What makes Data Analysis Plus notable?

1.2K downloads. Trust evidence available

How should Data Analysis Plus 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?

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agentCLAWHUBUnverified

Data Analysis Plus

Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, re... Skill: Data Analysis Plus Owner: 534422530 Summary: Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, re... Tags: latest:2.0.0 Version history: v2.0.0 | 2026-06-05T04:26:36.178Z | auto data-analysis-plus 2.0.0 introduces major enhancements: - Expanded features: now includes code templates in both Python and R, a v

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

2.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

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 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 2.0.0release · observed Jun 5, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170k9770tgh0506hw0dwtb6pd83kwyq:data-analysis-plus

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-534422530-data-analysis-plus/snapshot"

Documentation

CLAWHUB

11,475 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: data-analysis-plus
description: "Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation. Covers hypothesis testing, regression, clustering, time series, and more."
metadata:
  author: opencode
  version: 2.0
  tags: data-analysis, statistics, visualization, python, r
  compatibility: opencode
  license: MIT
---

# Data Analysis Plus

Enhanced data analysis with code templates, visualization gallery, and statistical methods.

## Features

- **Code Templates**: Python/R ready-to-use templates
- **Visualization Gallery**: Charts for every analysis type
- **Statistical Methods**: Hypothesis testing, regression, clustering
- **Automated Reports**: Decision-ready output formats
- **Data Validation**: Quality checks before analysis

## Quick Reference

| Analysis Type | Python Template | R Template |
|---------------|-----------------|------------|
| Descriptive | `df.describe()` | `summary(df)` |
| Hypothesis | `scipy.stats.ttest_ind()` | `t.test()` |
| Regression | `sklearn.linear_model` | `lm()` |
| Clustering | `sklearn.cluster.KMeans` | `kmeans()` |
| Time Series | `statsmodels.tsa` | `forecast::auto.arima()` |

## Python Templates

### Data Loading

```python
import pandas as pd
import numpy as np

# CSV
df = pd.read_csv("data.csv")

# Excel
df = pd.read_excel("data.xlsx")

# JSON
df = pd.read_json("data.json")

# Database
import sqlalchemy
engine = sqlalchemy.create_engine("sqlite:///data.db")
df = pd.read_sql("SELECT * FROM table", engine)
```

### Descriptive Statistics

```python
# Basic stats
df.describe()

# By group
df.groupby("category").agg({
    "value": ["mean", "median", "std", "count"]
})

# Correlation
df.corr()
```

### Data Cleaning

```python
# Missing values
df.isnull().sum()
df.fillna(df.mean())
df.dropna()

# Duplicates
df.duplicated().sum()
df.drop_duplicates()

# Outliers
Q1 = df["value"].quantile(0.25)
Q3 = df["value"].quantile(0.75)
IQR = Q3 - Q1
df = df[(df["value"] >= Q1 - 1.5*IQR) & (df["value"] <= Q3 + 1.5*IQR)]
```

### Hypothesis Testing

```python
from scipy import stats

# T-test
group1 = df[df["group"] == "A"]["value"]
group2 = df[df["group"] == "B"]["value"]
stat, p_value = stats.ttest_ind(group1, group2)

# Chi-square
contingency = pd.crosstab(df["cat1"], df["cat2"])
chi2, p_value, dof, expected = stats.chi2_contingency(contingency)

# ANOVA
f_stat, p_value = stats.f_oneway(group1, group2, group3)
```

### Regression

```python
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

X = df[["feature1", "feature2"]]
y = df["target"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = LinearRegression()
model.fit(X_train, y_train)

print(f"R²: {model.score(X_test, y_test)}")
print(f"Coefficients: {model.coef_}")
```

### Clustering

```python
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_sca

_meta.json

{
  "ownerId": "kn71pk44ca87scz3pstt90r66n80xhaa",
  "slug": "data-analysis-plus",
  "version": "2.0.0",
  "publishedAt": 1780633596178
}

skill-card.md

## Description:

Enhanced data analysis with Python/R code templates, visualization gallery, statistical tests, and automated report generation.

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

## Publisher:

[534422530](https://clawhub.ai/user/534422530)

### License/Terms of Use:

MIT-0

## Use Case:

Developers, analysts, and data practitioners use this skill to generate Python and R templates for data loading, cleaning, statistical analysis, visualization, and report drafting.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Visualization snippets save PNG files such as trend.png, scatter.png, and heatmap.png in the working directory, which can overwrite existing files.

Mitigation: Review and change output paths or filenames before running copied snippets.

Risk: Generated statistical templates can support misleading conclusions if data quality, assumptions, uncertainty, or sampling bias are not checked.

Mitigation: Validate the data, document assumptions, report uncertainty, and review results before using the analysis for decisions.

## Reference(s):


## Skill Output:

**Output Type(s):** [Text, Markdown, Code, Guidance]

**Output Format:** [Markdown with Python, R, and report-template code blocks]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May include copied examples that write local chart image files.]

## Skill Version(s):

2.0.0 (source: 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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