Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Added clear author and dependency metadata to SKILL.md. - Changed prerequisite wording to reference the `cellcog` skill directly. - No functional changes; documentation improvements only.
What This Skill Does
CellCog is a data analysis agent that executes Python code server-side and returns results directly: charts, cleaned datasets, statistical reports, and interactive dashboards. Upload CSV, Excel, JSON, Parquet, or SQL exports and describe what you want to learn. It handles exploratory analysis, data cleaning, statistical testing, ML model training, and visualization without requiring you to run any code yourself.
Most AI tools return code and leave execution to you; CellCog runs the code and returns the actual charts, cleaned files, and conclusions.
When to use it
- Profiling an unfamiliar CSV to understand distributions, missing values, and outliers
- Checking A/B test statistical significance and getting a ship-or-keep recommendation
- Training a customer churn prediction model and seeing feature importance
- Building an interactive HTML sales dashboard filterable by region and product
- Cleaning mixed date formats, dollar-sign values, and duplicate rows in a dataset
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Analyze this dataset and tell me everything interesting. [attaches customer_data.csv]
- 1Profiles the dataset: row count, column types, missing values, and duplicates
- 2Computes summary statistics and key distributions for each column
- 3Identifies correlations and flags outliers or data quality issues
- 4Generates charts for the most significant findings
- 5Assembles results into an interactive HTML report with annotated insights
Interactive HTML report with distribution charts, a correlation heatmap, flagged outliers, and 3-5 plain-English insights extracted from the data