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klingai-ci-integration - Agent Skill - Agent Skills
/ Skills / klingai-ci-integration klingai-ci-integration MIT
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'.
Use the skills CLI to install this skill with one command. Auto-detects all installed AI assistants.
Method 1 - skills CLI
npx skills i jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/klingai-pack/skills/klingai-ci-integrationMethod 2 - openskills (supports sync & update)
npx openskills install jeremylongshore/claude-code-plugins-plus-skillsAuto-detects Claude Code, Cursor, Codex CLI, Gemini CLI, and more. One install, works everywhere.
Installation Path
Download and extract to one of the following locations:
Claude Code Cursor OpenCode Gemini CLI Codex CLI
~/.claude/skills/klingai-ci-integration/ Back No setup needed. Let our cloud agents run this skill for you.
Select Model
Claude Haiku 4.5 $0.10 Claude Sonnet 4.5 $0.20 Claude Opus 4.5 $0.50 Claude Sonnet 4.5 $0.20 /task
Best for coding tasks
Environment setup included
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 "${{ inputs.model }}" \ --output output/
- name : Upload artifact uses : actions/upload-artifact@v4 with : name : generated-video path : output/*.mp4 retention-days : 7 #!/usr/bin/env python3 """scripts/generate-video.py -- CI-friendly video generation.""" import argparse # video-prompts.yml videos : - name : product-hero prompt : "Sleek laptop floating in space with particle effects" model : kling-v2-6 mode : professional - name : feature-demo prompt : "Dashboard interface morphing between screens" model : kling-v2-5-turbo mode : standard import yaml
with open ( "video-prompts.yml" ) as f: config = yaml.safe_load(f)
for video in config[ "videos" ]: task_id = submit_async(video[ "prompt" ], model = video[ "model" ]) print ( f " { video[ 'name' # .gitlab-ci.yml generate-video : image : python:3.11-slim stage : build script : - pip install PyJWT requests - python3 scripts/generate-video.py --prompt "$VIDEO_PROMPT" --output output/ artifacts : paths : - output/*.mp4 expire_in : 7 days variables : KLING_ACCESS_KEY : $KLING_ACCESS_KEY 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.
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()
]
}
:
{
task_id
}
"
)
KLING_SECRET_KEY : $KLING_SECRET_KEY
Claude Sonnet 4.5