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skill-audit LLM Chat Completion
x402
skill-audit LLM Chat Completion is a paid API for AI agents from eltociear-skill-audit.hf.space, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-15, last successful call 2026-08-19).
Run a prompt against an open-weight 20B-class language model with automatic failover across multiple inference backends, returning generated text.
Price / call$0.02
Success25%
StatusUnknown
How this service works
Run a chat completion against an open-weight 20B-class model — plain prompt in, text out, with automatic failover across independent inference backends so a single call still answers when any one provider is down or rate-limiting
Output
A plain text string containing the model's generated response to the supplied prompt, produced by an open-weight 20B-class model routed through whichever inference backend is currently available and not rate-limited.
Example use cases
Automated content drafting pipeline
“Use the open-weight LLM to draft a short product description for 'EcoBottle Pro', a stainless steel water bottle — keep the system instruction as 'You are a marketing copywriter' and limit the response to 300 tokens.”
Fallback LLM for resilient agent workflows
“My main LLM provider keeps hitting rate limits — can you run this customer support prompt through the multi-backend open-weight model instead? System: 'You are a helpful customer support agent.' Prompt: 'How do I reset my account password?'”
Code explanation with custom persona
“Ask the 20B model to explain what this Python snippet does, using a temperature of 0.2 and a system instruction of 'You are a senior software engineer': 'def fib(n): return n if n <= 1 else fib(n-1) + fib(n-2)'”
Technical details
Endpoint
POSThttps://eltociear-skill-audit.hf.space/llm/chatWhen to prefer
Choose this endpoint when you need reliable open-weight LLM inference and cannot tolerate downtime from a single provider — the automatic multi-backend failover ensures continuity even when individual inference providers are down or rate-limiting. Prefer it over direct model API calls when resilience matters more than latency optimization, or when you want to avoid vendor lock-in with a pay-per-call model at $0.02 USDC.
Side effects
- All inference backends simultaneously unavailable or overloaded — returns an error with no generated text
- Prompt or system string exceeds model context window — may return truncated output or an error
- max_tokens set to 0 or above 4000 — likely rejected with a validation error
- temperature outside 0-2 range — may cause unexpected behavior or rejection
- Empty or missing prompt field — likely returns an error or empty response
- Payment of $0.02 USDC not fulfilled — request rejected by x402 payment gate
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| prompt | string | The user prompt |
| system | string | Optional system instruction |
| max_tokens | integer | 1-4000 (default 800) |
| temperature | number | 0-2 (default 0.3) |
Output
Schema · Response
{
"type": "json",
"example": {
"usage": {
"ms": 740,
"total_tokens": 210
},
"answer": "EIP-3009 lets a holder sign an off-chain authorization…",
"provider": "llm7"
}
}Tags
x402
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