A powerful AI skill that converts raw business data into plain-language executive summaries and actionable analytical reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-analysis-reporting
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 data-analysis-reporting 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 & Reporting?
This skill acts as a sharp junior analyst for your AI agent, capable of processing diverse data formats like CSVs, SQLite databases, and tabular markdown. It bridges the gap between raw numbers and business decisions by providing context-aware insights, identifying trends, and flagging statistical confidence. By using these Openclaw Skills, developers can empower agents to handle complex business metrics without human intervention.
It is designed specifically for small business operators and analysts who need to turn noise into signal. The skill prioritizes data integrity by running comprehensive quality checks before analysis and ensures all findings are delivered in plain language, making it easier for decision-makers to act on the information provided.
Data Analysis & Reporting Use Cases
- Analyzing monthly recurring revenue (MRR) and customer churn trends.
- Generating executive summaries for leadership based on performance data.
- Validating data quality and identifying outliers in business datasets.
- Comparing performance across different time periods or market segments.
- Providing data-driven briefings for SQLite databases or uploaded spreadsheets.
How Data Analysis & Reporting Works
- Requirements Clarification: The agent asks up to three clarifying questions to understand the business decision context and target audience.
- Ingestion and Validation: Parses data and runs quality checks, reporting any missing values or inconsistencies before proceeding.
- Analysis Planning: Proposes a specific plan (e.g., MoM growth rates) to ensure the user agrees with the approach.
- Execution: Runs statistical analyses tailored to the data volume, including trend identification and distribution analysis.
- Insight Translation: Converts complex numbers into findings ranked by business impact rather than just statistical significance.
- Report Delivery: Generates a structured output with executive summaries, findings, and confidence indicators.
Data Analysis & Reporting Setup
To integrate this skill into your agent's workflow, include the skill definition and reference documents in your project directory.
# Navigate to your agent's skills folder
cd your-agent/skills
# Download the data-analysis-reporting skill
curl -O https://raw.githubusercontent.com/openclaw/skills/main/data-analysis-reporting/SKILL.md
Ensure your agent has access to the references for business metrics and data quality checks to enable full functionality of Openclaw Skills.
Data Analysis & Reporting Data Schema & Taxonomy
The skill uses a structured reporting schema to ensure clarity and professional presentation:
| Section | Description |
|---|---|
| Executive Summary | High-level findings answering the core question in plain language. |
| Data Quality Notes | Details on cleaning, row counts, and potential data gaps. |
| Key Findings | Fact, implication, and confidence level for each major insight. |
| Trend Analysis | Time-series patterns including direction, magnitude, and acceleration. |
| Watch Items | Emerging negative trends or thresholds that need monitoring. |
| Methodology | Explicit record of formulas and assumptions used for metrics. |
Data Analysis & Reporting Advanced Features
- Automatic PII (Personally Identifiable Information) detection and exclusion to ensure data privacy and compliance.
- Dynamic analysis depth calibration that scales complexity based on row count and data quality.
- Specialized handling for common business metrics like CAC, LTV, and churn rates using Openclaw Skills logic.
- Explicit confidence labeling system to distinguish between hard facts and directional inferences.
- Multi-dataset merging capabilities with orphaned record detection and relationship mapping.
SKILL.md
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