A professional-grade data analysis tool designed to interpret datasets, assess quality, and generate structured insights using business-centric logic.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install famou-data-analysis
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 famou-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 specialized capability within the Openclaw Skills ecosystem designed for deep data exploration and processing. It goes beyond simple file reading by emphasizing the understanding of business context, assessing data integrity, and identifying meaningful patterns, trends, and correlations.
This skill serves as a bridge between raw datasets (like CSV or Excel files) and high-level decision-making. It follows a strict methodology that prioritizes data quality and user confirmation, ensuring that every cleaning or transformation step is justified and documented for reproducibility.
Data Analysis Use Cases
- Explaining complex datasets to understand business implications.
- Identifying and fixing data quality issues like missing values, duplicates, or outliers.
- Performing statistical analysis and trend discovery on sales or survey data.
- Generating structured data processing pipelines and cleaned output files.
- Creating comprehensive data reports with actionable insights supported by evidence.
How Data Analysis Works
- Initial Contextual Understanding: The skill clarifies the business scenario and the user's specific goals before touching the data.
- Data Quality Assessment: It identifies structural issues, anomalies, and missing information, categorizing them by severity (blocking, decision-required, or minor).
- Iterative Processing: The skill proposes cleaning and transformation plans, seeking user approval for any modifications to the original data.
- Analytical Exploration: Using statistical tools and visualization best practices, it uncovers patterns and trends relevant to the user's objectives.
- Structured Reporting: It synthesizes findings into a formal report featuring data overviews, core insights, and future recommendations.
Data Analysis Setup
To utilize this skill within the Openclaw Skills framework, ensure your environment is configured for Python-based data processing:
# Standard data directory structure
mkdir -p /mnt/user-data/uploads/
mkdir -p /mnt/user-data/outputs/
# Recommended environment settings for visualization
# In your analysis script:
# matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
Simply upload your CSV or Excel files to the uploads directory and trigger the skill by asking to analyze the data.
Data Analysis Data Schema & Taxonomy
The skill manages data through a structured input/output taxonomy and specific encoding standards:
| Feature | Specification |
|---|---|
| Input Path | /mnt/user-data/uploads/ |
| Output Path | /mnt/user-data/outputs/ |
| Encoding | Primary: utf-8, Secondary: gbk / gb18030 |
| Excel Exports | Uses utf-8-sig to prevent character corruption |
| Reporting | Standardized Markdown structure (Overview, Quality, Findings, Process, Suggestions) |
Data Analysis Advanced Features
- Multi-stage data quality grading (Blocking vs. Decision-required).
- Integrated support for Chinese character rendering in visualizations and CSV exports.
- Automatic detection and correction of inconsistent date formats (e.g., mixing MM/DD/YYYY and YYYY-MM-DD).
- Detailed change-log generation documenting the exact delta between raw and cleaned datasets.
- Support for multi-sheet Excel processing with cross-sheet consistency checks.
SKILL.md
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