Openclaw Skills CSV Data Analyzer turns CSV files or pasted tables into instant summaries, chart recommendations, correlations, and Python analysis code.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install csv-data-analyzer
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 csv-data-analyzer 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 CSV Data Analyzer?
Openclaw Skills CSV Data Analyzer is a fast, analyst-friendly workflow for turning raw tabular data into actionable insight. It automatically identifies numeric and categorical columns, calculates descriptive statistics, flags missing values, surfaces top distributions, and checks correlations so you can understand a dataset in minutes instead of hours.
Beyond statistics, Openclaw Skills recommends the right visualization for the shape of your data—such as bar, line, or scatter charts—and returns ready-to-run Python examples using pandas and matplotlib. It also converts the findings into 3–5 business-readable conclusions, making it useful for both technical review and stakeholder reporting.
CSV Data Analyzer Use Cases
- Analyze sales CSVs to find revenue drivers, anomalies, and performance gaps.
- Compare channel, region, or product results with quick chart guidance.
- Audit data quality by checking missing values, skew, and unexpected distributions.
- Turn pasted spreadsheet tables or exported reports into structured analysis.
- Generate reproducible pandas + matplotlib code for further exploration.
- Produce concise business conclusions for non-technical stakeholders.
How CSV Data Analyzer Works
- Provide a CSV file path, upload a CSV, or paste a table into Openclaw Skills.
- The skill infers column types, separating numeric measures from categorical dimensions.
- It calculates core profiling metrics, including descriptive statistics, missing-value counts, top values, and correlations.
- It selects the most suitable chart type based on the dataset shape and the analysis goal.
- It generates pandas + matplotlib code for the recommended checks and visualizations.
- It summarizes the findings into 3–5 readable conclusions you can share or act on.
CSV Data Analyzer Setup
Openclaw Skills CSV Data Analyzer requires no dedicated package installation inside the agent. To get the best results:
- Ensure your CSV includes a header row and consistent delimiters.
- Prefer UTF-8 encoded files and clean column names.
- Upload the CSV or paste the table, then specify the business question you want answered.
- If you plan to run the generated charts locally, install pandas and matplotlib.
- Save the returned Python code in your own project and execute it against the same dataset.
python -m pip install pandas matplotlib
# Optional: preview the first rows before analysis
head -n 5 sales.csv
CSV Data Analyzer Data Schema & Taxonomy
Input schema
| Field | Type | Notes |
|---|---|---|
| source | CSV file or pasted table | Primary dataset input |
| headers | string array | Used to infer column roles |
| rows | array of records | Parsed tabular data |
| row_count | integer | Dataset size |
Inferred column taxonomy
- Numeric columns: used for summary statistics, correlation, and trend charts.
- Categorical columns: used for top-value frequency, group comparison, and bar charts.
- Missing-data flags: used to surface completeness issues.
Generated analysis outputs
| Output | Purpose |
|---|---|
| Descriptive stats | Central tendency and spread |
| Missing-value report | Data quality review |
| Top distributions | Category concentration and skew |
| Correlation view | Relationship detection across numeric fields |
| Chart suggestions | Bar, line, or scatter recommendation |
| Python code | pandas + matplotlib starter implementation |
| Business conclusions | 3-5 readable takeaways for stakeholders |
File and artifact handling
- The skill itself returns analysis as text and code.
- No persistent files are created unless you save the generated Python script.
- Common local artifact names after execution: analysis.py, chart.png, summary.md.
CSV Data Analyzer Advanced Features
- Automatic numeric-versus-categorical detection for mixed CSVs.
- Correlation and distribution analysis without manual setup.
- Chart selection logic that matches data shape to visualization type.
- Ready-to-run pandas + matplotlib code generation.
- Chinese business summary output for faster stakeholder communication.
- Openclaw Skills can scale into the enterprise kingai.work tier for batch processing, API automation, industry templates, and priority support with SLA.
SKILL.md
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