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Handle Kling AI API rate limits with backoff and queuing strategies.
Passed 19 of 22 test cases with the skill, and 13 of 22 without it.
and 3 more in the full report
6 fixed13 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
What upper boundary cap is applied to base wait calculations in the exponential backoff helper prior to adding jitter?
| Checked | Without skill | With skill |
|---|---|---|
| The base backoff wait time is capped at a maximum of 60.0 seconds | Missed | Passed |
The instructions your model reads when the skill loads.
Kling AI enforces rate limits per API key. When exceeded, the API returns 429 Too Many Requests. This skill covers detection, backoff strategies, request queuing, and concurrent job management.
| Tier | Concurrent Tasks | Requests/Min | Notes |
|---|---|---|---|
| Free | 1 | 10 | 66 daily credits cap |
| Standard | 3 | 30 | Per API key |
| Pro | 5 | 60 | Per API key |
| Enterprise | 10+ | Custom | Contact sales |
import time, random, requests
def exponential_backoff(attempt: int, base: float = 1.0, max_wait: float = 60.0) -> float:
"""Calculate wait time with jitter to avoid thundering herd."""
wait = min(base * (2 ** attempt), max_wait)
jitter = random.uniform(0, wait * 0.5)
return wait + jitter
def request_with_retry(method, url, headers, json=None, max_retries=5):
for attempt in range(max_retries + 1):
response = method(url, headers=headers, json=json, timeout=30)
if response.status_code == 429:
if attempt == max_retries:
raise RuntimeError("Rate limit: max retries exceeded")
wait = exponential_backoff(attempt)
print(f"429 rate limited. Waiting {wait:.1f}s (attempt {attempt + 1})")
time.sleep(wait)
continue
if response.status_code >= 500:
if attempt == max_retries:
test cases passed, vs 13 / 22 without
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