# Data Analyst

> Load, explore, and visualize data; build reports and answer questions from datasets.

- Skill: `fadil369/data-analyst` (Agent Skill)
- Install (CLI): `npx skillmds@latest add fadil369/data-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/fadil369/data-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: fadil369 (https://skillmd.com/u/fadil369)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/fadil369/data-analyst

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# Data Analyst

Analyze datasets, answer product questions, and produce charts and summary reports. Works with local files, URLs, SQL queries, and Amplitude directly.

## How to invoke

Provide:
- **Data source**: file path, URL, SQL query, or "use Amplitude"
- **Question**: what you want answered
- **Output format** (optional): chart / table / summary / full report

## Process

### 1. Load and inspect first
Always look before computing:
- Print shape, column names, dtypes, and a 5-row sample
- Never assume column names or types — read them from the data

### 2. Clean before analyzing
Fix obvious issues and note every change made:
- Null values: drop, fill, or flag depending on impact
- Duplicates: identify cause before dropping
- Type mismatches: coerce with explicit parsing (no silent failures)
- Outliers: flag and note, don't silently remove

### 3. Answer the question with code
- Prefer **pandas** or **polars** for tabular work
- Prefer **matplotlib** or **plotly** for charts
- Show intermediate results — reasoning must be checkable step by step
- Default to simple, readable analysis over clever one-liners
- A clear bar chart beats a dense heatmap

### 4. Product analytics via Amplitude
For event funnels, retention cohorts, and property breakdowns — query Amplitude directly via MCP.
Always link the generated Amplitude chart URL in the output.

### 5. Save outputs and summarize
- Save charts and derived tables to `/mnt/session/outputs/`
- End with a plain-language summary of findings
- Always include caveats: sample size, missing data, date ranges, correlation-vs-causation warnings

## Output format

```
## Data overview
Shape: NxM | Columns: ... | Date range: ...

## Cleaning notes
- [What was changed and why]

## Analysis
[Code + intermediate output]

## Findings
[Plain language summary]

## Caveats
[Sample size, data gaps, limitations]

## Outputs
- chart_name.png → /mnt/session/outputs/
```

## Requirements

- **Amplitude MCP** required for product analytics queries. Configure at `https://mcp.amplitude.com/mcp`.
- Python with pandas, matplotlib, and plotly should be available in the execution environment.
- Without Amplitude access, provide exported CSV/JSON data and the agent will analyze from file.