# CSV to Questions

Summarizes a CSV dataset to stay within token limits, then generates focused analytical questions.

> For the complete documentation index, see [llms.txt](/llms.txt). Markdown variants are available by appending `.md` to any URL or sending an `Accept: text/markdown` header. An agent skill is available at [/.well-known/agent-skills/site-skill.md](/.well-known/agent-skills/site-skill.md).



<DocsBaseSwitcher base="flue" agent="csv-to-questions" />

<AgentPreview
  agent="csv-to-questions"
  framework="flue"
  inputFields="[
  {
    name: &#x22;url&#x22;,
    label: &#x22;CSV URL&#x22;,
    placeholder: &#x22;https://example.com/world-gdp.csv&#x22;,
    type: &#x22;text&#x22;,
  },
]"
/>

## Summary [#summary]

The **CSV to Questions Agent** takes a CSV dataset and turns it into a set of
sharp, answerable analytical questions. It first summarizes the data — columns,
types, ranges, patterns — to compress large files and avoid token-limit errors,
then generates questions a data analyst would actually ask. Reach for it to
kickstart exploratory analysis or build study material from raw data.

## Install [#install]

```bash
npx shadcn@latest add @agentcn/flue/csv-to-questions
```

## Composition [#composition]

```text
agents/
└── csv-to-questions.ts   # Single-agent variant with the fetch_csv tool
workflows/
└── csv-to-questions.ts   # summarizer → questioner pipeline (typed output)
tools/
└── fetch-csv.ts          # Loads a CSV file from a URL
```

## Customization [#customization]

* **Read local files.** Swap `fetch-csv.ts` to read from disk or object storage.
* **Reshape the output.** Edit the `Questions` valibot schema to add fields such
  as difficulty or the column each question targets.
* **Swap the models.** Use a large-context model on the summarizer for wide
  datasets; a smaller one on the questioner for speed.
* **Chunk huge files.** Summarize in row batches and merge before questioning.