Create Viz
Skill ActiveCreate publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
Create publication-quality visualizations with Python, turning data into charts for reports, presentations, or interactive exploration.
Features
- Generate charts from query results or DataFrames
- Select appropriate chart types for trends and comparisons
- Apply design best practices for clarity and accuracy
- Create interactive charts with hover and zoom using Plotly
- Save charts as high-quality PNG files
Use Cases
- Turning query results into a chart
- Selecting the right chart type for a trend or comparison
- Generating a plot for a report or presentation
- Needing an interactive chart with hover and zoom
Non-Goals
- Performing complex statistical analysis beyond visualization
- Directly querying databases without user-provided data source
- Creating animations or advanced graphical effects
Maintenance
- warning:Dependency ManagementThe skill relies on Python libraries (matplotlib, seaborn, pandas, plotly) but there is no explicit mention of version pinning or dependency management strategies like lockfiles for these. There is also no mention of vulnerability checks.
Trust
- warning:Issues AttentionIn the last 90 days, 29 issues were opened and 4 were closed, indicating a low closure rate (approximately 12%) and potentially slow maintainer response.
Code Execution
- info:ValidationThe workflow describes data cleaning and preparation steps, but there's no explicit mention of using a schema library for input validation or sanitization for the Python code generation or data handling.
- info:Error HandlingThe workflow outlines steps and considerations for data handling and chart generation but does not detail specific error catching mechanisms or structured error reporting for potential issues like invalid data or library failures.
Portability
- warning:Runtime stabilityThe skill implicitly assumes a Python environment with specific libraries (matplotlib, seaborn, pandas, plotly) installed. While standard, it does not explicitly declare these as prerequisites or version requirements, which could lead to runtime failures if not met.
- warning:Stack assumptionsThe skill assumes a Python environment with libraries like matplotlib, seaborn, and plotly but does not explicitly declare minimum versions or package manager requirements, potentially leading to runtime issues.
Errors
- info:Actionable error messagesWhile the workflow describes potential failure modes, it does not explicitly detail the user-facing error messages or provide specific remediation steps beyond general advice for data preparation.
Execution
- warning:Pinned dependenciesThe SKILL.md does not specify exact versions for its Python dependencies (matplotlib, seaborn, pandas, plotly), nor does it provide a lockfile, which could lead to compatibility issues.
Practical Utility
- info:Edge casesThe workflow mentions data cleaning and preparation but does not explicitly list failure modes (e.g., malformed input, missing dependencies) with symptoms and recovery steps.
Installation
First, add the marketplace
/plugin marketplace add anthropics/knowledge-work-plugins/plugin install data@knowledge-work-pluginsQuality Score
Trust Signals
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