data-scientist

What This Skill Does

The data-scientist skill acts as an advanced analytical engine integrated directly into your OpenClaw environment. It empowers users to perform complex statistical modeling, rigorous experiment design, and deep-dive customer behavior analysis. Whether you are conducting A/B testing to optimize conversion rates, employing causal inference to understand the impact of specific interventions, or building predictive models for Customer Lifetime Value (CLV) and churn, this skill provides the necessary mathematical framework and procedural structure. It handles the end-to-end analytical pipeline, ensuring that data-driven insights are actionable and scientifically sound.

Installation

To integrate this skill into your OpenClaw agent, execute the following command in your terminal: clawhub install openclaw/skills/skills/anton-abyzov/sw-data-scientist Ensure you have the latest version of ClawHub installed to avoid dependency conflicts.

Use Cases

• Experimentation: Designing randomized controlled trials and A/B/n tests with power analysis to ensure statistical significance.
• Customer Analytics: Predicting churn probability and calculating long-term value to optimize marketing spend and retention strategies.
• Forecasting: Utilizing time-series decomposition and ARIMA/Prophet methodologies to project sales, inventory requirements, or traffic patterns.
• Causal Inference: Determining the true impact of marketing campaigns or product changes using propensity score matching or difference-in-differences methods.

Example Prompts

1. "Perform an exploratory data analysis on the provided e-commerce dataset, focusing on identifying key drivers for customer churn over the last six months."
2. "Design an A/B test for our new checkout flow. Calculate the required sample size to achieve 80% power and a 0.05 significance level given our current conversion rate."
3. "Build a time-series forecasting model for our Q4 revenue based on the historical CSV files uploaded, accounting for seasonality and major holiday spikes."

Tips & Limitations

• Chunking Policy: For large-scale projects exceeding 800 lines of code or complex data processing, the agent follows a strict multi-phase workflow. Please wait for the agent to complete the current phase (e.g., EDA) before requesting the next (e.g., Modeling).
• Data Privacy: Ensure sensitive customer data is anonymized before processing. While the agent operates locally, data handling should always adhere to your organization's security standards.
• Computational Bounds: The skill is optimized for statistical software libraries; avoid passing unstructured, massive datasets that exceed local memory limits without pre-aggregation.