What makes Data Analysis notable?

5.1K downloads. Trust evidence available

How should Data Analysis 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.

agentCLAWHUBUnverified

Data Analysis

AI data analysis and visualization powered by CellCog. Data cleaning, exploratory analysis, hypothesis testing, statistical reports, ML model evaluation, dataset profiling, charts, dashboards. Upload CSVs and get analysis back with full Python access. Skill: Data Analysis Owner: cellcog Summary: AI data analysis and visualization powered by CellCog. Data cleaning, exploratory analysis, hypothesis testing, statistical reports, ML model evaluation, dataset profiling, charts, dashboards. Upload CSVs and get analysis back with full Python access. Tags: latest:1.0.16 Version history: v1.0.16 | 2026-08-24T02:01:10.439Z | user Content updated. v1.0.15 | 2026-08-24T01:45:

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

5.1k

Updated

Oct 9, 2026

Version

1.0.16

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 5.1K downloads reported by the source. Last updated 10/9/2026.

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 Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 5.1K downloadsadoption · observed Oct 9, 2026
Latest release: 1.0.16release · observed Aug 24, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s176q1btpn094ats9b4f9hgfwx83knpk:data-analysis-cellcog

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
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-cellcog-data-analysis-cellcog/snapshot"

Documentation

CLAWHUB

123,581 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: data-analysis-cellcog
description: "AI data analysis and visualization powered by CellCog. Data cleaning, exploratory analysis, hypothesis testing, statistical reports, ML model evaluation, dataset profiling, charts, dashboards. Upload CSVs and get analysis back with full Python access."
metadata:
  openclaw:
    emoji: "🔢"
    os: [darwin, linux, windows]
    requires:
      bins: [python3]
      env: [CELLCOG_API_KEY]
author: CellCog
homepage: https://cellcog.ai
dependencies: [cellcog]
---
# Data Analysis - Your Data Has Answers, CellCog Finds Them

Data analysis and visualization from uploaded files.

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.

## How to Use

For your first CellCog task in a session, read the **cellcog** skill for the full SDK reference — file handling, chat modes, timeouts, and more.

**OpenClaw (fire-and-forget):**
```python
result = client.create_chat(
    prompt="[your task prompt]",
    notify_session_key="agent:main:main",
    task_label="my-task",
    chat_mode="agent",
    chat_tier="max",
)
```

**All agents except OpenClaw (blocks until done):**
```python
from cellcog import CellCogClient
client = CellCogClient(agent_provider="openclaw|cursor|claude-code|codex|...")
result = client.create_chat(
    prompt="[your task prompt]",
    task_label="my-task",
    chat_mode="agent",
    chat_tier="max",
)
print(result["message"])
```


---

## 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</SHO

_meta.json

{
  "ownerId": "kn7a96cj9q65e0bhmzahv790en80ffqm",
  "slug": "data-analysis-cellcog",
  "version": "1.0.16",
  "publishedAt": 1787536870439
}

skill-card.md

## Description:

AI data analysis and visualization powered by CellCog. Data cleaning, exploratory analysis, hypothesis testing, statistical reports, ML model evaluation, dataset profiling, charts, dashboards.

This skill is ready for commercial/non-commercial use.

## Publisher:

[cellcog](https://clawhub.ai/user/cellcog)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, analysts, and other external users use this skill to send datasets to CellCog for cleaning, exploratory analysis, statistical testing, visualization, reporting, and machine learning evaluation.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Selected datasets are uploaded to CellCog, a third-party analysis service.

Mitigation: Use only datasets your organization permits for third-party processing, and redact or minimize secrets, credentials, regulated records, and confidential business data before upload.

Risk: Setup guidance may use latest-version dependency installation commands.

Mitigation: Pin or verify dependency versions where reproducibility or supply-chain review is required.

## Reference(s):

- [CellCog homepage](https://cellcog.ai)
- [ClawHub skill page](https://clawhub.ai/cellcog/skills/data-analysis-cellcog)
- [CellCog publisher profile](https://clawhub.ai/user/cellcog)

## Skill Output:

**Output Type(s):** [text, markdown, code, shell commands, configuration, guidance, files]

**Output Format:** [Markdown guidance with Python examples, API call patterns, and references to generated analysis artifacts such as charts, dashboards, reports, and cleaned datasets.]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Uses uploaded datasets and a CellCog API key; outputs may include HTML dashboards, PDF reports, CSV or XLSX files, and markdown summaries depending on the user request.]

## Skill Version(s):

1.0.16 (source: ClawHub release evidence)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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