data-cog

---
name: data-cog
description: "Your data has answers. CellCog asks the right questions. #1 on DeepResearch Bench (Feb 2026) + frontier coding agent — upload messy CSVs with minimal prompting and get structured insights back: charts, dashboards, statistical reports, and clean data. Full Python access for data cleaning, exploratory analysis, visualization, hypothesis testing, ML model evaluation, and dataset profiling. Analyzes everything, presents it beautifully."
metadata:
  openclaw:
    emoji: "🔢"
author: CellCog
dependencies: [cellcog]
---

# Data Cog - Your Data Has Answers, CellCog Finds Them

**Your data has answers. CellCog asks the right questions.** #1 on DeepResearch Bench (Feb 2026) + frontier coding agent.

Most AI tools return code when you ask about data. CellCog returns answers — actual charts, clean datasets, statistical reports, and visual dashboards. Upload messy CSVs with a minimal prompt, and CellCog's coding agent explores your data, finds the patterns, and presents them beautifully. Full Python access for everything from data cleaning to ML model evaluation.

---

## Prerequisites

This skill requires the `cellcog` skill for SDK setup and API calls.

```bash
clawhub install cellcog
```

**Read the cellcog skill first** for SDK setup. This skill shows you what's possible.

**Quick pattern (v1.0+):**
```python
# Fire-and-forget - returns immediately
result = client.create_chat(
    prompt="Analyze this dataset: <SHOW_FILE>/path/to/data.csv</SHOW_FILE>",
    notify_session_key="agent:main:main",
    task_label="data-analysis",
    chat_mode="agent"  # Agent mode for most data work
)
# Daemon notifies you when complete - do NOT poll
```

---

## What Makes Data-Cog Different

### Code as Tool, Not as Output

Other AI tools give you Python code and say "run this." CellCog **runs the code for you** and delivers the results:

| Other AI Tools | Data-Cog |
|---------------|----------|
| "Here's a pandas script to analyze your data" | Here are your actual insights with charts |
| "Run this matplotlib code to see the chart" | Here's the chart, annotated with findings |
| "This SQL query will find outliers" | Found 23 outliers, here's what they mean |
| "You'll need scikit-learn for this" | Model trained, here's accuracy and feature importance |

You upload data. You get answers. The code runs behind the scenes.

---

## What Data Work You Can Do

### Exploratory Data Analysis

Understand your data fast:

- **Dataset Profiling**: "Analyze this CSV — distributions, missing values, outliers, correlations, and data quality summary"
- **Pattern Discovery**: "What patterns and trends exist in this sales data? Surprise me."
- **Anomaly Detection**: "Find unusual patterns in this server log data — what looks abnormal?"
- **Relationship Analysis**: "What factors most strongly correlate with customer churn in this dataset?"

**Example prompt:**
> "Analyze this dataset:
> <SHOW_FILE>/path/to/customer_data.csv</SHOW_FILE>
> 
> I don't know much about this data yet. Give me:
> - Overview: rows, columns, data types, missing values
> - Key distributions and summary statistics
> - Most interesting correlations
> - Any outliers or data quality issues
> - 3-5 insights that jump out
> 
> Present findings as an interactive HTML report with charts."

### Data Cleaning & Transformation

Wrangle messy data into shape:

- **Clean Messy Data**: "Clean this CSV — fix inconsistent date formats, handle missing values, remove duplicates, standardize column names"
- **Data Transformation**: "Pivot this transaction data into a monthly summary by product category"
- **Data Merging**: "Join these three CSV files on customer_id and create a unified dataset"
- **Feature Engineering**: "Create useful features from this raw data for predicting house prices"

**Example prompt:**
> "Clean and transform this dataset:
> <SHOW_FILE>/path/to/messy_data.csv</SHOW_FILE>
> 
> Issues I know about:
> - Dates are in mixed formats (MM/DD/YYYY and YYYY-MM-DD)
> - 'Revenue' column has some values with $ signs and commas
> - Duplicate rows exist
> - Missing values in 'Region' column
> 
> Clean it up and give me back a clean CSV plus a summary of what you changed."

### Statistical Analysis

Rigorous analysis with real numbers:

- **Hypothesis Testing**: "Is there a statistically significant difference in conversion rates between our A and B variants?"
- **Regression Analysis**: "What factors predict employee salary in this HR dataset? Build a regression model."
- **Time Series Analysis**: "Analyze this monthly revenue data — trend, seasonality, and forecast next 6 months"
- **Cohort Analysis**: "Create a cohort analysis showing user retention by signup month"

**Example prompt:**
> "I ran an A/B test on our checkout page:
> <SHOW_FILE>/path/to/ab_test_results.csv</SHOW_FILE>
> 
> Columns: user_id, variant (A or B), converted (0/1), revenue, timestamp
> 
> Tell me:
> - Is variant B statistically better? (p-value, confidence interval)
> - Conversion rate difference
> - Revenue per user difference
> - Sample size adequacy check
> - My recommendation: ship B or keep testing?
> 
> Present with clear charts and a plain-English conclusion."

### Visualization & Reporting

Turn data into visual stories:

- **Chart Generation**: "Create a set of charts showing our quarterly performance from this data"
- **Dashboard Reports**: "Build an interactive dashboard from this sales dataset with filters by region and product"
- **Presentation-Ready Visuals**: "Create publication-quality charts from this research data"
- **Comparison Visuals**: "Visualize how our metrics compare to industry benchmarks"

### Machine Learning

Applied ML without the setup:

- **Classification**: "Predict which customers will churn based on this dataset — train a model, show feature importance"
- **Clustering**: "Segment these customers into groups based on behavior — how many natural clusters exist?"
- **Forecasting**: "Forecast next quarter's sales using this historical data"
- **Model Evaluation**: "I trained a model — here are the predictions. Evaluate: accuracy, precision, recall, confusion matrix, ROC curve"

**Example prompt:**
> "Predict customer churn from this dataset:
> <SHOW_FILE>/path/to/customer_features.csv</SHOW_FILE>
> 
> Target column: 'churned'
> 
> - Train a model, try at least 2 algorithms
> - Show feature importance — what drives churn?
> - Confusion matrix and ROC curve
> - Plain-English summary: 'The top 3 reasons customers churn are...'
> - Actionable recommendations based on findings
> 
> I want insights, not just metrics."

---

## Supported Data Formats

| Format | How to Send |
|--------|-------------|
| **CSV** | Upload via SHOW_FILE |
| **Excel (XLSX)** | Upload via SHOW_FILE |
| **JSON** | Upload via SHOW_FILE |
| **Parquet** | Upload via SHOW_FILE |
| **SQL exports** | Upload the dump via SHOW_FILE |
| **Inline data** | Describe small datasets directly in prompt |

---

## Output Formats

| Format | Best For |
|--------|----------|
| **Interactive HTML Dashboard** | Explorable charts, filters, drill-downs |
| **PDF Report** | Shareable analysis reports with charts and findings |
| **Clean CSV/XLSX** | Cleaned or transformed data files for downstream use |
| **Markdown** | Quick insights for integration into docs |

---

## Chat Mode for Data

| Scenario | Recommended Mode |
|----------|------------------|
| Quick data cleaning, simple charts, basic statistics | `"agent"` |
| Deep analysis with multiple techniques, ML modeling, comprehensive reports | `"agent team"` |

**Use `"agent"` for most data work.** Data cleaning, EDA, chart generation, and standard statistical analysis execute well in agent mode.

**Use `"agent team"` for complex analytical projects** — multi-technique analysis, ML model comparisons, or when you need deep domain reasoning about what the data means.

---

## Example Prompts

**Minimal prompt, maximum insight:**
> "Analyze this:
> <SHOW_FILE>/path/to/data.csv</SHOW_FILE>