[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CBXGCS6VQF7AFQHZP9S370":3,"skill-same:01M7CBXGCS6VQF7AFQHZP9S370":52},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":29,"commitSha":30,"skillDirectory":31,"forksCount":32,"repositoryHtmlUrl":33,"repositoryHomepageUrl":34,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":36,"updatedAt":37,"install":38,"resources":51},"01M7CBXGCS6VQF7AFQHZP9S370","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.","YYC-Cube\u002FYYC3-Knowledge-Base","YYC-Cube","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235006543?v=4","01M79J2PH4SWD2X0825X6PN748","yyc3","01M79J2PHVK3F6ZBYT8K2SCZCW",1,"MIT","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-antigravity-awesome-skills\u002Fplugins\u002Fantigravity-awesome-skills\u002Fskills\u002Fplotly\u002FSKILL.md",[],[],"2026-11-15 14:19:17",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- 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\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.\n","# Plotly\n\nPython graphing library for creating interactive, publication-quality visualizations with 40+ chart types.\n\n## When to Use\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\n## Limitations\n- Use this skill only when the task clearly matches the scope described above.\n- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.\n- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.\n",{"name":5,"risk":24,"source":25,"license":26,"metadata":27,"description":7},"unknown","community","MIT license",{"skill-author":28},"K-Dense Inc.","main","87afe6ce96220accf50a345dea46784c8c9f210e","YYC3-Skills-技能包\u002FYYC3-Skills-技能包-antigravity-awesome-skills\u002Fplugins\u002Fantigravity-awesome-skills\u002Fskills\u002Fplotly",0,"https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base","https:\u002F\u002Fkb.yyc3.top\u002F","https:\u002F\u002Fgithub.com\u002FYYC-Cube","万象归元于云枢 | 深栈智启新纪元","2026-11-10 10:37:32",{"source":8,"skillName":5,"commands":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill plotly",{"tool":44,"command":45},"bunx","bunx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill plotly",{"tool":47,"command":48},"pnpm","pnpm dlx skills add YYC-Cube\u002FYYC3-Knowledge-Base --skill plotly","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-Skills-技能包\u002FYYC3-Skills-技能包-antigravity-awesome-skills\u002Fplugins\u002Fantigravity-awesome-skills\u002Fskills\u002Fplotly\u002FSKILL.md",true,[],[53,60,67,74,81,88],{"skillNo":54,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":57,"categories":58,"keywords":59,"publishedAt":19},"01M7CTDWHERCXPQCP0K03A86AS","vercel-sandbox","Run agent-browser + Chrome inside Vercel Sandbox microVMs for browser automation from any Vercel-deployed app. Use when the user needs browser automation in a Vercel app (Next.js, SvelteKit, Nuxt, Remix, Astro, etc.), wants to run headless Chrome without binary size limits, needs persistent browser sessions across commands, or wants ephemeral isolated browser environments. Triggers include \"Vercel Sandbox browser\", \"microVM Chrome\", \"agent-browser in sandbox\", \"browser automation on Vercel\", or any task requiring Chrome in a Vercel Sandbox.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fvercel-sandbox\u002FSKILL.md",[],[],{"skillNo":61,"name":62,"nameZh":6,"description":63,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":66,"publishedAt":19},"01M7CTD8D2BNJY54S65M47HEJA","slack","Interact with Slack workspaces using browser automation. Use when the user needs to check unread channels, navigate Slack, send messages, extract data, find information, search conversations, or automate any Slack task. Triggers include \"check my Slack\", \"what channels have unreads\", \"send a message to\", \"search Slack for\", \"extract from Slack\", \"find who said\", or any task requiring programmatic Slack interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fslack\u002FSKILL.md",[],[],{"skillNo":68,"name":69,"nameZh":6,"description":70,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":73,"publishedAt":19},"01M7CTCSSB8ZP3Q58C3YZDZK1W","electron","Automate Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify, etc.) using agent-browser via Chrome DevTools Protocol. Use when the user needs to interact with an Electron app, automate a desktop app, connect to a running app, control a native app, or test an Electron application. Triggers include \"automate Slack app\", \"control VS Code\", \"interact with Discord app\", \"test this Electron app\", \"connect to desktop app\", or any task requiring automation of a native Electron application.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Felectron\u002FSKILL.md",[],[],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M7CTCBJY76688V4DCZGWFHGQ","dogfood","Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to \"dogfood\", \"QA\", \"exploratory test\", \"find issues\", \"bug hunt\", \"test this app\u002Fsite\u002Fplatform\", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fdogfood\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":19},"01M7CTC4H0FFGVEAQR9PA4GA1A","agent-browser","Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to \"open a website\", \"fill out a form\", \"click a button\", \"take a screenshot\", \"scrape data from a page\", \"test this web app\", \"login to a site\", \"automate browser actions\", or any task requiring programmatic web interaction.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-WebSpec-Web标准\u002FYYC3-WebSpec-Web标准-standards\u002Fbrowser-agent\u002Fskills\u002Fagent-browser\u002FSKILL.md",[],[],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":19},"01M7CTBKVFYBG78FDSABSDJTTQ","shadcn","Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI, including chat interfaces. Provides project context, component docs, and usage examples. Applies when working with shadcn\u002Fui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for \"shadcn init\", \"create an app with --preset\", \"switch to --preset\", and for building a design system or applying a DESIGN.md or brand spec to shadcn\u002Fui.","https:\u002F\u002Fgithub.com\u002FYYC-Cube\u002FYYC3-Knowledge-Base\u002Fblob\u002Fmain\u002FYYC3-UI-界面组件\u002FYYC3-UI-界面组件-ui\u002Fskills\u002Fshadcn\u002FSKILL.md",[],[]]