From klingai-pack
Generates first Kling AI text-to-video using minimal Python or Node.js code. Covers auth, task creation, polling, and download via REST API. For quickstarts or setup tests.
How this skill is triggered — by the user, by Claude, or both
Slash command
/klingai-pack:klingai-hello-worldThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Generate your first AI video in under 20 lines of code. This skill walks through the complete create-poll-download cycle using the Kling AI REST API.
Generate your first AI video in under 20 lines of code. This skill walks through the complete create-poll-download cycle using the Kling AI REST API.
Base URL: https://api.klingai.com/v1
klingai-install-auth setuprequests and PyJWTimport jwt, time, os, requests
# --- Auth ---
def get_token():
ak = os.environ["KLING_ACCESS_KEY"]
sk = os.environ["KLING_SECRET_KEY"]
payload = {"iss": ak, "exp": int(time.time()) + 1800, "nbf": int(time.time()) - 5}
return jwt.encode(payload, sk, algorithm="HS256",
headers={"alg": "HS256", "typ": "JWT"})
BASE = "https://api.klingai.com/v1"
HEADERS = {"Authorization": f"Bearer {get_token()}", "Content-Type": "application/json"}
# --- Step 1: Create task ---
task = requests.post(f"{BASE}/videos/text2video", headers=HEADERS, json={
"model_name": "kling-v2-master",
"prompt": "A golden retriever running through autumn leaves in slow motion, cinematic lighting",
"duration": "5",
"aspect_ratio": "16:9",
"mode": "standard",
}).json()
task_id = task["data"]["task_id"]
print(f"Task created: {task_id}")
# --- Step 2: Poll until complete ---
import time as t
while True:
t.sleep(10)
status = requests.get(f"{BASE}/videos/text2video/{task_id}", headers=HEADERS).json()
state = status["data"]["task_status"]
print(f"Status: {state}")
if state == "succeed":
video_url = status["data"]["task_result"]["videos"][0]["url"]
print(f"Video ready: {video_url}")
break
elif state == "failed":
print(f"Failed: {status['data']['task_status_msg']}")
break
import jwt from "jsonwebtoken";
const BASE = "https://api.klingai.com/v1";
function getHeaders() {
const token = jwt.sign(
{ iss: process.env.KLING_ACCESS_KEY, exp: Math.floor(Date.now() / 1000) + 1800,
nbf: Math.floor(Date.now() / 1000) - 5 },
process.env.KLING_SECRET_KEY,
{ algorithm: "HS256", header: { typ: "JWT" } }
);
return { Authorization: `Bearer ${token}`, "Content-Type": "application/json" };
}
// Create task
const res = await fetch(`${BASE}/videos/text2video`, {
method: "POST",
headers: getHeaders(),
body: JSON.stringify({
model_name: "kling-v2-master",
prompt: "A golden retriever running through autumn leaves in slow motion",
duration: "5",
aspect_ratio: "16:9",
mode: "standard",
}),
});
const { data } = await res.json();
console.log(`Task: ${data.task_id}`);
// Poll
const poll = setInterval(async () => {
const r = await fetch(`${BASE}/videos/text2video/${data.task_id}`, { headers: getHeaders() });
const s = await r.json();
if (s.data.task_status === "succeed") {
console.log("Video:", s.data.task_result.videos[0].url);
clearInterval(poll);
} else if (s.data.task_status === "failed") {
console.error("Failed:", s.data.task_status_msg);
clearInterval(poll);
}
}, 10000);
{
"code": 0,
"message": "success",
"data": {
"task_id": "abc123...",
"task_status": "succeed",
"task_result": {
"videos": [{
"id": "vid_001",
"url": "https://cdn.klingai.com/...",
"duration": "5.0"
}]
}
}
}
| Status | Meaning |
|---|---|
submitted | Task queued, waiting for processing |
processing | Video generation in progress |
succeed | Complete — video URL available |
failed | Generation failed — check task_status_msg |
| Problem | Fix |
|---|---|
401 response | JWT token expired or AK/SK wrong |
task_status: failed | Prompt too vague — add visual detail |
Empty videos array | Task still processing — poll longer |
| Slow generation | Standard mode takes 60-120s; use mode: "standard" for first test |
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 videos from text prompts via fal.ai models like Kling 2.6 Pro, Sora 2, LTX-2 Pro, Runway Gen-3 Turbo, Luma Dream Machine; supplies endpoints, durations, aspect ratios, prompt structures, TypeScript/Python code.