Data Analysis for Openclaw

A comprehensive tool for analyzing spreadsheet data, calculating statistics, and generating visual reports automatically.

di5cip1e
v1.0.0
Apr 8, 2026
1
1.6k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install director-data-analysis

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install director-data-analysis 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 Data Analysis?

The Data Analysis skill is a robust extension for Openclaw Skills designed to bridge the gap between raw data and decision-making. It enables AI agents to ingest, clean, and interpret data from common formats like CSV and Excel using industry-standard libraries. By automating the extraction of descriptive statistics and the creation of visualizations, this skill allows users to uncover trends, correlations, and anomalies without manual spreadsheet manipulation. It is built to handle everything from simple file summaries to complex time-series analysis, ensuring that data is not just read, but understood.

Data Analysis Use Cases

  • Generating automated monthly sales reports and revenue trend visualizations.
  • Performing customer segmentation analysis using demographic and behavioral data.
  • Cleaning and preparing raw research data by handling missing values and malformed dates.
  • Visualizing stock levels or IoT sensor data over time to identify patterns.
  • Calculating key performance indicators (KPIs) and correlations across multi-column datasets.

How Data Analysis Works

  1. The process begins by detecting the file type and loading the data into a structured format using pandas.
  2. The skill performs an automated exploratory data analysis (EDA) to determine the shape, columns, and data types.
  3. Descriptive statistics and specific metrics like mean, median, and correlations are calculated based on numeric columns.
  4. Visualizations are generated using matplotlib, selecting the best chart type (bar, line, or histogram) for the data provided.
  5. A final summary report is produced, combining raw metrics with plain-English insights and actionable recommendations.

Data Analysis Setup

To integrate this capability into your environment, ensure you have the required Python dependencies installed. This skill relies on pandas for data manipulation and matplotlib for rendering charts. Use the following command to prepare your system for Openclaw Skills data processing:

pip install pandas openpyxl matplotlib

Once dependencies are met, the agent can begin processing files by targeting specific CSV or Excel paths.

Data Analysis Data Schema & Taxonomy

The skill organizes analysis into a structured output format to ensure consistency across different datasets. The following table describes the data components:

Component Details
Input Formats CSV, Excel (.xlsx, .xls)
Overview Row/column counts, data types, and date ranges
Key Metrics Top 5-10 calculated numbers (totals, averages, etc.)
Insights Bulleted list of qualitative findings derived from the data
Visualizations PNG or SVG charts for datasets exceeding 100 rows

Data Analysis Advanced Features

  • Large file support using chunked processing for datasets exceeding 100MB to prevent memory overflow.
  • Intelligent date parsing with automated error coercion for inconsistent time-series data.
  • Custom aggregation logic for grouping data by segments, categories, or time-based intervals.
  • Advanced data cleaning capabilities including automated filling or dropping of missing values (NaN).

SKILL.md


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