Excel2Insights turns structured files into statistical profiles, data quality checks, visualizations, and shareable insight reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install excel2insights-pro
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 excel2insights-pro 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 Excel2Insights?
Excel2Insights is an Openclaw Skills data-analysis workflow that turns CSV, XLSX, XLS, and TSV files into structured profiles, statistical summaries, quality diagnostics, charts, and ready-to-share reports.
It is built for fast first-pass understanding and deeper exploratory analysis: use Quick Scan to inspect shape, dtypes, previews, and missingness, or run Deep Analysis to profile, clean, analyze, visualize, and publish a Markdown report. The result is a repeatable, agent-friendly way to convert raw tabular data into actionable insight.
Excel2Insights Use Cases
- You need to understand a CSV or XLSX file before writing queries or transformations.
- You want a data quality report showing missing values, duplicates, outliers, and type issues.
- You need charts and summaries for sales, operations, finance, or product datasets.
- You are comparing two tabular exports or checking for anomalies between file versions.
- You want Openclaw Skills to turn raw spreadsheets into a concise insight report for stakeholders.
How Excel2Insights Works
- Load and inspect the file with
excel-reader.pyto capture metadata, column names, dtypes, previews, missing values, and basic quality flags. - Profile the dataset with
data-analyzer.pyto compute type-specific statistics for numeric, categorical, and date columns, plus correlations and cross-tabulation when applicable. - Run built-in quality checks to detect missing-value ratios, duplicate rows, outliers via the IQR method, data type inconsistencies, and value range violations.
- Generate charts with
chart-generator.pybased on the data type and analysis goal, such as histograms, boxplots, bars, lines, pies, heatmaps, scatter plots, and pairplots. - Render the final report with
report-generator.py, combining the analysis JSON and chart images into a structured Markdown document with insights and recommendations. - Use
auto-pipeline.pywhen you want the full sequence executed in one command for end-to-end automation.
Excel2Insights Setup
- Ensure Python 3.9+ is installed.
- Create an isolated environment and install the project dependencies.
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
- Verify the reader works with a sample file.
python3 scripts/excel-reader.py --file data.csv
- Run the full pipeline when you want analysis, charts, and a report in one pass.
python3 scripts/auto-pipeline.py --file data/sales_2024.csv --charts histogram,bar,line,heatmap
- For XLSX workbooks, optionally target a sheet and set encoding when needed.
python3 scripts/excel-reader.py --file workbook.xlsx --sheet Sheet1 --encoding utf-8
Excel2Insights Data Schema & Taxonomy
Excel2Insights uses a file-based, JSON-first data model rather than a database schema. Each stage emits structured metadata that downstream scripts consume.
| Artifact | Produced By | Location / Format | Purpose |
|---|---|---|---|
| File metadata | excel-reader.py |
JSON output | Captures rows, columns, file size, sheet selection, encoding, column names, dtypes, previews, and missing counts. |
| Analysis payload | data-analyzer.py |
JSON output | Stores numeric statistics, categorical frequency data, date ranges, correlations, cross-tabs, and quality flags. |
| Chart assets | chart-generator.py |
output/charts/*.png |
Saves histograms, boxplots, bars, lines, pies, heatmaps, scatter plots, and pairplots for report embedding. |
| Insight report | report-generator.py or auto-pipeline.py |
output/report.md by default |
Combines the dataset overview, column analysis, chart gallery, anomalies, and recommendations. |
| Optional alternate reports | report-generator.py |
HTML or PDF | Produces non-Markdown deliverables when --format html or --format pdf is selected. |
Metadata taxonomy
- Dataset metadata: row count, column count, file size, file type, sheet name, and encoding.
- Column profile metadata: column name, dtype, non-null count, missing ratio, unique count, and type-specific statistics.
- Quality metadata: duplicates, outliers, inconsistent types, range violations, and other data integrity flags.
- Relationship metadata: correlations for numeric fields and cross-tabulation for categorical fields.
- Visualization metadata: chart type, source columns, generated filename, and theme.
- Report metadata: executive summary, dataset overview, key findings, anomalies, recommendations, and appendix notes.
Excel2Insights Advanced Features
- Quick Scan mode for instant file triage without running the entire pipeline.
- Deep Analysis mode for end-to-end profiling, quality checks, visualization, and reporting.
- IQR-based outlier detection, duplicate detection, missing-value ratios, and data type consistency checks.
- Numeric correlation matrices, categorical cross-tabulation, and date-range analysis for richer context.
- Flexible chart selection with column targeting, output directory control, and theme customization.
- Multi-format reporting support for Markdown, HTML, and PDF deliverables.
- One-command automation through
auto-pipeline.pyfor repeatable batch analysis jobs. - Structured JSON outputs that make Openclaw Skills easy to chain into larger agent workflows and downstream automations.
SKILL.md
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