Fox Data Analyst

Data visualization, report generation, SQL queries, and spreadsheet automation. Transform your AI agent into a data-savvy analyst that turns raw data into ac...

Security Scan

The skill largely matches a local data-analysis helper, but there are mismatches between its claims and what it actually requires (undeclared DB clients/env usage and unimplemented cloud integrations), so review before installing or running.

Purpose & Capability

The skill's name, description, SKILL.md and included scripts align with a data-analysis helper for local files and SQL. However the README and description claim Google Sheets / BigQuery / Snowflake integrations that are not implemented in the shipped files. Conversely, the shipped query.sh expects database client binaries (sqlite3/psql/mysql) and a DB_CONNECTION environment variable even though the registry metadata lists no required binaries or env vars.

Instruction Scope

Runtime instructions are mostly scoped to creating a workspace (~/.openclaw/workspace/data-analysis), templates, and running queries. The scripts instruct running arbitrary SQL on user-provided connections (expected for the purpose). SKILL.md references configuring TOOLS.md (which is not present) and mentions Google Sheets/Cloud warehouses without providing code or instructions for authenticating those services. The scripts read DB_CONNECTION/DB_TYPE from the environment or flags, which is expected but not declared.

Install Mechanism

There is no install spec and no network downloads; the skill is instruction-only plus two small local scripts. No archives or remote URLs are fetched, and nothing is written outside the user's home workspace created by data-init.sh.

Credentials

The skill's metadata declares no required env vars, yet scripts rely on DB_CONNECTION and DB_TYPE. DB connection strings commonly contain sensitive credentials — the skill does not request or document these in metadata. Additionally, the description claims cloud integrations (Google Sheets, BigQuery, Snowflake) but no environment variables or auth instructions are provided for those services, which is inconsistent.

Persistence & Privilege

always:false and the skill does not request system-wide privileges. The only persistent action is data-init.sh creating a workspace under the user's HOME (~/.openclaw/workspace/data-analysis), which is confined to the user's account and is reasonable for a local helper.

What to consider before installing

This skill appears to be a local data-analysis helper and the included scripts are readable and benign, but there are a few mismatches you should be aware of before running: - The query tool expects DB clients (sqlite3/psql/mysql) and a DB_CONNECTION environment variable; ensure those clients are present and understand that DB_CONNECTION can contain sensitive credentials (do not pass secrets you don't trust). - The description mentions Google Sheets/BigQuery/Snowflake but the package provides no authentication code or instructions for those services — treat those claims as aspirational, not implemented. - data-init.sh will create ~/.openclaw/workspace/data-analysis and write templates/scripts there; review the files it creates and run the script in a controlled environment if you want to inspect behavior first. Recommended steps before installing/running: 1. Inspect scripts locally (you already have them) and confirm they match your expectations. 2. Run data-init.sh in a non-production account or container to verify workspace creation. 3. Do not export production DB credentials into DB_CONNECTION until you confirm the tool is configured as you expect; prefer using a readonly/test database for initial runs. 4. If you need Google Sheets / cloud warehouse functionality, request clarification from the author or only use trusted, audited code that implements proper auth flows. Given these inconsistencies (undeclared env usage and unimplemented integrations) the package is suspicious rather than clearly benign; these issues could be benign oversights but warrant caution.

Data Analyst Skill 📊

Turn your AI agent into a data analysis powerhouse.

Query databases, analyze spreadsheets, create visualizations, and generate insights that drive decisions.

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What This Skill Does

✅ SQL Queries — Write and execute queries against databases ✅ Spreadsheet Analysis — Process CSV, Excel, Google Sheets data ✅ Data Visualization — Create charts, graphs, and dashboards ✅ Report Generation — Automated reports with insights ✅ Data Cleaning — Handle missing data, outliers, formatting ✅ Statistical Analysis — Descriptive stats, trends, correlations

---

Quick Start

1. Configure your data sources in TOOLS.md:

### Data Sources
- Primary DB: [Connection string or description]
- Spreadsheets: [Google Sheets URL / local path]
- Data warehouse: [BigQuery/Snowflake/etc.]

2. Set up your workspace:

./scripts/data-init.sh

3. Start analyzing!

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SQL Query Patterns

Common Query Templates

Basic Data Exploration

-- Row count
SELECT COUNT(*) FROM table_name;

-- Sample data
SELECT * FROM table_name LIMIT 10;

-- Column statistics
SELECT 
    column_name,
    COUNT(*) as count,
    COUNT(DISTINCT column_name) as unique_values,
    MIN(column_name) as min_val,
    MAX(column_name) as max_val
FROM table_name
GROUP BY column_name;

Time-Based Analysis

-- Daily aggregation
SELECT 
    DATE(created_at) as date,
    COUNT(*) as daily_count,
    SUM(amount) as daily_total
FROM transactions
GROUP BY DATE(created_at)
ORDER BY date DESC;

-- Month-over-month comparison
SELECT 
    DATE_TRUNC('month', created_at) as month,
    COUNT(*) as count,
    LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at)) as prev_month,
    (COUNT(*) - LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at))) / 
        NULLIF(LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at)), 0) * 100 as growth_pct
FROM transactions
GROUP BY DATE_TRUNC('month', created_at)
ORDER BY month;

Cohort Analysis

-- User cohort by signup month
SELECT 
    DATE_TRUNC('month', u.created_at) as cohort_month,
    DATE_TRUNC('month', o.created_at) as activity_month,
    COUNT(DISTINCT u.id) as users
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY cohort_month, activity_month
ORDER BY cohort_month, activity_month;

Funnel Analysis

-- Conversion funnel
WITH funnel AS (
    SELECT
        COUNT(DISTINCT CASE WHEN event = 'page_view' THEN user_id END) as views,
        COUNT(DISTINCT CASE WHEN event = 'signup' THEN user_id END) as signups,
        COUNT(DISTINCT CASE WHEN event = 'purchase' THEN user_id END) as purchases
    FROM events
    WHERE date >= CURRENT_DATE - INTERVAL '30 days'
)
SELECT 
    views,
    signups,
    ROUND(signups * 100.0 / NULLIF(views, 0), 2) as signup_rate,
    purchases,
    ROUND(purchases * 100.0 / NULLIF(signups, 0), 2) as purchase_rate
FROM funnel;

---

Data Cleaning

Common Data Quality Issues

Issue	Detection	Solution
Missing values	IS NULL or empty string	Impute, drop, or flag
Duplicates	GROUP BY with HAVING COUNT(*) > 1	Deduplicate with rules
Outliers	Z-score > 3 or IQR method	Investigate, cap, or exclude
Inconsistent formats	Sample and pattern match	Standardize with transforms
Invalid values	Range checks, referential integrity	Validate and correct

Data Cleaning SQL Patterns

-- Find duplicates
SELECT email, COUNT(*)
FROM users
GROUP BY email
HAVING COUNT(*) > 1;

-- Find nulls
SELECT 
    COUNT(*) as total,
    SUM(CASE WHEN email IS NULL THEN 1 ELSE 0 END) as null_emails,
    SUM(CASE WHEN name IS NULL THEN 1 ELSE 0 END) as null_names
FROM users;

-- Standardize text
UPDATE products
SET category = LOWER(TRIM(category));

-- Remove outliers (IQR method)
WITH stats AS (
    SELECT 
        PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY value) as q1,
        PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY value) as q3
    FROM data
)
SELECT * FROM data, stats
WHERE value BETWEEN q1 - 1.5*(q3-q1) AND q3 + 1.5*(q3-q1);

Data Cleaning Checklist

# Data Quality Audit: [Dataset]

## Row-Level Checks
- [ ] Total row count: [X]
- [ ] Duplicate rows: [X]
- [ ] Rows with any null: [X]

## Column-Level Checks
| Column | Type | Nulls | Unique | Min | Max | Issues |
|--------|------|-------|--------|-----|-----|--------|
| [col] | [type] | [n] | [n] | [v] | [v] | [notes] |

## Data Lineage
- Source: [Where data came from]
- Last updated: [Date]
- Known issues: [List]

## Cleaning Actions Taken
1. [Action and reason]
2. [Action and reason]

---

Spreadsheet Analysis

CSV/Excel Processing with Python

import pandas as pd

# Load data
df = pd.read_csv('data.csv')  # or pd.read_excel('data.xlsx')

# Basic exploration
print(df.shape)  # (rows, columns)
print(df.info())  # Column types and nulls
print(df.describe())  # Numeric statistics

# Data cleaning
df = df.drop_duplicates()
df['date'] = pd.to_datetime(df['date'])
df['amount'] = df['amount'].fillna(0)

# Analysis
summary = df.groupby('category').agg({
    'amount': ['sum', 'mean', 'count'],
    'quantity': 'sum'
}).round(2)

# Export
summary.to_csv('analysis_output.csv')

Common Pandas Operations

# Filtering
filtered = df[df['status'] == 'active']
filtered = df[df['amount'] > 1000]
filtered = df[df['date'].between('2024-01-01', '2024-12-31')]

# Aggregation
by_category = df.groupby('category')['amount'].sum()
pivot = df.pivot_table(values='amount', index='month', columns='category', aggfunc='sum')

# Window functions
df['running_total'] = df['amount'].cumsum()
df['pct_change'] = df['amount'].pct_change()
df['rolling_avg'] = df['amount'].rolling(window=7).mean()

# Merging
merged = pd.merge(df1, df2, on='id', how='left')

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Data Visualization

Chart Selection Guide

Data Type	Best Chart	Use When
Trend over time	Line chart	Showing patterns/changes over time
Category comparison	Bar chart	Comparing discrete categories
Part of whole	Pie/Donut	Showing proportions (≤5 categories)
Distribution	Histogram	Understanding data spread
Correlation	Scatter plot	Relationship between two variables
Many categories	Horizontal bar	Ranking or comparing many items
Geographic	Map	Location-based data

Python Visualization with Matplotlib/Seaborn

import matplotlib.pyplot as plt
import seaborn as sns

# Set style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")

# Line chart (trends)
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['value'], marker='o')
plt.title('Trend Over Time')
plt.xlabel('Date')
plt.ylabel('Value')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('trend.png', dpi=150)

# Bar chart (comparisons)
plt.figure(figsize=(10, 6))
sns.barplot(data=df, x='category', y='amount')
plt.title('Amount by Category')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('comparison.png', dpi=150)

# Heatmap (correlations)
plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Matrix')
plt.tight_layout()
plt.savefig('correlation.png', dpi=150)

ASCII Charts (Quick Terminal Visualization)

When you can't generate images, use ASCII:

Revenue by Month (in $K)
========================
Jan: ████████████████ 160
Feb: ██████████████████ 180
Mar: ████████████████████████ 240
Apr: ██████████████████████ 220
May: ██████████████████████████ 260
Jun: ████████████████████████████ 280

---

Report Generation

Standard Report Template

# [Report Name]
**Period:** [Date range]
**Generated:** [Date]
**Author:** [Agent/Human]

## Executive Summary
[2-3 sentences with key findings]

## Key Metrics

| Metric | Current | Previous | Change |
|--------|---------|----------|--------|
| [Metric] | [Value] | [Value] | [+/-X%] |

## Detailed Analysis

### [Section 1]
[Analysis with supporting data]

### [Section 2]
[Analysis with supporting data]

## Visualizations
[Insert charts]

## Insights
1. **[Insight]**: [Supporting evidence]
2. **[Insight]**: [Supporting evidence]

## Recommendations
1. [Actionable recommendation]
2. [Actionable recommendation]

## Methodology
- Data source: [Source]
- Date range: [Range]
- Filters applied: [Filters]
- Known limitations: [Limitations]

## Appendix
[Supporting data tables]

Automated Report Script

#!/bin/bash
# generate-report.sh

# Pull latest data
python scripts/extract_data.py --output data/latest.csv

# Run analysis
python scripts/analyze.py --input data/latest.csv --output reports/

# Generate report
python scripts/format_report.py --template weekly --output reports/weekly-$(date +%Y-%m-%d).md

echo "Report generated: reports/weekly-$(date +%Y-%m-%d).md"

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Statistical Analysis

Descriptive Statistics

Statistic	What It Tells You	Use Case
Mean	Average value	Central tendency
Median	Middle value	Robust to outliers
Mode	Most common	Categorical data
Std Dev	Spread around mean	Variability
Min/Max	Range	Data boundaries
Percentiles	Distribution shape	Benchmarking

Quick Stats with Python

# Full descriptive statistics
stats = df['amount'].describe()
print(stats)

# Additional stats
print(f"Median: {df['amount'].median()}")
print(f"Mode: {df['amount'].mode()[0]}")
print(f"Skewness: {df['amount'].skew()}")
print(f"Kurtosis: {df['amount'].kurtosis()}")

# Correlation
correlation = df['sales'].corr(df['marketing_spend'])
print(f"Correlation: {correlation:.3f}")

Statistical Tests Quick Reference

Test	Use Case	Python
T-test	Compare two means	scipy.stats.ttest_ind(a, b)
Chi-square	Categorical independence	scipy.stats.chi2_contingency(table)
ANOVA	Compare 3+ means	scipy.stats.f_oneway(a, b, c)
Pearson	Linear correlation	scipy.stats.pearsonr(x, y)

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Analysis Workflow

Standard Analysis Process

1. Define the Question

   • What are we trying to answer?
   • What decisions will this inform?

2. Understand the Data

   • What data is available?
   • What's the structure and quality?

3. Clean and Prepare

   • Handle missing values
   • Fix data types
   • Remove duplicates

4. Explore

   • Descriptive statistics
   • Initial visualizations
   • Identify patterns

5. Analyze

   • Deep dive into findings
   • Statistical tests if needed
   • Validate hypotheses

6. Communicate

   • Clear visualizations
   • Actionable insights
   • Recommendations

Analysis Request Template

# Analysis Request

## Question
[What are we trying to answer?]

## Context
[Why does this matter? What decision will it inform?]

## Data Available
- [Dataset 1]: [Description]
- [Dataset 2]: [Description]

## Expected Output
- [Deliverable 1]
- [Deliverable 2]

## Timeline
[When is this needed?]

## Notes
[Any constraints or considerations]

---

Scripts

data-init.sh

Initialize your data analysis workspace.

query.sh

Quick SQL query execution.

# Run query from file
./scripts/query.sh --file queries/daily-report.sql

# Run inline query
./scripts/query.sh "SELECT COUNT(*) FROM users"

# Save output to file
./scripts/query.sh --file queries/export.sql --output data/export.csv

analyze.py

Python analysis toolkit.

# Basic analysis
python scripts/analyze.py --input data/sales.csv

# With specific analysis type
python scripts/analyze.py --input data/sales.csv --type cohort

# Generate report
python scripts/analyze.py --input data/sales.csv --report weekly

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Integration Tips

With Other Skills

Skill	Integration
Marketing	Analyze campaign performance, content metrics
Sales	Pipeline analytics, conversion analysis
Business Dev	Market research data, competitor analysis

Common Data Sources

• Databases: PostgreSQL, MySQL, SQLite
• Warehouses: BigQuery, Snowflake, Redshift
• Spreadsheets: Google Sheets, Excel, CSV
• APIs: REST endpoints, GraphQL
• Files: JSON, Parquet, XML

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Best Practices

1. Start with the question — Know what you're trying to answer
2. Validate your data — Garbage in = garbage out
3. Document everything — Queries, assumptions, decisions
4. Visualize appropriately — Right chart for right data
5. Show your work — Methodology matters
6. Lead with insights — Not just data dumps
7. Make it actionable — "So what?" → "Now what?"
8. Version your queries — Track changes over time

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Common Mistakes

❌ Confirmation bias — Looking for data to support a conclusion ❌ Correlation ≠ causation — Be careful with claims ❌ Cherry-picking — Using only favorable data ❌ Ignoring outliers — Investigate before removing ❌ Over-complicating — Simple analysis often wins ❌ No context — Numbers without comparison are meaningless

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License

License: MIT — use freely, modify, distribute.

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"The goal is to turn data into information, and information into insight." — Carly Fiorina