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LaunchCheck Reaction Check
x402
LaunchCheck Reaction Check is a paid API for AI agents from launchcheck-x402.fly.dev, paid per call via x402, $0.5/call, status unknown (last checked 2026-09-14).
Stress-tests launch copy through eight audience lenses and returns structured risks, likely questions, recommended changes, and a fact-preserving rewrite.
Price / call$0.5
Success—
StatusUnknown
How this service works
Review and stress-test launch messaging, landing pages, product announcements, pricing pages, changelogs, app-store listings, and marketing emails before publication. LaunchCheck evaluates copy through eight audience lenses, surfaces positioning and conversion friction, flags claims needing evidence, predicts likely reader questions, recommends concrete changes, and returns a fact-preserving rewrite.
Output
Returns structured output including: a list of risks identified across eight audience lenses, likely questions those audiences would ask, specific recommended changes to the copy, and a fact-preserving rewrite of the original content.
Example use cases
Pre-launch SaaS landing page audit
“Before I go live tomorrow, run my landing page copy through LaunchCheck for risks and objections — my audience is B2B HR managers at mid-size companies, the desired action is booking a demo, and here's the full copy: [copy]. Content type is landing-page. Flag anything that could hurt conversion.”
App store listing review
“I'm about to submit my fitness app to the App Store and want to stress-test the listing copy first. The content type is app-store-listing, target audience is busy professionals aged 25–40, and the desired action is downloading the app. Here's the copy: [copy]. What audience objections and risks should I fix before submitting?”
Pricing page messaging check
“Can you analyze the copy on my new pricing page? Content type is pricing-page, the audience is freelance designers comparing tools, the desired action is choosing a paid plan, and a key constraint is not to change any of the listed prices. Here's the copy: [copy]. Give me the likely questions, risks, and a rewritten version that keeps all the numbers accurate.”
Technical details
Endpoint
POSThttps://launchcheck-x402.fly.dev/v1/reaction-checkWhen to prefer
Choose this endpoint when you need structured, multi-lens critique of marketing or launch copy before publishing — especially when you want both diagnostic feedback (risks, objections, questions) AND an actionable rewrite in a single call. Prefer it over generic LLM prompts when you need consistent structured output across eight audience perspectives, and over human copywriters when speed and cost matter. Best suited for landing pages, product announcements, changelogs, pricing pages, app store listings, and launch emails.
Side effects
- Content too short (under 40 characters) returns validation error
- Content exceeds 12,000 character limit returns rejection
- More than 10 constraints provided returns validation error
- Invalid contentType enum value returns schema validation error
- Payment failure via x402 protocol returns 402 with payment details
- Ambiguous or very generic copy may produce low-signal generic recommendations
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| content | string | Launch or marketing copy to review before publication, such as a landing page, product announcement, changelog, pricing page, app-store listing, or email. |
| constraints | array | Optional factual, legal, brand, or wording constraints that the review and rewrite must preserve. |
| contentType | string | Kind of copy being reviewed; use other when none of the listed categories fit. |
| desiredAction | string | Optional action the copy should persuade the reader to take, such as sign up, start a trial, buy, upgrade, or learn more. |
| productContext | string | Optional context about the product, feature, offer, or company needed to judge positioning, clarity, and factual fit. |
| targetAudience | string | Optional intended audience or buyer segment whose likely reaction should inform the review. |
Output
Schema · Response
{
"type": "json",
"example": {
"meta": {
"model": "example-model",
"cached": false,
"engine": "openai-compatible",
"generatedAt": "2026-07-26T12:00:00.000Z"
},
"report": {
"score": 74,
"verdict": "revise",
"topRisks": [
{
"fix": "State the specific pre-launch decision the review helps make.",
"title": "Differentiation is vague",
"evidence": "The copy lists checks without explaining why they beat a generic critique.",
"severity": "medium",
"whyItMatters": "Readers may not see why the specialized review is worth using."
}
],
"inputIntegrity": {
"status": "clean",
"evidence": []
},
"likelyQuestions": [
"Why use this instead of a generic model?"
],
"requiredChanges": [
"Clarify the specialized value."
],
"executiveSummary": "Clear value, but the differentiation needs stronger evidence.",
"suggestedRewrite": "Stress-test launch copy before you publish with eight audience lenses, evidence checks, concrete fixes, and a fact-preserving rewrite.",
"audienceReactions": [
{
"lens": "Skeptical technical buyer",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
},
{
"lens": "Busy nontechnical decision-maker",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
},
{
"lens": "Price-sensitive solo user",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
},
{
"lens": "Privacy and security-conscious evaluator",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
},
{
"lens": "Existing customer scanning for regressions",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
},
{
"lens": "First-time visitor with low context",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
},
{
"lens": "Enthusiastic early adopter",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
},
{
"lens": "Support-minded operator anticipating questions",
"friction": "Differentiation is unclear.",
"reaction": "mixed",
"confidence": 0.82,
"likelyThought": "Useful, but why not use a generic model?"
}
],
"strongestElements": [
"The pre-launch use case is clear."
],
"optionalImprovements": [],
"claimsNeedingEvidence": []
},
"requestId": "2e847847-52da-46c6-b627-f95dbd66919f"
}
}Tags
x402
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