A specialized tool that converts raw text report data into a structured JSON stream of visual components and interactive charts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install report-to-visualization
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 report-to-visualization 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 Report-to-Visualization?
The report-to-visualization skill is designed to bridge the gap between long-form text reports and high-impact visual dashboards. It analyzes complex report content and maps it to a strictly defined library of visualization components, ensuring that data trends, comparisons, and key performance indicators are presented clearly and professionally. By utilizing a standardized MODULE format, it allows developers and analysts to automate the creation of graphical summaries from unstructured data sources.
This skill is particularly powerful for creating automated insights where manual chart creation is too slow. It enforces strict data integrity, such as requiring numeric values for charts and ensuring that qualitative insights are captured in text-based card components. It follows a specific layout logic that optimizes for readability, preventing information overload by organizing data into logical rows and columns within a coherent dashboard structure.
Report-to-Visualization Use Cases
- Converting text-heavy market analysis into visual executive summaries.
- Automating the generation of performance dashboards from raw agent reports.
- Transforming project milestones and execution steps into interactive timelines and phase cards.
- Identifying risks and consequences in reports and highlighting them via specialized alert components.
- Presenting multi-dimensional competitive analysis through structured list and comparison tables.
How Report-to-Visualization Works
- The system ingests raw report data via the
${moduleData}variable. - It performs an internal planning phase to map headers and sub-headers into logical module blocks.
- Content is analyzed to select the most appropriate component type (e.g., Line charts for trends, Pie charts for distributions).
- Data points are extracted and validated against strict schemas (e.g., ensuring 100% sums for pie charts or specific line counts for text cards).
- The skill applies layout constraints to organize components into rows (r1, r2, etc.) and columns (c1, c2).
- A final, strictly formatted JSON object wrapped in a specialized code block is generated for the UI to render.
Report-to-Visualization Setup
To integrate this skill into your workflow, ensure your environment can handle the seller-report custom block.
# Standard initialization for Openclaw Skills
npm install @openclaw/report-viz-engine
Provide the following input variable to the skill:
${moduleData}: The string content of the report you wish to visualize.
Report-to-Visualization Data Schema & Taxonomy
The skill organizes data into a modules array, where each module represents a logical section of the report.
| Component | Type | Primary Purpose |
|---|---|---|
| Chart.Line | Visual | Displays trends with at least 5 data points. |
| Chart.Column | Visual | Compares values across categories. |
| DataCard | Metric | Shows core KPIs with period-over-period changes. |
| TextCard | Content | Aggregates qualitative insights and suggestions. |
| Chart.List | Table | Handles 3+ objects with 3-4 comparison dimensions. |
| AlertCard | Warning | Highlights risks, consequences, and action items. |
Report-to-Visualization Advanced Features
- Multi-component grouping: Automatically pairs complementary charts (like Column + Pie) in a single layout row.
- Semantic Data Labeling: Enforces unit and prefix inclusion to prevent 'naked numbers' without context.
- Dynamic Milestone Mapping: Automatically generates TimelineCard sequences for chronological data.
- Influence Leveling: Ranks factors 1-5 using InfluenceCard to show priority and impact focus points.
SKILL.md
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