A local-first chart generation engine that turns CSV or JSON data into high-quality visual trends and comparisons without cloud dependencies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chart
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install chart using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Chart?
The Chart skill is a robust data visualization tool designed for the Openclaw Skills ecosystem. It prioritizes data privacy and speed by generating all visual assets locally using Python and Matplotlib. By focusing on core chart types—bar, line, pie, and scatter—it ensures that developers and analysts can quickly interpret datasets, compare numbers, and document trends without relying on third-party APIs or network connectivity.
Built with a philosophy of clarity over complexity, this skill helps users move from raw data to actionable insights in seconds. Whether you are generating reports, preparing slides, or performing exploratory data analysis, the Chart skill provides a reliable, offline-ready workflow for any AI agent environment.
Chart Use Cases
- Visualizing performance trends over time using line charts.
- Comparing categorical data distributions with bar charts.
- Analyzing part-to-whole relationships in datasets using pie charts.
- Identifying correlations between two variables through scatter plots.
- Converting raw JSON or CSV outputs from other Openclaw Skills into visual reports.
How Chart Works
- Initialization: The skill prepares a local directory structure within the workspace to store metadata and image files.
- Analysis & Suggestion: The system evaluates provided labels and values to recommend the most effective chart type for the specific data structure.
- Local Rendering: Using a Python-based execution engine, the skill invokes Matplotlib to render the data into a high-resolution image file.
- Storage: The generated image is saved to a local output folder, and a record is added to a central JSON index for future retrieval.
- History Tracking: Users can query the local history to list, view, or reuse previously generated visual assets.
Chart Setup
To use this skill, ensure Python 3 and Matplotlib are installed on your local system. Run the following commands to initialize the Openclaw Skills environment for charting:
# Install dependencies
pip install matplotlib
# Initialize local storage folders
python3 init_storage.py
Chart Data Schema & Taxonomy
All data generated by this Openclaw Skills module is stored locally to ensure privacy and fast access. The storage hierarchy is organized as follows:
| Path | Purpose |
|---|---|
~/.openclaw/workspace/memory/chart/charts.json |
Metadata registry of all generated charts including titles, types, and timestamps. |
~/.openclaw/workspace/memory/chart/output/ |
Directory containing the actual image files (PNG/JPG) of the rendered charts. |
Data is indexed by a unique ID, allowing for easy integration with other local automation scripts.
Chart Advanced Features
- Intelligent Chart Selection: Automatically determines if a dataset is better suited for a line trend or a bar comparison via
suggest_chart.py. - Local-First Architecture: Operates entirely without network access, making it ideal for secure or air-gapped environments.
- Workspace Memory Integration: Maintains a persistent history of all visualizations generated across different sessions.
- Scriptable CLI Interface: Can be easily wrapped into larger automation pipelines to generate daily or weekly visual reports automatically.
SKILL.md
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