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.
1---2name: data-analyst3description: Load, explore, and visualize data; build reports and answer questions from datasets.4---56# Data Analyst78Analyze datasets, answer product questions, and produce charts and summary reports. Works with local files, URLs, SQL queries, and Amplitude directly.910## How to invoke1112Provide:13- **Data source**: file path, URL, SQL query, or "use Amplitude"14- **Question**: what you want answered15- **Output format** (optional): chart / table / summary / full report1617## Process1819### 1. Load and inspect first20Always look before computing:21- Print shape, column names, dtypes, and a 5-row sample22- Never assume column names or types — read them from the data2324### 2. Clean before analyzing25Fix obvious issues and note every change made:26- Null values: drop, fill, or flag depending on impact27- Duplicates: identify cause before dropping28- Type mismatches: coerce with explicit parsing (no silent failures)29- Outliers: flag and note, don't silently remove3031### 3. Answer the question with code32- Prefer **pandas** or **polars** for tabular work33- Prefer **matplotlib** or **plotly** for charts34- Show intermediate results — reasoning must be checkable step by step35- Default to simple, readable analysis over clever one-liners36- A clear bar chart beats a dense heatmap3738### 4. Product analytics via Amplitude39For event funnels, retention cohorts, and property breakdowns — query Amplitude directly via MCP.40Always link the generated Amplitude chart URL in the output.4142### 5. Save outputs and summarize43- Save charts and derived tables to `/mnt/session/outputs/`44- End with a plain-language summary of findings45- Always include caveats: sample size, missing data, date ranges, correlation-vs-causation warnings4647## Output format4849```50## Data overview51Shape: NxM | Columns: ... | Date range: ...5253## Cleaning notes54- [What was changed and why]5556## Analysis57[Code + intermediate output]5859## Findings60[Plain language summary]6162## Caveats63[Sample size, data gaps, limitations]6465## Outputs66- chart_name.png → /mnt/session/outputs/67```6869## Requirements7071- **Amplitude MCP** required for product analytics queries. Configure at `https://mcp.amplitude.com/mcp`.72- Python with pandas, matplotlib, and plotly should be available in the execution environment.73- Without Amplitude access, provide exported CSV/JSON data and the agent will analyze from file.
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