'Download and store Kling AI generated videos in cloud storage (S3, GCS, Azure). Use when persisting videos or building CDN pipelines. Trigger with phrases like ''klingai storage'', ''save klingai video'', ''kling ai s3 upload'', ''klingai cloud storage''. '
Scanned 9/12/2026
Install to Claude Code
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---
name: klingai-storage-integration
description: "'Download and store Kling AI generated videos in cloud storage (S3, GCS, Azure). Use when persisting videos or building CDN pipelines. Trigger with phrases like ''klingai storage'', ''save klingai video'', ''kling ai s3 upload'', ''klingai cloud storage''. '"
allowed-tools: "Read, Write, Edit, Bash(npm:*), Grep"
category: mcp-and-integrations
source_repo: jeremylongshore/claude-code-plugins-plus-skills
source_path: "skills/.curated/klingai-storage-integration/SKILL.md"
source_url: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/HEAD/skills/.curated/klingai-storage-integration/SKILL.md
---
# Kling AI Storage Integration
## Overview
Kling AI video URLs from `task_result.videos[].url` are temporary CDN links that expire. You must download and store videos in your own storage. This skill covers S3, GCS, and Azure Blob.
## Download from Kling CDN
```python
import requests
import os
def download_video(video_url: str, output_dir: str = "output") -> str:
"""Download generated video from Kling CDN."""
os.makedirs(output_dir, exist_ok=True)
# Extract filename or generate one
filename = video_url.split("/")[-1].split("?")[0]
if not filename.endswith(".mp4"):
filename = f"kling_{int(time.time())}.mp4"
filepath = os.path.join(output_dir, filename)
response = requests.get(video_url, stream=True, timeout=120)
response.raise_for_status()
with open(filepath, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
size_mb = os.path.getsize(filepath) / (1024 * 1024)
print(f"Downloaded: {filepath} ({size_mb:.1f} MB)")
return filepath
```
## Upload to AWS S3
```python
import boto3
def upload_to_s3(filepath: str, bucket: str, key_prefix: str = "kling-videos/") -> str:
"""Upload video to S3 and return public URL."""
s3 = boto3.client("s3")
filename = os.path.basename(filepath)
s3_key = f"{key_prefix}{filename}"
s3.upload_file(
filepath, bucket, s3_key,
ExtraArgs={"ContentType": "video/mp4", "CacheControl": "max-age=86400"}
)
url = f"https://{bucket}.s3.amazonaws.com/{s3_key}"
print(f"Uploaded to S3: {url}")
return url
# Generate signed URL for private buckets
def get_signed_url(bucket: str, key: str, expiry: int = 3600) -> str:
s3 = boto3.client("s3")
return s3.generate_presigned_url(
"get_object",
Params={"Bucket": bucket, "Key": key},
ExpiresIn=expiry,
)
```
## Upload to Google Cloud Storage
```python
from google.cloud import storage
def upload_to_gcs(filepath: str, bucket_name: str, prefix: str = "kling-videos/") -> str:
"""Upload video to GCS and return public URL."""
client = storage.Client()
bucket = client.bucket(bucket_name)
filename = os.path.basename(filepath)
blob = bucket.blob(f"{prefix}{filename}")
blob.upload_from_filename(filepath, content_type="video/mp4")
blob.make_public() # or use signed URLs for private access
print(f"Uploaded to GCS: {blob.public_url}")
return blob.public_url
# Signed URL for private access
def get_gcs_signed_url(bucket_name: str, blob_name: str, expiry_min: int = 60) -> str:
from datetime import timedelta
client = storage.Client()
bucket = client.bucket(bucket_name)
blob = bucket.blob(blob_name)
return blob.generate_signed_url(expiration=timedelta(minutes=expiry_min))
```
## Upload to Azure Blob Storage
```python
from azure.storage.blob import BlobServiceClient
def upload_to_azure(filepath: str, container: str,
connection_string: str = None) -> str:
"""Upload video to Azure Blob Storage."""
conn_str = connection_string or os.environ["AZURE_STORAGE_CONNECTION_STRING"]
client = BlobServiceClient.from_connection_string(conn_str)
filename = os.path.basename(filepath)
blob_client = client.get_blob_client(container=container, blob=f"kling-videos/{filename}")
with open(filepath, "rb") as f:
blob_client.upload_blob(f, content_type="video/mp4", overwrite=True)
url = blob_client.url
print(f"Uploaded to Azure: {url}")
return url
```
## End-to-End Pipeline
```python
def generate_and_store(prompt: str, bucket: str, provider: str = "s3"):
"""Generate video with Kling AI and store in cloud."""
# 1. Generate
r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": "kling-v2-master",
"prompt": prompt,
"duration": "5",
"mode": "standard",
}).json()
task_id = r["data"]["task_id"]
# 2. Poll
result = poll_task("/videos/text2video", task_id)
video_url = result["videos"][0]["url"]
# 3. Download
filepath = download_video(video_url)
# 4. Upload
if provider == "s3":
return upload_to_s3(filepath, bucket)
elif provider == "gcs":
return upload_to_gcs(filepath, bucket)
elif provider == "azure":
return upload_to_azure(filepath, bucket)
# 5. Cleanup temp file
os.remove(filepath)
```
## Metadata Preservation
```python
import json
def save_with_metadata(filepath: str, task_id: str, prompt: str, model: str):
"""Save video metadata alongside the file."""
meta = {
"task_id": task_id,
"prompt": prompt,
"model": model,
"generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
"filename": os.path.basename(filepath),
}
meta_path = filepath.replace(".mp4", ".meta.json")
with open(meta_path, "w") as f:
json.dump(meta, f, indent=2)
return meta_path
```
## Resources
- [API Reference](https://app.klingai.com/global/dev/document-api/apiReference/model/textToVideo)
- [AWS S3 SDK](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html)
- [Google Cloud Storage](https://cloud.google.com/storage/docs)
---
**Source:** [`jeremylongshore/claude-code-plugins-plus-skills`](https://github.com/jeremylongshore/claude-code-plugins-plus-skills) → `skills/.curated/klingai-storage-integration/SKILL.md`
**Also appears in:** `jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/klingai-pack/skills/klingai-storage-integration/SKILL.md`
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