Data Analyzer

Data analysis and visualization skill for CSV, Excel, and JSON data. Use when analyzing sales data, creating reports, generating charts, or processing e-comm...

Security Scan

The code included (analyze_data.py) looks benign and matches the core description, but the skill's documentation (SKILL.md/README) advertises many additional scripts and features that are not present and the package provenance is unknown — this mismatch is concerning and warrants caution.

Purpose & Capability

The skill claims multiple scripts and e-commerce-specific analyses (analyze_sales.py, generate_charts.py, inventory_analysis.py, customer_analysis.py) and many features, but the bundle only contains a single script (scripts/analyze_data.py). No homepage or source repository is provided and the owner identity is opaque. The declared metadata lists no required env vars or binaries, yet SKILL.md refers to pandas/matplotlib as required packages. These gaps make the stated purpose and the delivered capability inconsistent.

Instruction Scope

SKILL.md contains runnable examples that invoke several scripts that do not exist in the package; following those examples would fail or cause the agent to search for/attempt to fetch missing files. The instructions also advise installing Python packages (pandas, matplotlib) but there is no controlled install mechanism declared. The instructions do not request any credentials or external endpoints for data exfiltration, and the included analyze_data.py performs only local file I/O and report generation (no network calls).

Install Mechanism

No install specification is present in the registry metadata (lowest risk), but README suggests 'npx clawhub install yinan-data-analyzer' as a user command. The lack of an official install spec in the registry means installations are not reproducibly declared. Missing declared package dependencies (pandas/matplotlib) could lead to ad-hoc pip installs by users or the agent.

Credentials

The skill requests no environment variables, credentials, or config paths — which is proportionate for a local data-processing tool. The included Python script also does not read environment variables or network endpoints. This is appropriate given the claimed functionality.

Persistence & Privilege

The skill does not request elevated persistence (always:false) and the default autonomous invocation is unchanged. It does not attempt to modify other skills or system configuration. No persistence-related red flags were found.

What to consider before installing

Proceed cautiously. The single provided script (scripts/analyze_data.py) appears benign and only reads local files and writes reports, but the documentation advertises many additional scripts and features that are missing and there is no homepage or repository to verify provenance. Before installing or running: (1) ask the publisher for the missing scripts/source repo and verify authenticity; (2) review the code locally (especially for any network access) and run it in an isolated environment or VM on non-sensitive data; (3) avoid giving any credentials — none are required; (4) be prepared to manually install pandas/matplotlib if you need full functionality; and (5) if you expect the extra e-commerce scripts, do not trust this package until the author supplies the actual files or a verified source. If you are uncomfortable with unknown provenance or missing files, do not install or enable autonomous invocation.

Overview

Professional data analysis skill for OpenClaw. Analyze CSV, Excel, and JSON data with statistical functions, visualizations, and automated report generation.

Features

• CSV/Excel/JSON data processing
• Basic statistical analysis
• HTML report generation
• Group-by analysis
• E-commerce data support

Quick Start

Analyze Data

python scripts/analyze_data.py \
  --input sales.csv \
  --output report.html \
  --group-by date

Generate JSON Summary

python scripts/analyze_data.py \
  --input orders.json \
  --output summary.json

Scripts

analyze_data.py

Analyze CSV/Excel/JSON data and generate reports.

Arguments:

• --input - Input data file
• --output - Output report file
• --group-by - Group data by field
• --metrics - Metrics to calculate (comma-separated)
• --format - Output format (html, json)

E-commerce Analytics

Taobao/Douyin Sales Analysis

# Daily sales report
python scripts/analyze_sales.py \
  --input taobao_orders.csv \
  --output daily_report.html \
  --group-by product \
  --metrics revenue,quantity,profit

# Monthly trend analysis
python scripts/generate_charts.py \
  --input monthly_sales.json \
  --charts line \
  --x-axis month \
  --y-axis revenue

Inventory Analysis

python scripts/inventory_analysis.py \
  --input stock_levels.csv \
  --output inventory_report.xlsx \
  --alert-low-stock 10

Customer Analytics

python scripts/customer_analysis.py \
  --input customers.csv \
  --output customer_segments.html \
  --segment-by purchase_frequency

Output Formats

HTML Report

Interactive report with charts and tables.

Excel Workbook

Multiple sheets with raw data, analysis, and charts.

CSV Export

Clean data for further processing.

Templates

Daily Sales Report

• Total revenue
• Order count
• Top products
• Hourly breakdown

Weekly Summary

• Week-over-week comparison
• Trend analysis
• Top categories
• Customer insights

Monthly Executive Report

• KPI dashboard
• Revenue breakdown
• Growth metrics
• Recommendations

Best Practices

1. Clean data first - Remove duplicates, handle missing values
2. Validate inputs - Check data types and ranges
3. Use appropriate charts - Match chart type to data
4. Label clearly - Add titles, axis labels, legends
5. Export in multiple formats - HTML for viewing, CSV for further analysis

Troubleshooting

• Import errors: Install required packages (pandas, matplotlib)
• Memory issues: Process large files in chunks
• Chart rendering: Check output directory permissions
• Date parsing: Ensure consistent date formats