From klingai-pack
Extends Kling AI videos using video-extend API to append seamless continuations. Takes task_id, optional prompt/duration/mode; polls for completion. Python example included.
How this skill is triggered — by the user, by Claude, or both
Slash command
/klingai-pack:klingai-video-extensionThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Extend an existing video by appending additional seconds. The extension endpoint takes the `task_id` of a completed video and generates a seamless continuation.
Extend an existing video by appending additional seconds. The extension endpoint takes the task_id of a completed video and generates a seamless continuation.
Endpoint: POST https://api.klingai.com/v1/videos/video-extend
| Parameter | Type | Required | Description |
|---|---|---|---|
task_id | string | Yes | Task ID of the completed source video |
prompt | string | No | Motion/scene description for extension |
duration | string | No | Extension length: "5" (default) |
mode | string | No | "standard" or "professional" |
model_name | string | No | Default: "kling-v2-master" |
callback_url | string | No | Webhook for completion |
import jwt, time, os, requests
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"}
# Step 1: Generate the initial 5s video
initial = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": "kling-v2-master",
"prompt": "A rocket launching from a desert landscape, cinematic",
"duration": "5",
"mode": "standard",
}).json()
initial_task_id = initial["data"]["task_id"]
# Wait for completion...
# (poll until task_status == "succeed")
# Step 2: Extend by 5 more seconds
extension = requests.post(f"{BASE}/videos/video-extend", headers=get_headers(), json={
"task_id": initial_task_id,
"prompt": "The rocket ascends through clouds into the stratosphere",
"duration": "5",
"mode": "standard",
}).json()
ext_task_id = extension["data"]["task_id"]
# Step 3: Poll extension task
while True:
time.sleep(15)
result = requests.get(
f"{BASE}/videos/video-extend/{ext_task_id}", headers=get_headers()
).json()
if result["data"]["task_status"] == "succeed":
extended_url = result["data"]["task_result"]["videos"][0]["url"]
print(f"Extended video: {extended_url}")
break
elif result["data"]["task_status"] == "failed":
print(f"Failed: {result['data']['task_status_msg']}")
break
def chain_extensions(initial_task_id: str, prompts: list[str],
duration: str = "5", mode: str = "standard") -> list[str]:
"""Chain multiple extensions to build a longer video."""
current_task_id = initial_task_id
video_urls = []
for i, prompt in enumerate(prompts):
print(f"Extension {i + 1}/{len(prompts)}: submitting...")
# Submit extension
r = requests.post(f"{BASE}/videos/video-extend", headers=get_headers(), json={
"task_id": current_task_id,
"prompt": prompt,
"duration": duration,
"mode": mode,
}).json()
ext_task_id = r["data"]["task_id"]
# Poll for completion
while True:
time.sleep(15)
result = requests.get(
f"{BASE}/videos/video-extend/{ext_task_id}", headers=get_headers()
).json()
status = result["data"]["task_status"]
if status == "succeed":
url = result["data"]["task_result"]["videos"][0]["url"]
video_urls.append(url)
current_task_id = ext_task_id # next extension chains from this
print(f"Extension {i + 1} complete: {url}")
break
elif status == "failed":
raise RuntimeError(f"Extension {i + 1} failed: {result['data']['task_status_msg']}")
return video_urls
# Generate initial 5s
initial_r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": "kling-v2-master",
"prompt": "Morning sunrise over a mountain lake, mist rising",
"duration": "5",
"mode": "standard",
}).json()
initial_id = initial_r["data"]["task_id"]
# ... poll until complete ...
# Chain 3 more extensions = 5 + 5 + 5 + 5 = 20 seconds total
extensions = chain_extensions(initial_id, [
"Sun rises higher, birds begin flying across the lake",
"A deer approaches the water's edge to drink",
"Wide shot pulling back to reveal the full mountain range",
])
Each extension costs the same as a new generation:
| Extension Duration | Standard | Professional |
|---|---|---|
| 5 seconds | 10 credits | 35 credits |
A 20-second video (initial + 3 extensions) costs 40 credits in standard mode.
| Error | Cause | Fix |
|---|---|---|
Invalid task_id | Source task doesn't exist | Verify task_id is from a completed generation |
| Source not complete | Extending a task still processing | Wait for source task to reach succeed status |
| Extension failed | Prompt conflict with source | Align extension prompt with original scene |
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin klingai-packGenerates videos from text prompts via Kling AI API. Supports v1-v2.6 models, professional mode, camera control, negative prompts. Python example for JWT auth, task polling.
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.
Generates AI videos from text descriptions or images using Google Veo 3.1 (default) or OpenAI Sora. Supports dialogue/audio, reference images, image-to-video animation, and interactive requirement gathering.