A professional analytical framework that transforms raw data into decision-ready insights using statistical rigor and standardized reporting templates.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install super-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 super-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 Super Data Analysis?
The Super Data Analysis skill is a high-performance framework designed to elevate AI coding agents from simple arithmetic to strategic analysis. By integrating this into your Openclaw Skills ecosystem, agents can handle complex tasks such as KPI debugging, experiment readouts, and cohort analysis with a methodology-first approach. It focuses on the principle that analysis without a decision is just arithmetic, ensuring every data point serves a tangible business outcome.
This skill provides structured guidance for metric definitions, visual selection, and executive reporting. It helps developers and data scientists avoid common pitfalls like p-hacking or misleading visualizations by enforcing a strict methodology that separates data extraction, transformation, and interpretation.
Super Data Analysis Use Cases
- Performing deep-dive cohort and churn analysis to understand user retention patterns.
- Debugging KPI anomalies and identifying the root cause of metric fluctuations.
- Designing and interpreting A/B testing experiments with statistical confidence.
- Generating executive-ready decision briefs from raw SQL queries or spreadsheet exports.
- Selecting the most effective chart types to communicate specific analytical findings clearly.
How Super Data Analysis Works
- Decision Alignment: The process begins by identifying the core decision the analysis supports and the timeframe required.
- Metric Contracting: Define the grain, numerator, denominator, and source of truth to ensure consistency across the dataset.
- Logical Extraction: Query logic is developed separately from cleanup assumptions to maintain transparency.
- Statistical Validation: The analysis is stress-tested for sample size sufficiency, confounding variables, and effect size.
- Brief Generation: Results are delivered in a structured decision format including evidence, confidence levels, and actionable next steps.
Super Data Analysis Setup
To add this capability to your agent, use the following commands to manage your Openclaw Skills library:
clawhub install super-data-analysis
clawhub sync
No external network requests or local folder persistence are required, making it a secure and lightweight addition to your workflow.
Super Data Analysis Data Schema & Taxonomy
The skill utilizes a set of reference markdown files to organize its metadata and taxonomy for high-quality outputs:
| Component | File Reference | Purpose |
|---|---|---|
| KPI Definitions | metric-contracts.md | Ensures consistency in denominators and timezones. |
| Visualization | chart-selection.md | Guides the choice of trend, comparison, or distribution charts. |
| Stakeholder Output | decision-briefs.md | Formats answers with evidence and recommendations. |
| Quality Control | pitfalls.md | Lists failure modes to catch early in the analysis process. |
| Methodology | techniques.md | Maps specific questions to statistical approaches like regression or hypothesis testing. |
Super Data Analysis Advanced Features
- Metric Contract Locking: Prevents trend invalidation by standardizing definitions before calculations begin.
- Multi-Skill Integration: Works in tandem with SQL and CSV Openclaw Skills for robust end-to-end data pipelines.
- Uncertainty Quantification: Moves beyond point estimates to provide ranges and confidence intervals for better risk assessment.
- Decision-Ready Hooks: Automatically structures output for stakeholders, translating technical methods into business implications.
- Robustness Checklists: Built-in safeguards to flag small sample sizes or unreliable data sources before recommendations are made.
SKILL.md
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