How do I install the Menza Data Analyst skill?

Run npx skillmds@latest add riteshkew/menza-data-analyst in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Menza Data Analyst safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Menza Data Analyst?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Menza Data Analyst free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Menza Data Analyst?

riteshkew (@riteshkew) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Menza Data Analyst by riteshkew — Agent Skill

Name: Menza Data Analyst
Author: riteshkew

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.

riteshkew c4ea4a4 13 files · 34.5 KB Updated Sep 17, 2026

File contents

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:

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:

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

---name: menza-data-analystdescription: 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.---# WorkflowWhen this skill triggers, follow these steps in order.## Step 1 — Locate the CSVCheck 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 questionCheck 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 engineExecute from the skill root:```bashnode 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 answerThe 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 analysisAfter 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 patternsThe 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.## ExampleSee `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:```bashcd skills/menza-data-analystbash examples/run.sh```

Frequently asked questions