A comprehensive workspace template designed to standardize data science projects, from exploratory analysis to model deployment.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install fumi-data-analysis-workflow
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 fumi-data-analysis-workflow 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 Workflow?
The Data Analysis Workflow is a specialized template for Openclaw Skills designed to streamline the entire lifecycle of a data science project. By providing a structured environment for AI agents, it ensures that data-driven decision-making, exploratory data analysis (EDA), and model development follow industry best practices. This skill transforms a standard AI assistant into a specialized DataBot, equipped with the analytical mindset and practical tools required for professional-grade insights.
This workflow is essential for developers and data scientists who want to maintain high standards of reproducibility and experimentation. It integrates seamlessly with popular Python libraries and data tools, providing a consistent framework for managing datasets, recording experiment progress, and generating clear, visual reports.
Data Analysis Workflow Use Cases
- Automating Exploratory Data Analysis (EDA) to uncover hidden patterns in new datasets.
- Setting up standardized environments for machine learning model development and evaluation.
- Creating marketing data assistants that provide business-centric insights from complex metrics.
- Managing reproducible research by maintaining strict logs of data cleaning and experiment results.
How Data Analysis Workflow Works
- The user installs the workflow via the CLI to add the template to their local skill library.
- A dedicated project workspace is created to house the data and analysis scripts.
- The template files (SOUL, IDENTITY, etc.) are copied into the workspace to define the agent's behavior and rules.
- The user customizes the metadata and agent profile to match the specific domain, such as Marketing or ML Engineering.
- The agent executes the lifecycle stages: data collection, cleaning, feature engineering, modeling, and visualization.
- Periodic checks are performed via the heartbeat mechanism to record progress and document results.
Data Analysis Workflow Setup
To get started with this skill, use the following commands in your terminal:
# Install the workflow
clawhub install data-analysis-workflow
# Create your project workspace
mkdir -p ~/.openclaw/workspace/my-data-analysis
# Copy the templates to your new workspace
cp -r ~/.openclaw/workspace/skills/data-analysis-workflow/templates/* \
~/.openclaw/workspace/my-data-analysis/
# Navigate to the folder and customize your configuration files
cd ~/.openclaw/workspace/my-data-analysis
Data Analysis Workflow Data Schema & Taxonomy
The Data Analysis Workflow organizes project assets into a clear hierarchy to ensure transparency and reproducibility:
| File | Purpose |
|---|---|
| SOUL.md | Defines the core principles, such as prioritizing data quality and reproducibility. |
| IDENTITY.md | Sets the agent's persona (e.g., DataBot), tone, and specialized type. |
| AGENTS.md | Contains the operational rules for data management and experimental logging. |
| USER.md | Stores user preferences, expertise levels, and specific analysis goals. |
| HEARTBEAT.md | Manages periodic check-ins, experiment tracking, and result documentation. |
Data Analysis Workflow Advanced Features
- Multi-persona support: Easily switch between a Business Analyst and an ML Engineer profile.
- Comprehensive Tool Integration: Native support for Pandas, Scikit-learn, SQL, and MLflow.
- Automated Documentation: Generates data dictionaries and model specification sheets as part of the workflow.
- Integrated Visualization: Pre-configured hooks for Matplotlib, Seaborn, and Plotly to ensure high-quality reporting.
SKILL.md
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