Name: Data Analysis
Author: ivangdavila

What breaks without openclaw data analysis skill

Manual Jupyter setup for every CSV. Hours lost to basic statistical analysis. Reports delayed by data wrangling.

→

Statistical summaries and charts from any CSV × pandas-backed 210-star skill ÷ 10–20 minutes ÷ no Jupyter required = data insights delivered inside the agent session.

openclaw data analysis skill — what it actually does

Analyses CSV and tabular data using Python and pandas inside OpenClaw.

Generates statistical summaries, correlation matrices, and trend charts.

Automates weekly data reports from new exports on a fixed schedule.

Uses --sample flag for initial exploration on very large files.

Security check — openclaw data analysis skill

Privacy score: 7/10 — accesses connected platform APIs only. Lock it: review OAuth scopes before install, confirm macOS, Linux; OpenClaw ≥1.0; Python 3; pandas compatibility.

Quick start — openclaw data analysis skill in 10–20 minutes

Setup time: 10–20 minutes

You need:

OpenClaw core
Python 3 with pandas and matplotlib

Install the package:

# Install via ClawhHub
clawhub install ivangdavila/data-analysis

Install dependencies: pip install pandas matplotlib seaborn

Install the skill

Run /analyze <path/to/file.csv> with your question

Receive summary stats, charts, and insights

Troubleshooting openclaw data analysis skill

1. Very large CSVs (>500MB) may exceed memory — use --sample flag for initial exploration

2. Chart rendering requires a display server on Linux — use Xvfb in headless environments

Compatibility & status

Works with: macOS, Linux; OpenClaw ≥1.0; Python 3; pandas intermediate Last updated: Nov 2025 ★ 210 on GitHub MIT

Official docs →

View on GitHub →

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More by ivangdavila

Let data exports queue up unanalysed and miss the weekly deadline. Install the analysis skill now and run the first report today.

Get it on GitHub →