CrushData AI

Data Analyst Intelligence for AI IDEs

An AI skill that provides structured, professional data analysis workflows with built-in validation - helping AI coding assistants perform data analysis like a careful human analyst.

🎯 What It Does

CrushData AI provides:

• 10 Analysis Workflows - EDA, Dashboard, A/B Test, Cohort, Funnel, Time Series, Segmentation, Data Cleaning, Ad-hoc, KPI Reporting
• 400+ Searchable Patterns - Metrics, SQL, Python, Charts, Database Tips, Common Mistakes
• Context-Building Protocol - Forces AI to ask questions and validate before delivering results
• 4 Industry Modules - SaaS, E-commerce, Finance, Marketing specific metrics

🚀 Quick Start

Install via CLI

npm install -g crushdataai

What npm install -g crushdataai Does

The -g flag means Global Install:

	Local Install (npm install)	Global Install (npm install -g)
Location	./node_modules/ in current folder	System-wide (e.g., %APPDATA%\npm\)
Scope	Only available in that project	Available everywhere on your computer
Use Case	Libraries for your project	CLI tools you want to run anywhere

Then in any project:

cd your-project
crushdataai init --ai all    # All AI IDEs
crushdataai init --ai claude # Claude Code only

What crushdataai init Does

When you run crushdataai init --ai all, the CLI:

1. Creates .shared/data-analyst/ - Contains the BM25 search engine and 13 CSV knowledge databases (~400 rows of data analyst patterns)

2. Creates AI IDE config files based on --ai flag:

   Flag	Creates
   --ai claude	.claude/skills/data-analyst/SKILL.md
   --ai cursor	.cursor/commands/data-analyst.md
   --ai windsurf	.windsurf/workflows/data-analyst.md
   --ai antigravity	.agent/workflows/data-analyst.md
   --ai copilot	.github/prompts/data-analyst.prompt.md
   --ai kiro	.kiro/steering/data-analyst.md
   --ai all	All of the above

3. Your AI IDE automatically detects the config files and enables the /data-analyst command

Updating

To update the CLI and refresh your project's AI skill files:

npm install -g crushdataai@latest
# Update specific IDE (recommended):
crushdataai init --ai cursor --force

# Or update everything:
crushdataai init --force

🔌 Data Connections (New in v1.2)

CrushData AI now features a Connection Manager to securely handle your data credentials.

1. Add Data Sources

Run the connect command to open the management UI:

crushdataai connect

• Supported Types: CSV, MySQL, PostgreSQL, Shopify, BigQuery, Snowflake
• Private & Secure: Credentials are stored locally on your machine (~/.crushdataai/connections.json). They are never uploaded to any server or included in the npm package.

Note

Persistence: Once you add a connection, you can close the UI (Ctrl+C). The AI IDE reads the saved connection details directly from your local config file, so the server does NOT need to keep running.

2. View Saved Connections

crushdataai connections

💻 Usage

Step 1: Initialize

crushdataai init --ai all

Step 2: Use in AI IDE

The skill activates automatically (Claude) or via slash command (others).

Example Workflow:

1. User Request: "Analyze the sales trends in my-shop-data"
2. AI Action: The AI checks your saved connections.
3. AI Action: The AI runs:

   npx crushdataai snippet my-shop-data --lang python

4. Result: The AI receives the secure code to connect to your data (read-only) and proceeds with analysis.

Claude Code

Claude Code

The skill activates automatically when you request data analysis work. Just chat naturally:

Analyze customer churn for my SaaS product

Cursor / Windsurf / Antigravity

Use the slash command to invoke the skill:

/data-analyst Analyze customer churn for my SaaS product

Kiro

Type / in chat to see available commands, then select data-analyst:

/data-analyst Analyze customer churn for my SaaS product

GitHub Copilot

In VS Code with Copilot, type / in chat to see available prompts, then select data-analyst:

/data-analyst Analyze customer churn for my SaaS product

Example Prompts

Analyze customer churn for my SaaS product
Create a dashboard for e-commerce analytics
Calculate MRR and ARR from subscription data
Build a cohort retention analysis
Perform A/B test analysis on conversion rates

Search Directly

# Search workflows
python3 .shared/data-analyst/scripts/search.py "EDA" --domain workflow

# Search metrics
python3 .shared/data-analyst/scripts/search.py "churn" --domain metric

# Search SQL patterns
python3 .shared/data-analyst/scripts/search.py "cohort" --domain sql

# Industry-specific
python3 .shared/data-analyst/scripts/search.py "MRR" --industry saas

📊 Search Domains

Domain	Content
workflow	Step-by-step analysis processes
metric	Metric definitions with formulas
chart	Visualization recommendations
cleaning	Data quality patterns
sql	SQL patterns (window functions, cohorts)
python	pandas/polars code snippets
database	PostgreSQL, BigQuery, Snowflake tips
report	Dashboard UX guidelines
validation	Common mistakes to avoid

🏭 Industry Modules

Industry	Key Metrics
saas	MRR, ARR, Churn, CAC, LTV, NRR
ecommerce	Conversion, AOV, Cart Abandonment
finance	Margins, ROI, Cash Flow, Ratios
marketing	CTR, CPA, ROAS, Lead Conversion

🔒 How It Works

Context-Building Protocol

1. Discovery - AI asks about business context before coding
2. Data Profiling - Mandatory checks before analysis
3. Data Cleaning (ETL) - Handle missing values/duplicates in etl/ folder
4. Validation - Verify JOINs, aggregations, and totals
5. Sanity Checks - Compare to benchmarks before delivery

Python Environment

To prevent global conflicts, the AI is instructed to:

1. Check: Look for existing venv or .venv.
2. Create: If missing, run python3 -m venv venv.
3. Reports: Save all validation/profiling outputs to reports/ folder. Create if missing.

This prevents the common AI mistakes:

• ❌ Wrong metric definitions
• ❌ Duplicate row inflation
• ❌ Incorrect JOIN types
• ❌ Unreasonable totals
• ❌ Cluttered workspaces (scripts are organized in analysis/ and etl/)

📝 License

Apache 2.0