A sophisticated data processing skill that automates financial analysis, KPI tracking, and professional report generation from raw business datasets.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install auto-data-analysis-claw
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 auto-data-analysis-claw 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 Automated Data Analysis Claw?
The Automated Data Analysis Claw is a powerful extension designed for deep-dive analysis of financial and operational data. By integrating into the Openclaw Skills ecosystem, this tool handles the entire lifecycle of data management, from initial quality profiling to the generation of executive-ready reports. It is specifically built to assist developers and analysts in extracting value from complex datasets like profit and loss statements, balance sheets, and sales records.
This skill leverages Python-based automation to perform logical business processing that would otherwise take hours of manual effort. It ensures technical accuracy by applying standardized analytical patterns, making it a reliable choice for organizations looking to scale their data-driven decision-making processes.
Automated Data Analysis Claw Use Cases
- Automating monthly or quarterly financial statement analysis including profit and loss and cash flow reports.
- Calculating complex business KPIs such as gross margin percentages, order volumes, and revenue growth.
- Performing trend and variance analysis to identify period-over-period performance shifts.
- Cleaning and profiling large CSV or Excel datasets to ensure data integrity before reporting.
- Generating actionable business insights and executive summaries for stakeholders.
How Automated Data Analysis Claw Works
- Requirement Gathering: The skill identifies data sources, analysis goals, and comparison baselines such as budgets or prior periods.
- Data Profiling: It executes a profiling script to check for null values, data types, and numerical ranges to ensure data quality.
- Automated Cleaning: Missing values are handled using median or mode imputation, and redundant data is purged.
- Core Analysis: The agent runs specialized modules for trend analysis, correlation, and variance detection based on user requirements.
- Domain Integration: It references standardized financial and business metric libraries to ensure formulas align with industry best practices.
- Report Generation: A final structured report is produced in Markdown or HTML format using a JSON-based configuration.
Automated Data Analysis Claw Setup
To utilize this skill within the Openclaw Skills framework, ensure you have pandas and numpy installed. You can initiate data profiling and cleaning using the following CLI commands:
# Profile your data source
python3 scripts/analyze.py profile your_data.csv
# Clean your data for analysis
python3 scripts/analyze.py clean input.csv -o cleaned_output.csv
Automated Data Analysis Claw Data Schema & Taxonomy
The skill organizes data through structured JSON configurations to ensure repeatable and accurate reporting.
| Component | Type | Description |
|---|---|---|
| KPI Config | JSON | Defines metrics using formulas like sum(), mean(), or count(). |
| Report Config | JSON | Outlines the title, metadata, and sections of the final document. |
| References | Markdown | Uses external files for financial metrics and business patterns. |
| Data Formats | CSV/Excel | Supports standard flat-file formats for ingestion. |
Automated Data Analysis Claw Advanced Features
- Custom KPI engine allowing users to define complex formulas via JSON configuration.
- Multi-format output support for both developer-friendly Markdown and stakeholder-ready HTML reports.
- Intelligent anomaly detection that alerts users to data points deviating from expected ranges.
- Sampling logic for large datasets exceeding 100MB to maintain processing efficiency.
- Integration with specialized reference templates for consistent professional formatting across different business units.
SKILL.md
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