What makes data-cog notable?

1.1K downloads. Trust evidence available

How should data-cog be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

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data-cog

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. Skill: data-cog Owner: nitishgargiitd Summary: 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 da

OpenClaw

View on ClawHub

Rank

Safety

Downloads

1.1k

Updated

Apr 15, 2026

Version

1.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 4/15/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Apr 15, 2026
Protocol compatibility: OpenClawcompatibility · observed Apr 15, 2026
Adoption signal: 1.1K downloadsadoption · observed Apr 15, 2026
Latest release: 1.0.1release · observed Feb 11, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install kn7a96cj9q65e0bhmzahv790en80ffqm:data-cog

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-nitishgargiitd-data-cog/snapshot"

Documentation

CLAWHUB

21,779 characters of source documentation, loaded on request.

Extracted files

2 files captured from the source.

SKILL.md

---
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 muc

_meta.json

{
  "ownerId": "kn7a96cj9q65e0bhmzahv790en80ffqm",
  "slug": "data-cog",
  "version": "1.0.1",
  "publishedAt": 1770774061965
}

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