# Menza Data Analyst

> Point this skill at a CSV and ask natural-language questions — total, average, min, max, top-N, filtered count, and group-by — and get a Markdown answer with supporting tables. Trigger when a user wants to explore or summarise tabular data without writing code.

- Skill: `riteshkew/menza-data-analyst` (Agent Skill, multi-file: 13 files)
- Install (CLI): `npx skillmds@latest add riteshkew/menza-data-analyst`
- Raw SKILL.md: https://api.skillmd.com/api/skills/riteshkew/menza-data-analyst/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: riteshkew (https://skillmd.com/u/riteshkew)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/riteshkew/menza-data-analyst

---


# Workflow

When this skill triggers, follow these steps in order.

## Step 1 — Locate the CSV

Check whether the user has provided a CSV path.

- If a path is provided, confirm the file exists and has a header row.
- If no path is provided, ask: "Please provide the path to your CSV file. See `resources/sales.csv` for a working example with columns: order_id, region, product, units, revenue, date."
- The engine works with any CSV that has no quoted commas — simple comma-separated values only.

## Step 2 — Clarify the question

Check whether the user has asked a question.

- If a question is provided, confirm it maps to a supported pattern (see list below).
- If no question is provided, ask what they want to know about the data.

## Step 3 — Run the analysis engine

Execute from the skill root:

```bash
node scripts/ask.mjs <path-to-csv> "<question>"
```

The engine exits 0 on success (including "unsupported" responses) and exits 1 only on file/input errors. Capture stdout. If the process exits non-zero, surface the stderr message.

## Step 4 — Present the answer

The engine outputs a Markdown block containing:
- The question (echoed)
- The detected intent
- The computed answer
- A supporting table (for top-N and group-by questions)

Present this output directly. For numeric answers, note the units (e.g. USD, count).

## Step 5 — Offer follow-up analysis

After presenting the answer, offer one or two follow-up questions the user might ask based on the result. For example:

- After a group-by: "Would you like to see the top N within a specific group?"
- After a total: "Would you like to break that down by region or product?"

## Supported question patterns

The engine handles a deterministic subset of natural-language data questions. Use the engine for these; compute directly for anything else.

| Pattern | Example |
|---------|---------|
| Row count | "How many rows?" |
| Filtered count | "How many rows where region is West?" |
| Sum of a column | "What is the total revenue?" |
| Average of a column | "What is the average units?" |
| Max of a column | "What is the highest revenue?" |
| Min of a column | "What is the lowest units?" |
| Top N by a column | "Show me the top 5 by revenue" |
| Group-by aggregation | "sum of revenue by region" |

If the question does not match any pattern or references an unknown column, the engine returns a clear "I can't answer that" message (exit 0) — this is not an error.

## Example

See `examples/input.md` for the dataset and example questions, and `examples/output.md` for the engine's output.

Run the example yourself from the skill root:

```bash
cd skills/menza-data-analyst
bash examples/run.sh
```