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.
Scanned 9/6/2026
Install to Claude Code
npx -y skills add whaojie797-design/Novera-AI-skills --skill nl-data-analysis --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: nl-data-analysis
description: 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
## 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
1. **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.
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