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KiHustle Clickbait Detector
x402
KiHustle Clickbait Detector is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-23).
Analyzes text to detect whether it contains clickbait language or tactics
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a 'result' field indicating whether the submitted text was classified as clickbait or not, along with a 'status' field confirming successful processing. Specific confidence scores or probability values are not documented in the current schema.
Example use cases
Headline quality check before publishing
“Before I publish this blog post, can you check if the headline 'This One Weird Habit Made Me $10,000 in a Month' is clickbait?”
Newsletter subject line review
“I'm about to send out my newsletter — can you tell me if this subject line sounds like clickbait: 'Doctors HATE this simple morning routine'?”
Content moderation for user submissions
“A user submitted this article title to our platform: 'The Secret the Government Doesn't Want You to Know About Solar Panels' — is it clickbait?”
Technical details
Endpoint
POSThttps://kihustle.tech/api/v1/clickbait-detector?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need a lightweight, low-cost ($0.002/call) automated check for clickbait language in headlines, article titles, email subject lines, or short content snippets. It is well-suited for content pipelines, editorial workflows, or moderation queues where quick clickbait flagging is needed at scale. Prefer a more robust NLP classification service if you require confidence scores, multi-label output, or multilingual support.
Side effects
- Empty or missing 'text' field returns an error or undefined behavior
- Very short or ambiguous text may produce unreliable classification
- Non-English text (given German-language site context) may yield inaccurate results
- Oversized text payloads may be rejected or truncated
- Generic success response schema makes it hard to distinguish nuanced outputs
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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