For the complete documentation index, see 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.
400
Sponsor

CSV to Questions

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

Output will stream here when you run the agent.

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.

Installation

$ pnpm dlx shadcn@latest add @agentcn/flue/csv-to-questions

Composition

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

  • 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.