Extract structured JSON fields from messy text

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Paste into a general LLM and extract structured fields from messy text using a JSON-only output schema

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Extract fields from the text. Return ONLY valid JSON matching this schema (no markdown fences):

{
  "title": string,
  "date": string|null,
  "people": string[],
  "action_items": [{"task": string, "owner": string|null, "due": string|null}],
  "risks": string[],
  "open_questions": string[]
}

If unknown, use null or []. Do not invent.

TEXT:
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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": "json-extract-from-text",
  "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"
}