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Run npx skillmds@latest add whaojie797-design/nl-data-analysis 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.

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Is Nl Data Analysis free to use?

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Who published Nl Data Analysis?

whaojie797-design (@whaojie797-design) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Nl Data Analysis by whaojie797-design

Name: Nl Data Analysis
Author: whaojie797-design

Nl Data Analysis

This skill should be used when the user wants to analyze tabular data or produce charts from natural-language requests. Trigger phrases include "分析这个表", "画个图", "这数据说明什么", "analyze this data", "make a chart", "数据可视化", "跑个分析". It profiles data, translates questions into pandas/SQL, generates charts, and summarizes insights. Trigger on uploads of CSV/Excel or requests to explore/visualize a dataset.

whaojie797-design 8a51571 4 files · 6.0 KB Updated Sep 17, 2026

File contents

Natural-Language Data Analysis

Overview

Turn natural-language questions about tabular data into executable analysis: profile the dataset, translate the question into pandas (or SQL), produce charts, and summarize insights in plain language.

When to Use

"分析这个表", "画个图", "这数据说明什么", "analyze", "make a chart", "数据可视化", "跑个分析", "correlation", "trend".
The user uploads or points to a CSV, Excel, or database table.
The user wants a quick exploratory view of a dataset.

Workflow

Profile — Load the data; report shape, columns, types, missing counts, head. Use scripts/profile_data.py (stdlib, no deps).
Clarify — If ambiguous (which column? what metric? time range?), ask one focused question. Do not guess silently.
Translate — Map the question to pandas/SQL (groupby, agg, join, pivot). Prefer pandas for files, SQL for databases.
Visualize — Generate a chart with scripts/make_chart.py (requires matplotlib). Choose by intent: trend→line, distribution→hist/box, composition→bar/pie, relationship→scatter.
Summarize — State the finding in 1-3 sentences; include the number that matters.

Rules

Never fabricate statistics. Compute from the actual data.
One chart per question unless comparing.
Keep code minimal; load the data once, reuse the DataFrame.
Save outputs to the working directory; report the path.

Bundled Resources

scripts/profile_data.py — profile a CSV (shape, columns, missing, head) with no third-party dependencies.
scripts/make_chart.py — generate a chart from a CSV + column spec (install: pip install matplotlib).
references/analysis-patterns.md — common NL→pandas translations and chart-selection guidance.

---name: nl-data-analysisdescription: This skill should be used when the user wants to analyze tabular data or produce charts from natural-language requests. Trigger phrases include "分析这个表", "画个图", "这数据说明什么", "analyze this data", "make a chart", "数据可视化", "跑个分析". It profiles data, translates questions into pandas/SQL, generates charts, and summarizes insights. Trigger on uploads of CSV/Excel or requests to explore/visualize a dataset.---# Natural-Language Data Analysis## OverviewTurn natural-language questions about tabular data into executable analysis:profile the dataset, translate the question into pandas (or SQL), produce charts,and summarize insights in plain language.## When to Use- "分析这个表", "画个图", "这数据说明什么", "analyze", "make a chart", "数据可视化", "跑个分析", "correlation", "trend".- The user uploads or points to a CSV, Excel, or database table.- The user wants a quick exploratory view of a dataset.## Workflow1. **Profile** — Load the data; report shape, columns, types, missing counts, head. Use `scripts/profile_data.py` (stdlib, no deps).2. **Clarify** — If ambiguous (which column? what metric? time range?), ask one focused question. Do not guess silently.3. **Translate** — Map the question to pandas/SQL (groupby, agg, join, pivot). Prefer pandas for files, SQL for databases.4. **Visualize** — Generate a chart with `scripts/make_chart.py` (requires `matplotlib`). Choose by intent: trend→line, distribution→hist/box, composition→bar/pie, relationship→scatter.5. **Summarize** — State the finding in 1-3 sentences; include the number that matters.## Rules- Never fabricate statistics. Compute from the actual data.- One chart per question unless comparing.- Keep code minimal; load the data once, reuse the DataFrame.- Save outputs to the working directory; report the path.## Bundled Resources- `scripts/profile_data.py` — profile a CSV (shape, columns, missing, head) with no third-party dependencies.- `scripts/make_chart.py` — generate a chart from a CSV + column spec (install: `pip install matplotlib`).- `references/analysis-patterns.md` — common NL→pandas translations and chart-selection guidance.

Frequently asked questions