratch\n- Need precise control over individual components\n- Creating specialized visualizations with custom shapes and annotations\n\nSee reference\u002Fgraph-objects.md for complete guide.\n\n**Note:** Plotly Express returns graph objects Figure, so you can combine approaches:\n```python\nfig = px.scatter(df, x='x', y='y')\nfig.update_layout(title='Custom Title')  # Use go methods on px figure\nfig.add_hline(y=10)                     # Add shapes\n```\n\n## Core Capabilities\n\n### 1. Chart Types\n\nPlotly supports 40+ chart types organized into categories:\n\n**Basic Charts:** scatter, line, bar, pie, area, bubble\n\n**Statistical Charts:** histogram, box plot, violin, distribution, error bars\n\n**Scientific Charts:** heatmap, contour, ternary, image display\n\n**Financial Charts:** candlestick, OHLC, waterfall, funnel, time series\n\n**Maps:** scatter maps, choropleth, density maps (geographic visualization)\n\n**3D Charts:** scatter3d, surface, mesh, cone, volume\n\n**Specialized:** sunburst, treemap, sankey, parallel coordinates, gauge\n\nFor detailed examples and usage of all chart types, see reference\u002Fchart-types.md.\n\n### 2. Layouts and Styling\n\n**Subplots:** Create multi-plot figures with shared axes:\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))\nfig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)\n```\n\n**Templates:** Apply coordinated styling:\n```python\nfig = px.scatter(df, x='x', y='y', template='plotly_dark')\n# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white\n```\n\n**Customization:** Control every aspect of appearance:\n- Colors (discrete sequences, continuous scales)\n- Fonts and text\n- Axes (ranges, ticks, grids)\n- Legends\n- Margins and sizing\n- Annotations and shapes\n\nFor complete layout and styling options, see reference\u002Flayouts-styling.md.\n\n### 3. Interactivity\n\nBuilt-in interactive features:\n- Hover tooltips with customizable data\n- Pan and zoom\n- Legend toggling\n- Box\u002Flasso selection\n- Rangesliders for time series\n- Buttons and dropdowns\n- Animations\n\n```python\n# Custom hover template\nfig.update_traces(\n    hovertemplate='\u003Cb>%{x}\u003C\u002Fb>\u003Cbr>Value: %{y:.2f}\u003Cextra>\u003C\u002Fextra>'\n)\n\n# Add rangeslider\nfig.update_xaxes(rangeslider_visible=True)\n\n# Animations\nfig = px.scatter(df, x='x', y='y', animation_frame='year')\n```\n\nFor complete interactivity guide, see reference\u002Fexport-interactivity.md.\n\n### 4. Export Options\n\n**Interactive HTML:**\n```python\nfig.write_html('chart.html')                       # Full standalone\nfig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file\n```\n\n**Static Images (requires kaleido):**\n```bash\nuv pip install kaleido\n```\n\n```python\nfig.write_image('chart.png')   # PNG\nfig.write_image('chart.pdf')   # PDF\nfig.write_image('chart.svg')   # SVG\n```\n\nFor complete export options, see reference\u002Fexport-interactivity.md.\n\n## Common Workflows\n\n### Scientific Data Visualization\n\n```python\nimport plotly.express as px\n\n# Scatter plot with trendline\nfig = px.scatter(df, x='temperature', y='yield', trendline='ols')\n\n# Heatmap from matrix\nfig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')\n\n# 3D surface plot\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])\n```\n\n### Statistical Analysis\n\n```python\n# Distribution comparison\nfig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)\n\n# Box plot with all points\nfig = px.box(df, x='category', y='value', points='all')\n\n# Violin plot\nfig = px.violin(df, x='group', y='measurement', box=True)\n```\n\n### Time Series and Financial\n\n```python\n# Time series with rangeslider\nfig = px.line(df, x='date', y='price')\nfig.update_xaxes(rangeslider_visible=True)\n\n# Candlestick chart\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Candlestick(\n    x=df['date'],\n    open=df['open'],\n    high=df['high'],\n    low=df['low'],\n    close=df['close']\n)])\n```\n\n### Multi-Plot Dashboards\n\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(\n    rows=2, cols=2,\n    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),\n    specs=[[{'type': 'scatter'}, {'type': 'bar'}],\n           [{'type': 'histogram'}, {'type': 'box'}]]\n)\n\nfig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)\nfig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)\nfig.add_trace(go.Histogram(x=data), row=2, col=1)\nfig.add_trace(go.Box(y=data), row=2, col=2)\n\nfig.update_layout(height=800, showlegend=False)\n```\n\n## Integration with Dash\n\nFor interactive web applications, use Dash (Plotly's web app framework):\n\n```bash\nuv pip install dash\n```\n\n```python\nimport dash\nfrom dash import dcc, html\nimport plotly.express as px\n\napp = dash.Dash(__name__)\n\nfig = px.scatter(df, x='x', y='y')\n\napp.layout = html.Div([\n    html.H1('Dashboard'),\n    dcc.Graph(figure=fig)\n])\n\napp.run_server(debug=True)\n```\n\n## Reference Files\n\n- **plotly-express.md** - High-level API for quick visualizations\n- **graph-objects.md** - Low-level API for fine-grained control\n- **chart-types.md** - Complete catalog of 40+ chart types with examples\n- **layouts-styling.md** - Subplots, templates, colors, customization\n- **export-interactivity.md** - Export options and interactive features\n\n## Additional Resources\n\n- Official documentation: https:\u002F\u002Fplotly.com\u002Fpython\u002F\n- API reference: https:\u002F\u002Fplotly.com\u002Fpython-api-reference\u002F\n- Community forum: https:\u002F\u002Fcommunity.plotly.com\u002F\n","# Plotly\n\nPython graphing library for creating interactive, publication-quality visualizations with 40+ chart types.\n\n## When to Use\n\n- You need interactive charts with hover, zoom, pan, or web embedding.\n- You are building dashboards, exploratory analysis notebooks, or presentations that benefit from rich interaction.\n- You want to choose between Plotly Express and Graph Objects for the same visualization task.\n\n## Quick Start\n\nInstall Plotly:\n```bash\nuv pip install plotly\n```\n\nBasic usage with Plotly Express (high-level API):\n```python\nimport plotly.express as px\nimport pandas as pd\n\ndf = pd.DataFrame({\n    'x': [1, 2, 3, 4],\n    'y': [10, 11, 12, 13]\n})\n\nfig = px.scatter(df, x='x', y='y', title='My First Plot')\nfig.show()\n```\n\n## Choosing Between APIs\n\n### Use Plotly Express (px)\nFor quick, standard visualizations with sensible defaults:\n- Working with pandas DataFrames\n- Creating common chart types (scatter, line, bar, histogram, etc.)\n- Need automatic color encoding and legends\n- Want minimal code (1-5 lines)\n\nSee reference\u002Fplotly-express.md for complete guide.\n\n### Use Graph Objects (go)\nFor fine-grained control and custom visualizations:\n- Chart types not in Plotly Express (3D mesh, isosurface, complex financial charts)\n- Building complex multi-trace figures from scratch\n- Need precise control over individual components\n- Creating specialized visualizations with custom shapes and annotations\n\nSee reference\u002Fgraph-objects.md for complete guide.\n\n**Note:** Plotly Express returns graph objects Figure, so you can combine approaches:\n```python\nfig = px.scatter(df, x='x', y='y')\nfig.update_layout(title='Custom Title')  # Use go methods on px figure\nfig.add_hline(y=10)                     # Add shapes\n```\n\n## Core Capabilities\n\n### 1. Chart Types\n\nPlotly supports 40+ chart types organized into categories:\n\n**Basic Charts:** scatter, line, bar, pie, area, bubble\n\n**Statistical Charts:** histogram, box plot, violin, distribution, error bars\n\n**Scientific Charts:** heatmap, contour, ternary, image display\n\n**Financial Charts:** candlestick, OHLC, waterfall, funnel, time series\n\n**Maps:** scatter maps, choropleth, density maps (geographic visualization)\n\n**3D Charts:** scatter3d, surface, mesh, cone, volume\n\n**Specialized:** sunburst, treemap, sankey, parallel coordinates, gauge\n\nFor detailed examples and usage of all chart types, see reference\u002Fchart-types.md.\n\n### 2. Layouts and Styling\n\n**Subplots:** Create multi-plot figures with shared axes:\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(rows=2, cols=2, subplot_titles=('A', 'B', 'C', 'D'))\nfig.add_trace(go.Scatter(x=[1, 2], y=[3, 4]), row=1, col=1)\n```\n\n**Templates:** Apply coordinated styling:\n```python\nfig = px.scatter(df, x='x', y='y', template='plotly_dark')\n# Built-in: plotly_white, plotly_dark, ggplot2, seaborn, simple_white\n```\n\n**Customization:** Control every aspect of appearance:\n- Colors (discrete sequences, continuous scales)\n- Fonts and text\n- Axes (ranges, ticks, grids)\n- Legends\n- Margins and sizing\n- Annotations and shapes\n\nFor complete layout and styling options, see reference\u002Flayouts-styling.md.\n\n### 3. Interactivity\n\nBuilt-in interactive features:\n- Hover tooltips with customizable data\n- Pan and zoom\n- Legend toggling\n- Box\u002Flasso selection\n- Rangesliders for time series\n- Buttons and dropdowns\n- Animations\n\n```python\n# Custom hover template\nfig.update_traces(\n    hovertemplate='\u003Cb>%{x}\u003C\u002Fb>\u003Cbr>Value: %{y:.2f}\u003Cextra>\u003C\u002Fextra>'\n)\n\n# Add rangeslider\nfig.update_xaxes(rangeslider_visible=True)\n\n# Animations\nfig = px.scatter(df, x='x', y='y', animation_frame='year')\n```\n\nFor complete interactivity guide, see reference\u002Fexport-interactivity.md.\n\n### 4. Export Options\n\n**Interactive HTML:**\n```python\nfig.write_html('chart.html')                       # Full standalone\nfig.write_html('chart.html', include_plotlyjs='cdn')  # Smaller file\n```\n\n**Static Images (requires kaleido):**\n```bash\nuv pip install kaleido\n```\n\n```python\nfig.write_image('chart.png')   # PNG\nfig.write_image('chart.pdf')   # PDF\nfig.write_image('chart.svg')   # SVG\n```\n\nFor complete export options, see reference\u002Fexport-interactivity.md.\n\n## Common Workflows\n\n### Scientific Data Visualization\n\n```python\nimport plotly.express as px\n\n# Scatter plot with trendline\nfig = px.scatter(df, x='temperature', y='yield', trendline='ols')\n\n# Heatmap from matrix\nfig = px.imshow(correlation_matrix, text_auto=True, color_continuous_scale='RdBu')\n\n# 3D surface plot\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Surface(z=z_data, x=x_data, y=y_data)])\n```\n\n### Statistical Analysis\n\n```python\n# Distribution comparison\nfig = px.histogram(df, x='values', color='group', marginal='box', nbins=30)\n\n# Box plot with all points\nfig = px.box(df, x='category', y='value', points='all')\n\n# Violin plot\nfig = px.violin(df, x='group', y='measurement', box=True)\n```\n\n### Time Series and Financial\n\n```python\n# Time series with rangeslider\nfig = px.line(df, x='date', y='price')\nfig.update_xaxes(rangeslider_visible=True)\n\n# Candlestick chart\nimport plotly.graph_objects as go\nfig = go.Figure(data=[go.Candlestick(\n    x=df['date'],\n    open=df['open'],\n    high=df['high'],\n    low=df['low'],\n    close=df['close']\n)])\n```\n\n### Multi-Plot Dashboards\n\n```python\nfrom plotly.subplots import make_subplots\nimport plotly.graph_objects as go\n\nfig = make_subplots(\n    rows=2, cols=2,\n    subplot_titles=('Scatter', 'Bar', 'Histogram', 'Box'),\n    specs=[[{'type': 'scatter'}, {'type': 'bar'}],\n           [{'type': 'histogram'}, {'type': 'box'}]]\n)\n\nfig.add_trace(go.Scatter(x=[1, 2, 3], y=[4, 5, 6]), row=1, col=1)\nfig.add_trace(go.Bar(x=['A', 'B'], y=[1, 2]), row=1, col=2)\nfig.add_trace(go.Histogram(x=data), row=2, col=1)\nfig.add_trace(go.Box(y=data), row=2, col=2)\n\nfig.update_layout(height=800, showlegend=False)\n```\n\n## Integration with Dash\n\nFor interactive web applications, use Dash (Plotly's web app framework):\n\n```bash\nuv pip install dash\n```\n\n```python\nimport dash\nfrom dash import dcc, html\nimport plotly.express as px\n\napp = dash.Dash(__name__)\n\nfig = px.scatter(df, x='x', y='y')\n\napp.layout = html.Div([\n    html.H1('Dashboard'),\n    dcc.Graph(figure=fig)\n])\n\napp.run_server(debug=True)\n```\n\n## Reference Files\n\n- **plotly-express.md** - High-level API for quick visualizations\n- **graph-objects.md** - Low-level API for fine-grained control\n- **chart-types.md** - Complete catalog of 40+ chart types with examples\n- **layouts-styling.md** - Subplots, templates, colors, customization\n- **export-interactivity.md** - Export options and interactive features\n\n## Additional Resources\n\n- Official documentation: https:\u002F\u002Fplotly.com\u002Fpython\u002F\n- API reference: https:\u002F\u002Fplotly.com\u002Fpython-api-reference\u002F\n- Community forum: https:\u002F\u002Fcommunity.plotly.com\u002F\n",{"name":5,"risk":26,"source":23,"license":47,"metadata":48,"description":7},"MIT license",{"skill-author":49},"K-Dense Inc.","main","69d1595b1e37ce252a542fab54b908bc8486139a","skills\u002Fplotly","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills","https:\u002F\u002Fgithub.com\u002FUhdyIndy","2026-04-12 18:53:09",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add UhdyIndy\u002Fantigravity-awesome-skills --skill plotly",{"tool":62,"command":63},"bunx","bunx skills add UhdyIndy\u002Fantigravity-awesome-skills --skill plotly",{"tool":65,"command":66},"pnpm","pnpm dlx skills add UhdyIndy\u002Fantigravity-awesome-skills --skill plotly","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Fplotly\u002FSKILL.md",true,[],[71,105,130,165,193,212],{"skillNo":72,"name":73,"nameZh":6,"description":74,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":75,"categories":76,"keywords":85,"publishedAt":42},"01M6X3BZ46SFSXPAH6A102Y8Q0","ckm:ui-styling","Create beautiful, accessible user interfaces with shadcn\u002Fui components (built on Radix UI + Tailwind), Tailwind CSS utility-first styling, and canvas-based visual designs. Use when building user interfaces, implementing design systems, creating responsive layouts, adding accessible components (dialogs, dropdowns, forms, tables), customizing themes and colors, implementing dark mode, generating visual designs and posters, or establishing consistent styling patterns across applications.","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Fui-styling\u002FSKILL.md",[77,80],{"categoryNo":78,"categoryName":79,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",{"categoryNo":81,"categoryName":82,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,24],"ui styling","ui","styling","ckm:ui styling","ckm","design","css","canvas","components","component","create","design systems","Tailwind","across","layout","themes","utility","mode","implementing",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":109,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":42},"01M6X39B8F9MTQ85PX83M52HGN","transformers-js","Run Hugging Face models in JavaScript or TypeScript with Transformers.js in Node.js or the browser.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Ftransformers-js\u002FSKILL.md",[],[113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,50,128,129,27,26],"Transformers","js","transformers js","typescript","github","webgpu","run","HuggingFace","learning","access","hugging face","modules","models","hub","es","local","internet",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":134,"categories":135,"keywords":148,"publishedAt":42},"01M6X1E1CF2FQ82HMY2965GYHE","scientific-writing","This is the core skill for the deep research and writing tool—combining AI-driven deep research with well-formatted written outputs. Every document produced is backed by comprehensive literature search and verified citations through the research-lookup skill.","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[136,139,142,145],{"categoryNo":137,"categoryName":138,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":140,"categoryName":141,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":143,"categoryName":144,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":146,"categoryName":147,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000036","内容创作者",[149,150,31,151,152,153,154,155,156,157,23,158,159,160,161,162,27,26,163,164],"writing","scientific writing","research","core","search","deep research","Citations","tool","document","deep","literature search","literature","with","verified","core skill","outputs",{"skillNo":166,"name":167,"nameZh":6,"description":168,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":169,"categories":170,"keywords":177,"publishedAt":42},"01M6X09E20CJKMMG0WKKZJQN1E","native-data-fetching","Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, React Query, SWR, error handling, caching, offline support, and Expo Router data loaders (useLoaderData).","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Fnative-data-fetching\u002FSKILL.md",[171,174],{"categoryNo":172,"categoryName":173,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":175,"categoryName":176,"taxonomyType":83,"taxonomyTypeDesc":84},"01JBSEEDCAT000000000000033","数据分析师",[178,179,180,181,182,183,184,185,186,34,23,187,188,189,190,191,104,26,24,192],"native data fetching","data","native","data fetching","react","debugging","error handling","api","fetch","call","query","caching","error","expo router","react query",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":197,"categories":198,"keywords":199,"publishedAt":42},"01M6X077866P0FKKXZAREXXJ7A","moyu","Anti-over-engineering guardrail that activates when an AI coding agent expands scope, adds abstractions, or changes files the user did not request.","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Fmoyu\u002FSKILL.md",[],[195,200,201,202,203,204,205,206,23,207,208,209,210,211,24],"safe","coding agent","engineering","coding","user","ai coding","files","guardrail","scope","changes","ai coding agent","request",{"skillNo":213,"name":214,"nameZh":6,"description":215,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":109,"licenseFileUrl":6,"sourceHtmlUrl":216,"categories":217,"keywords":222,"publishedAt":42},"01M6WZ68J48PFK341TQPN1JG3B","google-slides-automation","Lightweight Google Slides integration with standalone OAuth authentication. No MCP server required. Full read\u002Fwrite access.","https:\u002F\u002Fgithub.com\u002FUhdyIndy\u002Fantigravity-awesome-skills\u002Fblob\u002Fmain\u002Fskills\u002Fgoogle-slides-automation\u002FSKILL.md",[218,221],{"categoryNo":219,"categoryName":220,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000003","演示文稿",{"categoryNo":143,"categoryName":144,"taxonomyType":20,"taxonomyTypeDesc":21},[223,224,225,226,227,228,229,230,231,232,233,122,234,235,23,236,237,161,238],"google","automation","google slides","slides","google slides automation","MCP","mcp server","authentication","integration","server","OAuth","read","write","full","critical","standalone"]