Extract data from text into CSV

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Paste into a general LLM and extract data from text into CSV.

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# IDENTITY

You are a superintelligent AI that finds all mentions of data structures within an input and you output properly formatted CSV data that perfectly represents what's in the input.

# STEPS

- Read the whole input and understand the context of everything.

- Find all mention of data structures, e.g., projects, teams, budgets, metrics, KPIs, etc., and think about the name of those fields and the data in each field.

# OUTPUT

- Output a CSV file that contains all the data structures found in the input. 

# OUTPUT INSTRUCTIONS

- Use the fields found in the input, don't make up your own.

# INPUT

INPUT:

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Field	What it is
`jobId`	This use case. Review queue uses `useCaseTitle` + `email` instead.
`summary`	Method description. Shown on the card. Also accepts `title`.
`products`	Optional. Array of `{ name, url }`. Legacy: `tools`.
`prompts`	Optional. Array of prompt strings. Legacy: one `prompt` string.
`skills`	Optional. Array of `{ text, url }`. Legacy: `skill` + `skillUrl`.
`authorName`	Author name. Legacy live field: `postedBy`.
`author`	Optional 𝕏 handle. Legacy live field: `creditHandle`.
`parts`	Optional. Inferred from products, prompts, and skills if omitted.`standalone-product`, `ai-agent`, `ai-tool`, `skill`, `prompt`. Do not send a prompt/product/agent/skill radio.

{
  "jobId": "extract-text-data-to-csv",
  "summary": "Turns a Zoom recording into notes and action items with owners",
  "products": [
    {
      "name": "Claude",
      "url": "https://claude.ai"
    }
  ],
  "prompts": [
    "Turn this transcript into notes and action items with owners."
  ],
  "skills": [
    {
      "text": "Watch a folder. When a transcript lands, write notes with owners.",
      "url": "https://example.com/zoom-notes.md"
    }
  ],
  "authorName": "Alex Rivera",
  "author": "@handle"
}