菜单
Integrate Kling AI video generation into CI/CD pipelines. Use when automating video content in GitHub Actions or GitLab CI. Trigger with phrases like 'klingai ci', 'kling ai github actions', 'klingai automation', 'automated video generation'.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill klingai-ci-integration命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Implement user sign-up and sign-in flows with Clerk. Use when building authentication UI, customizing sign-in experience, or implementing OAuth social login. Trigger with phrases like "clerk sign-in", "clerk sign-up", "clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk. Use when managing user sessions, configuring route protection, or implementing token refresh and custom JWT templates. Trigger with phrases like "clerk session", "clerk middleware", "clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management. Use when implementing SSO integration, configuring role-based permissions, or setting up organization-level controls. Trigger with phrases like "clerk SSO", "clerk RBAC", "clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
正在显示 SKILL.md
基于 SOC 职业分类
| name | klingai-ci-integration |
| description | Integrate Kling AI video generation into CI/CD pipelines. Use when automating video content in GitHub Actions or GitLab CI. Trigger with phrases like 'klingai ci', 'kling ai github actions', 'klingai automation', 'automated video generation'. |
| allowed-tools | Read, Write, Edit, Bash(npm:*), Grep |
| version | 1.18.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","kling-ai","ci-cd","automation"] |
| compatibility | Designed for Claude Code, also compatible with Codex and OpenClaw |
Automate video generation in CI/CD pipelines. Common use cases: generate product demos on release, create marketing videos from prompts in a YAML file, regression-test video quality across model versions.
# .github/workflows/generate-videos.yml
name: Generate Videos
on:
workflow_dispatch:
inputs:
prompt:
description: "Video prompt"
required: true
model:
description: "Model version"
default: "kling-v2-master"
jobs:
generate:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: "3.11"
- name: Install dependencies
run: pip install PyJWT requests
- name: Generate video
env:
KLING_ACCESS_KEY: ${{ secrets.KLING_ACCESS_KEY
#!/usr/bin/env python3
"""scripts/generate-video.py -- CI-friendly video generation."""
import argparse
import jwt
import time
import os
import requests
import sys
BASE = "https://api.klingai.com/v1"
def get_headers():
ak, sk = os.environ["KLING_ACCESS_KEY"], os.environ["KLING_SECRET_KEY"]
token = jwt.encode(
{"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5},
sk, algorithm="HS256", headers={"alg": "HS256", "typ": "JWT"}
)
return {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--prompt", required=True)
parser.add_argument("--model", default="kling-v2-master")
parser.add_argument("--duration", default="5")
parser.add_argument("--mode", default="standard")
parser.add_argument("--output", default="output/")
parser.add_argument("--timeout", type=int, default=600)
args = parser.parse_args()
os.makedirs(args.output, exist_ok=True)
# Submit
r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": args.model,
"prompt": args.prompt,
"duration": args.duration,
"mode": args.mode,
})
r.raise_for_status()
task_id = r.json()["data"]["task_id"]
print(f"Task submitted: {task_id}")
# Poll
start = time.monotonic()
while time.monotonic() - start < args.timeout:
time.sleep(15)
result = requests.get(
f"{BASE}/videos/text2video/{task_id}", headers=get_headers()
).json()
status = result["data"]["task_status"]
elapsed = int(time.monotonic() - start)
print(f"[{elapsed}s] Status: {status}")
if status == "succeed":
video_url = result["data"]["task_result"]["videos"][0]["url"]
filepath = os.path.join(args.output, f"{task_id}.mp4")
with open(filepath, "wb") as f:
f.write(requests.get(video_url).content)
print(f"Saved: {filepath}")
return
if status == "failed":
print(f"FAILED: {result['data'].get('task_status_msg')}", file=sys.stderr)
sys.exit(1)
print("TIMEOUT: generation did not complete", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
main()
# video-prompts.yml
videos:
- name: product-hero
prompt: "Sleek laptop floating in space with particle effects"
model: kling-v2-6
mode: professional
- name: feature-demo
prompt: "Dashboard interface morphing between screens"
model: kling-v2-5-turbo
mode: standard
import yaml
with open("video-prompts.yml") as f:
config = yaml.safe_load(f)
for video in config["videos"]:
task_id = submit_async(video["prompt"], model=video["model"])
print(f"{video['name']}: {task_id}")
# .gitlab-ci.yml
generate-video:
image: python:3.11-slim
stage: build
script:
- pip install PyJWT requests
- python3 scripts/generate-video.py --prompt "$VIDEO_PROMPT" --output output/
artifacts:
paths:
- output/*.mp4
expire_in: 7 days
variables:
KLING_ACCESS_KEY: $KLING_ACCESS_KEY
KLING_SECRET_KEY: $KLING_SECRET_KEY
| Platform | Store AK/SK in |
|---|---|
| GitHub Actions | Repository Secrets |
| GitLab CI | CI/CD Variables (masked) |
| AWS CodeBuild | Parameter Store / Secrets Manager |
| GCP Cloud Build | Secret Manager |
Never put API keys in the workflow YAML or commit them to the repo.