Detect tone and sentiment in a tweet
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1 method on the page: 1 prompt.
Methods
Paste into a general LLM and identify a tweet's tone and sentiment with evidence from words, phrases, and emoji
LLMPrompt
Prompt
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Your task is to analyze the provided tweet and identify the primary tone and sentiment expressed by the author. Tone: Positive | Negative | Neutral | Humorous | Sarcastic | Enthusiastic | Angry | Informative Sentiment: Positive | Negative | Neutral Provide a brief explanation highlighting the key words, phrases, emoticons, or other elements that influenced your decision. TWEET: <<<paste tweet>>>
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How you would do this use case, in plain words. Anyone can post.
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GET /api/jobs/tweet-tone-sentiment - Schema:
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POST /api/jobs/method— same fields as the form. No API key yet.
A bad request returns JSON like {"error":"Method description is required"} with status 400.
| 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": "tweet-tone-sentiment",
"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"
}