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From klingai-pack
Provides production checklist for Kling AI video generation integrations, verifying auth, errors, costs, task handling, safety, security, monitoring, and performance before deployment.
How this skill is triggered — by the user, by Claude, or both
Slash command
/klingai-pack:klingai-prod-checklistThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Checklist covering authentication, error handling, cost controls, monitoring, security, and content policy before deploying Kling AI video generation to production.
Checklist covering authentication, error handling, cost controls, monitoring, security, and content policy before deploying Kling AI video generation to production.
.env in repo)Authorization: Bearer <token> format verifiedtask_status: "failed" logs task_status_msgduration sent as string "5" not integer 5standard mode used for non-final renders# Pre-batch credit check
credits_needed = len(prompts) * 10 # 10 credits per 5s standard
if credits_needed > DAILY_BUDGET:
raise RuntimeError(f"Batch needs {credits_needed}, budget is {DAILY_BUDGET}")
callback_url used instead of polling in productionrequests.Session()# Connection pooling
session = requests.Session()
adapter = requests.adapters.HTTPAdapter(pool_connections=5, pool_maxsize=10)
session.mount("https://", adapter)
from kling_client import KlingClient
c = KlingClient()
result = c.text_to_video("test: blue sky with clouds", duration=5, mode="standard")
assert result["videos"][0]["url"], "No video URL"
print("READY FOR PRODUCTION")
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin klingai-packReviews security and compliance for Kling AI video generation API integrations using checklists for credentials, data flow, input validation, privacy, and GDPR prep.
Executes production checklist for Anthropic Claude API integrations: auth/keys, error handling, rate limits/costs, reliability, observability. Use before launch.
Guides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.