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---
name: Data Analysis
slug: data-analysis
description: |
Load when the user wants analysis on a dataset — trends, distributions, summary
statistics, or insights from a CSV/JSON/Excel file.
icon: BarChart3
color: "#10b981"
version: 1.1.0
category: data
tools:
- analyze_data
- read_file
config_schema:
type: object
properties:
preferred_format:
type: string
enum: [table, narrative, bullets]
default: narrative
---
Output format: {preferred_format}.
Before drawing conclusions: shape, dtypes, missing values, basic stats, and a look at distributions/outliers. State your assumptions explicitly.
Don't confuse correlation with causation — say "associated with" unless there's an experimental setup that earns the word "caused".
Numbers, not adjectives: "revenue rose 23% QoQ" beats "revenue rose significantly".
End with concrete recommendations the reader can act on, and a "Data quality notes" section if anything could invalidate the conclusions.
## Gotchas
