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Integrate Kling AI video generation into CI/CD pipelines.
Passed 18 of 23 test cases with the skill, and 15 of 23 without it.
and 2 more in the full report
4 fixed14 passed either way4 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 4 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which standard environment variable names should be exported to convey API credentials to the Python automation script?
| Checked | Without skill | With skill |
|---|---|---|
| Uses KLING_ACCESS_KEY for the access key variable. | Missed | Passed |
| Uses KLING_SECRET_KEY for the secret key variable. | Missed | Passed |
The instructions your model reads when the skill loads.
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 \
test cases passed, vs 15 / 23 without
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