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data-analysis

Profile and aggregate a bundled sales dataset - group revenue by region, category, channel or month, apply filters, and compute sums, means, medians and ranges.

Skill by DougTrajano

IndexedMIT★ 381
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Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/dougtrajano-data-analysis

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

Profile and aggregate a bundled sales dataset - group revenue by region, category, channel or month, apply filters, and compute sums, means, medians and ranges. Use for questions about sales figures, trends, or which segments perform best.

Use cases: data, sales, data-analysis

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found
Runs as
Python package

Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/data-analysis

What's inside

Skill name
data-analysis
Description
Profile and aggregate a bundled sales dataset - group revenue by region, category, channel or month, apply filters, and compute sums, means, medians and ranges. Use for questions about sales figures, trends, or which segments perform best.
License
MIT
Compatibility
Standard library only, no network - runs unchanged on the host or inside a sandbox executor
Instructions
76 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (4)

  • SKILL.md
  • sales.csv · resources/
  • aggregate.py · scripts/
  • profile_dataset.py · scripts/

SKILL.md (first lines)

# Data Analysis Skill

Answers questions about a bundled sales dataset (`resources/sales.csv`): 96 rows
covering six months, four regions, four product categories and three sales
channels.

This is the reference skill for exercising sandbox executors. Both scripts are
standard library only and read their data from the skill folder, so they do real
work with no network, no third-party packages and no host access — exactly the
shape a sandbox is meant to run.

## When to Use This Skill

- "Which region had the highest revenue?" → `aggregate`
- "What were average units sold per category?" → `aggregate`
- "How many rows are in the dataset and what does revenue look like?" → `profile_dataset`
- "Compare online versus retail sales in the north" → `aggregate` with filters

## Skill Scripts

### profile_dataset

Reports dataset shape, column names, summary statistics for every numeric column
(count, sum, mean, median, stdev, min, max) and distinct-value counts for the
categorical ones.

- `column` (optional): Profile a single numeric column instead of all of them

Exits 2 for an unknown column.

### aggregate

Groups rows and aggregates a numeric column, optionally filtered and truncated.

- `group-by` (required): Column to group by — `month`, `region`, `category`, `channel`
- `metric` (optional): Numeric column to aggregate, default `revenue`
- `agg` (optional): `sum` (default), `mean`, `median`, `min`, `max`, `count`
- `where` (optional, repeatable): Filter as `column=value`, e.g. `region=north`
- `top` (optional): Keep only the highest N groups

Exits 2 for an unknown column or malformed filter, 1 when no rows match.

### Usage Examples

**Revenue by region, highest first:**

- group-by: region

**Top 3 categories by units sold, online only:**

- group-by: category
- metric: units
- agg: sum
- where: channel=online
- top: 3

**Average revenue per month in the west:**

- group-by: month
- agg: mean
- where: region=west

## Data Source

`resources/sales.csv` is generated sample data, included so the skill is
self-contained and deterministic. It is not real sales data.

## Sandbox Behaviour

Both scripts produce byte-identical output on the host and under
`LocalSandboxScriptExecutor` or `OpenSandboxScriptExecutor`, including the
`../resources/sales.csv` read through the skill root and non-zero exit codes for
bad arguments. That equivalence is the point: swapping the executor changes where
the script runs, not what the model sees.

From DougTrajano/pydantic-ai-skills, MIT.

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    Stars
    381
    Forks
    29
    Open issues
    0
    Last commit (this folder)
    Aug 16, 2026 (2 months ago)
    Last release
    Oct 4, 2026 (24 days ago)

    From GitHub (fetched Oct 24, 2026).

    Details

    License
    MIT
    First seen
    Oct 24, 2026
    Last checked
    Oct 24, 2026

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    Cite this page

    data-analysis skill. Agent Mart. https://agentmart.dev/skills/dougtrajano-data-analysis (last updated 2026-10-24)