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Strale Prompt Optimizer
x402
Strale Prompt Optimizer is a paid API for AI agents from api.strale.io, paid per call via x402, $0.162/call, status unknown (last checked 2026-09-15).
Optimizes an AI prompt by analyzing the task description, current prompt, and examples to produce an improved version
Price / call$0.162
Success—
StatusUnknown
How this service works
The trust layer for AI agents — 250+ independently tested data capabilities across 27 countries. Execute capabilities via REST, MCP, A2A, or x402 micropayments. Every call returns an audit record with cryptographic chain hashing.
Output
Returns an optimized version of the submitted prompt, tailored to the task description and informed by good/bad examples. The response includes an auditable record with cryptographic chain hashing for traceability.
Example use cases
Fixing a flawed customer support prompt
“My customer support prompt keeps giving vague responses. Here's the current prompt: 'Handle this customer issue.' The task is to triage and resolve billing complaints. I have 3 examples where it worked and 2 where it totally failed — can you optimize the prompt so it's more specific and reliable?”
Improving a code-generation system prompt
“I'm building a coding assistant and my system prompt — 'Write clean Python code for the following task' — produces inconsistent results. Can you optimize it using my good and bad output examples so it always follows PEP-8 and includes docstrings?”
Refining a content-moderation classifier prompt
“I need to improve this content moderation prompt: 'Decide if this post violates community guidelines.' The task is to flag hate speech and spam. Here are examples of correct and incorrect classifications — please optimize the prompt to reduce false positives.”
Technical details
Endpoint
POSThttps://api.strale.io/x402/v2/prompt-optimizeWhen to prefer
Choose this endpoint when you need a structured, auditable prompt optimization service backed by cryptographic audit trails — especially in enterprise or compliance-sensitive workflows where traceability of AI tooling decisions matters. Prefer it over ad-hoc LLM self-improvement loops when you have concrete good/bad examples to guide the optimization and want a reproducible, versioned result.
Side effects
- Missing current_prompt or task_description causes a validation error
- Malformed or empty examples arrays may reduce optimization quality
- Ambiguous task descriptions can produce generic rather than targeted improvements
- Payment failure (insufficient USDC balance) results in a 402 response and no output
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bad_examples | array | |
| good_examples | array | |
| current_prompt | string | |
| task_description | string |
Tags
x402
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