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
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
Use cases: data, sales, data-analysis
Reviews
Write a reviewNo 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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Featured in
Signals
- 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
- Source
- GitHubgithub-graphql
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Cite this page
data-analysis skill. Agent Mart. https://agentmart.dev/skills/dougtrajano-data-analysis (last updated 2026-10-24)