Data Analytics Skills for Claude

Data Analytics Skills for Claude

Skill by ara.so — Data Skills collection.

A structured library of 31 reusable AI-powered skills that turn Claude into a hands-on analytics partner. Each skill is a self-contained instruction set that works on-demand without setup, asking targeted questions to gather exactly what it needs before executing a complete analytical workflow.

What This Project Does

This is a skill collection, not a library you import. When you describe an analytics task, Claude automatically selects and activates the appropriate skill from six categories:

1. Data Quality & Validation (5 skills) - EDA, quality audits, query validation, schema mapping
2. Documentation & Knowledge (5 skills) - Semantic models, documentation, SQL translation
3. Data Analysis & Investigation (7 skills) - Cohorts, segmentation, funnels, time series, A/​B tests
4. Data Storytelling & Visualization (5 skills) - Insights, charts, dashboards, narratives
5. Stakeholder Communication (5 skills) - Translation, requirements, impact quantification
6. Workflow Optimization (4 skills) - Planning, context packaging, peer review

Installation

For Individual Use

1. Clone the repository:

git clone https://github.com/​nimrodfisher/​data-analytics-skills.git
cd data-analytics-skills

1. Reference skills in your Claude conversations:

"Use the programmatic-eda skill to analyze this dataset"

For AI Coding Agents

Add to your agent's system prompt or project context:

# In your agent configuration
SKILL_PATH = "./​data-analytics-skills"

# Reference specific skills
with open(f"{SKILL_PATH}/​01-data-quality-validation/​programmatic-eda/​SKILL.md") as f:
    skill_context = f.read()

For Teams

Create a central repository with customized skills:

# Fork and customize
git clone https://github.com/​your-org/​data-analytics-skills.git
cd data-analytics-skills

# Add company-specific context
mkdir -p 01-data-quality-validation/​programmatic-eda/​references
echo "# Company Schema" > references/​company-schema.md

Key Concepts

Skill Structure

Every skill follows this pattern:

skill-name/
├── SKILL.md              # Complete skill instructions
├── README.md             # Human-readable documentation
└── references/           # Optional: company-specific context
    ├── company-schema.md
    ├── metric-definitions.md
    └── business-rules.md

How Skills Work

1. Minimal Context Request - Skill asks only essential information
2. Structured Execution - Step-by-step analytical workflow
3. Assumption Surfacing - Flags uncertainties explicitly
4. Templated Output - Consistent, shareable results

Common Usage Patterns

Pattern 1: Exploratory Data Analysis

# You provide data
import pandas as pd

df = pd.read_csv('user_data.csv')
print(df.head())
print(df.info())

# Then say:
# "Run programmatic-eda on this dataset"

# Claude will:
# 1. Ask about business context
# 2. Check data quality systematically
# 3. Identify patterns, outliers, distributions
# 4. Flag potential issues
# 5. Suggest next analysis steps

Pattern 2: Root Cause Investigation

# You have a metric drop
"""
Our daily active users dropped from 10,000 to 8,800 last week.
Activate root-cause-investigation.
"""

# Claude asks:
# - Metric definition
# - Normal baseline/​range
# - Recent changes
# - Segmentation dimensions

# Then provides:
# 1. Hypothesis tree
# 2. Data checks to run
# 3. Segmentation analysis
# 4. Timeline correlation
# 5. Likely root causes ranked

Pattern 3: A/​B Test Analysis

import pandas as pd
import numpy as np

# Load experiment data
test_data = pd.DataFrame({
    'user_id': range(1000),
    'variant': np.random.choice(['control', 'treatment'], 1000),
    'converted': np.random.binomial(1, 0.15, 1000)
})

# Say: "Analyze this A/​B test with ab-test-analysis skill"

# Claude will:
# 1. Validate sample sizes
# 2. Check for bias (SRM test)
# 3. Calculate statistical significance
# 4. Compute confidence intervals
# 5. Assess practical significance
# 6. Flag any issues

Pattern 4: SQL Documentation

-- Complex query you need to explain
SELECT 
    DATE_TRUNC('week', u.created_at) AS cohort_week,
    COUNT(DISTINCT u.user_id) AS cohort_size,
    COUNT(DISTINCT CASE WHEN o.created_at <= u.created_at + INTERVAL '7 days' 
          THEN o.user_id END) AS week_1_retained
FROM users u
LEFT JOIN orders o ON u.user_id = o.user_id
WHERE u.created_at >= '2024-01-01'
GROUP BY 1
ORDER BY 1;

Say: "Use sql-to-business-logic to document this query"

Claude produces:

• Plain English explanation
• Business logic breakdown
• Key assumptions
• Edge cases handled
• When to use/​not use

Pattern 5: Dashboard Specification

"I need a dashboard for marketing performance. 
Use dashboard-specification skill."

Claude asks about:

• Primary audience
• Key questions to answer
• Available data sources
• Refresh frequency needs

Then delivers:

• Metric definitions
• Visual layout mockup
• Filter specifications
• Data requirements
• Success criteria

Pattern 6: Chaining Multiple Skills

# Full analysis workflow

# 1. Planning phase
"""
Use analysis-planning for investigating signup drop.
Context: B2C SaaS, 15% drop in weekly signups over 2 weeks.
"""

# 2. Data quality check
"""
Run data-quality-audit on signup table.
[Provide schema and sample data]
"""

# 3. Investigation
"""
Execute root-cause-investigation with validated data.
"""

# 4. Documentation
"""
Use analysis-documentation to document findings.
"""

# 5. Stakeholder communication
"""
Generate executive-summary for VP of Growth.
"""

Customization

Adding Company Context

Create a references/ folder in any skill:

cd 02-documentation-knowledge/​semantic-model-builder
mkdir references

# Add your definitions
cat > references/​metric-definitions.md << 'EOF'
# Standard Metrics

## Activation Rate
- **Formula**: (Users completing 3+ key actions in 7 days) / Total signups
- **Threshold**: >40% is healthy
- **Owner**: Growth team

## Monthly Recurring Revenue (MRR)
- **Formula**: Sum of all active subscription values
- **Excludes**: One-time payments, paused subscriptions
- **Currency**: USD normalized
EOF

Claude automatically uses these references when the skill runs.

Creating Team Conventions

# Add analysis standards
cat > 06-workflow-optimization/​analysis-planning/​references/​team-conventions.md << 'EOF'
# Analysis Standards

## Before Starting
- [ ] Create JIRA ticket
- [ ] Check if similar analysis exists
- [ ] Schedule 15min scoping with PM

## During Analysis
- [ ] Log all assumptions in analysis-assumptions-log
- [ ] Validate data with data-quality-audit
- [ ] Document queries in SQL comments

## Before Sharing
- [ ] Run analysis-qa-checklist
- [ ] Peer review with another analyst
- [ ] Add to company wiki
EOF

Skill Quick Reference

When to Use Each Category

Most Frequently Used Skills

# Top 10 by typical usage
CORE_SKILLS = [
    "programmatic-eda",              # Start of every analysis
    "root-cause-investigation",      # Metric drops/​spikes
    "analysis-planning",             # Before big projects
    "query-validation",              # SQL review
    "semantic-model-builder",        # One-time setup, huge ROI
    "ab-test-analysis",              # Experiments
    "cohort-analysis",               # Retention tracking
    "executive-summary-generator",   # Reporting up
    "visualization-builder",         # Chart decisions
    "analysis-documentation"         # Knowledge capture
]

Troubleshooting

Issue: Skill asks too many questions

Solution: Provide more upfront context

# Instead of:
"Analyze this data"

# Do:
"""
Run programmatic-eda on user signup data.
Context:
- Table: signups
- Rows: 50,000
- Timeframe: Last 90 days
- Business: B2C SaaS
- Main concerns: Conversion rate, signup source quality
"""

Issue: Output doesn't match company standards

Solution: Add company references

cd skill-name/​references
# Create markdown files with your standards
# Claude will automatically use them

Issue: Skill produces generic insights

Solution: Provide business context explicitly

# Good context example:
"""
Use root-cause-investigation.

Metric: Daily Active Users (DAU)
- Normal range: 8,000-10,000
- Current: 6,500 (35% below normal)
- Started: March 15 (5 days ago)
- Recent changes:
  - New onboarding flow deployed March 14
  - Email provider switched March 10
  - Major competitor launched promo March 12
- Segments to check: Platform (iOS/​Android/​Web), User cohort, Geography
"""

Issue: Want to use skills programmatically

Solution: Reference skills in system prompts

import os

def load_skill(skill_path):
    """Load skill content for programmatic use."""
    with open(f"{skill_path}/​SKILL.md", 'r') as f:
        return f.read()

# Example: Build custom analysis pipeline
skills = [
    load_skill("01-data-quality-validation/​data-quality-audit"),
    load_skill("03-data-analysis-investigation/​cohort-analysis"),
    load_skill("04-data-storytelling-visualization/​insight-synthesis")
]

system_prompt = f"""
You are a data analyst. Use these skills in sequence:

{chr(10).join(skills)}

Follow each skill's workflow completely before moving to the next.
"""

Issue: Skills not activating automatically

Solution: Skills activate based on natural language. Be explicit:

# Explicit activation:
"Use the cohort-analysis skill to..."
"Activate root-cause-investigation for..."
"Run programmatic-eda on..."

# Or describe the task clearly:
"I need to understand retention by signup cohort"  # → cohort-analysis
"Why did our conversion rate drop?"                # → root-cause-investigation
"Check if this dataset is clean"                   # → data-quality-audit

Real-World Examples

Example 1: Complete Investigation Workflow

"""
PROJECT: Investigate 20% drop in mobile app engagement

STEP 1: Plan the investigation
→ Use analysis-planning skill
Output: Structured approach with hypotheses

STEP 2: Validate data quality
→ Use data-quality-audit on app_events table
Output: Quality report, issues flagged

STEP 3: Run investigation
→ Use root-cause-investigation
Output: Ranked hypotheses with evidence

STEP 4: Deep dive on top cause
→ Use segmentation-analysis on iOS vs Android
Output: iOS users affected, Android stable

STEP 5: Document findings
→ Use analysis-documentation
Output: Reproducible methodology doc

STEP 6: Present to stakeholders
→ Use executive-summary-generator
Output: 1-page summary for leadership
"""

Example 2: Building a Metrics Framework

"""
PROJECT: Create company-wide metric definitions

STEP 1: Build semantic model
→ Use semantic-model-builder skill

Provide:
- List of key metrics (ARR, MRR, CAC, LTV, Churn, etc.)
- Business definitions
- Owners

Output: Standardized metric layer

STEP 2: Add to each relevant skill
→ Save output to references/​metric-definitions.md in:
  - business-metrics-calculator/​references/
  - ab-test-analysis/​references/
  - executive-summary-generator/​references/

STEP 3: Create data catalog
→ Use data-catalog-entry for each data source

Result: Everyone uses same definitions automatically
"""

Configuration

Environment Variables

No environment variables required. Skills work with data you provide in conversation.

Optional: Team Configuration

Create a .data-skills-config.json in your project root:

{
  "default_references_path": "./​company-context",
  "metric_definitions": "./​company-context/​metrics.md",
  "schema_docs": "./​company-context/​schema.md",
  "business_rules": "./​company-context/​rules.md",
  "team_conventions": "./​company-context/​conventions.md"
}

Reference in skills with:

See ${project_root}/​.data-skills-config.json for company standards

Advanced Patterns

Pattern: Automated Quality Gates

# Pre-commit hook for analysis files
"""
Before committing analysis:
1. Run analysis-qa-checklist
2. Verify all assumptions logged with analysis-assumptions-log
3. Check documentation exists via analysis-documentation
4. Validate SQL with query-validation
"""

Pattern: Analysis Templates

# Create project template
ANALYSIS_TEMPLATE = """
1. [analysis-planning] → approach.md
2. [data-quality-audit] → quality-report.md
3. [relevant-analysis-skill] → findings.md
4. [analysis-assumptions-log] → assumptions.md
5. [analysis-documentation] → documentation.md
6. [executive-summary-generator] → summary.md
7. [analysis-retrospective] → learnings.md
"""

Pattern: Skill Chaining Syntax

# Define analysis pipeline
"""
PIPELINE: User retention deep-dive

data-quality-audit → cohort-analysis → segmentation-analysis → 
insight-synthesis → visualization-builder → executive-summary-generator

Pass context forward at each step.
Flag any blockers immediately.
"""

Best Practices

1. Always start with analysis-planning for complex work
2. Run data-quality-audit before analyzing unfamiliar data
3. Use semantic-model-builder once, reference forever
4. Document with analysis-assumptions-log as you go
5. Chain skills for complete workflows, don't jump to conclusions
6. Add company context to references/ folders for frequently-used skills
7. Run analysis-qa-checklist before sharing any findings

Resources

• Repository: https://github.com/nimrodfisher/data-analytics-skills
• Skill Map: Interactive visualization
• Individual Skills: Browse the repository structure by category