From klingai-pack
Automates Kling AI text-to-video generation in GitHub Actions and GitLab CI pipelines using Python scripts and JWT auth. For release-triggered demos or marketing videos.
How this skill is triggered — by the user, by Claude, or both
Slash command
/klingai-pack:klingai-ci-integrationThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
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.
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 }}
KLING_SECRET_KEY: ${{ secrets.KLING_SECRET_KEY }}
run: |
python3 scripts/generate-video.py \
--prompt "${{ inputs.prompt }}" \
--model "${{ inputs.model }}" \
--output output/
- name: Upload artifact
uses: actions/upload-artifact@v4
with:
name: generated-video
path: output/*.mp4
retention-days: 7
#!/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.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin klingai-packProvides production reference architecture for scalable Kling AI video generation platforms, including API gateway, job queues, workers, storage, and monitoring.
Generates videos from text prompts or images, animates still images, and creates talking avatars from photos with audio using Kling AI models (VIDEO 3.0, Avatar 2.0, etc.). Handles multi-shot storyboards, character consistency, and prompt engineering.
Integrates generative video into applications: text-to-video, image-to-video, avatar video. Covers async architecture, cost optimization, cinematographic prompting, provider selection (FAL.ai, Veo, Sora, Runway).