klingai-sdk-patterns
Provides production-ready SDK patterns for Kling AI, including client wrappers, retry logic, and async handling.
Install
mkdir -p .claude/skills/klingai-sdk-patterns && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/6884" && unzip -o skill.zip -d .claude/skills/klingai-sdk-patterns && rm skill.zipInstalls to .claude/skills/klingai-sdk-patterns
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Production SDK patterns for Kling AI: client wrapper, retry logic, asyncKey capabilities
- →Implement auto-refreshing JWT authentication
- →Execute synchronous and asynchronous video generation
- →Apply exponential backoff for API retries
- →Manage task polling with timeout limits
- →Handle structured error responses
How it works
The skill defines a client wrapper that manages JWT token lifecycle, handles API requests with retry decorators, and polls for task completion.
Inputs & outputs
When to use klingai-sdk-patterns
- →Implement a production-ready Kling AI client
- →Configure automatic JWT token refresh logic
- →Add retry and exponential backoff to API requests
- →Set up structured error handling for video generation tasks
About this skill
Kling AI SDK Patterns
Overview
Production-ready client patterns for the Kling AI API. Covers auto-refreshing JWT, typed request/response models, exponential backoff polling, async batch submission, and structured error handling.
Python Client Wrapper
import jwt
import time
import os
import requests
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class KlingConfig:
access_key: str = field(default_factory=lambda: os.environ["KLING_ACCESS_KEY"])
secret_key: str = field(default_factory=lambda: os.environ["KLING_SECRET_KEY"])
base_url: str = "https://api.klingai.com/v1"
token_buffer_sec: int = 300
poll_interval_sec: int = 10
max_poll_attempts: int = 120 # 20 minutes max
timeout_sec: int = 30
class KlingClient:
"""Production Kling AI client with auto-refreshing JWT."""
def __init__(self, config: Optional[KlingConfig] = None):
self.config = config or KlingConfig()
self._token = None
self._token_expires = 0
@property
def _headers(self) -> dict:
now = int(time.time())
if now >= (self._token_expires - self.config.token_buffer_sec):
payload = {"iss": self.config.access_key, "exp": now + 1800, "nbf": now - 5}
self._token = jwt.encode(payload, self.config.secret_key,
algorithm="HS256",
headers={"alg": "HS256", "typ": "JWT"})
self._token_expires = now + 1800
return {"Authorization": f"Bearer {self._token}",
"Content-Type": "application/json"}
def _post(self, path: str, body: dict) -> dict:
r = requests.post(f"{self.config.base_url}{path}",
headers=self._headers, json=body,
timeout=self.config.timeout_sec)
r.raise_for_status()
return r.json()
def _get(self, path: str) -> dict:
r = requests.get(f"{self.config.base_url}{path}",
headers=self._headers,
timeout=self.config.timeout_sec)
r.raise_for_status()
return r.json()
def _poll_task(self, endpoint: str, task_id: str) -> dict:
"""Poll with exponential backoff until task completes."""
interval = self.config.poll_interval_sec
for attempt in range(self.config.max_poll_attempts):
time.sleep(interval)
result = self._get(f"{endpoint}/{task_id}")
status = result["data"]["task_status"]
if status == "succeed":
return result["data"]["task_result"]
elif status == "failed":
raise KlingGenerationError(result["data"].get("task_status_msg", "Unknown"))
# Increase interval up to 30s max
interval = min(interval * 1.2, 30)
raise KlingTimeoutError(f"Task {task_id} did not complete in time")
# --- Public API ---
def text_to_video(self, prompt: str, **kwargs) -> dict:
body = {"model_name": kwargs.get("model", "kling-v2-master"),
"prompt": prompt,
"duration": str(kwargs.get("duration", 5)),
"aspect_ratio": kwargs.get("aspect_ratio", "16:9"),
"mode": kwargs.get("mode", "standard")}
if kwargs.get("negative_prompt"):
body["negative_prompt"] = kwargs["negative_prompt"]
if kwargs.get("cfg_scale") is not None:
body["cfg_scale"] = kwargs["cfg_scale"]
if kwargs.get("callback_url"):
body["callback_url"] = kwargs["callback_url"]
task = self._post("/videos/text2video", body)
task_id = task["data"]["task_id"]
if kwargs.get("wait", True):
return self._poll_task("/videos/text2video", task_id)
return {"task_id": task_id}
def image_to_video(self, image_url: str, **kwargs) -> dict:
body = {"model_name": kwargs.get("model", "kling-v2-1"),
"image": image_url,
"duration": str(kwargs.get("duration", 5)),
"mode": kwargs.get("mode", "standard")}
if kwargs.get("prompt"):
body["prompt"] = kwargs["prompt"]
task = self._post("/videos/image2video", body)
task_id = task["data"]["task_id"]
if kwargs.get("wait", True):
return self._poll_task("/videos/image2video", task_id)
return {"task_id": task_id}
def extend_video(self, task_id: str, **kwargs) -> dict:
body = {"task_id": task_id,
"prompt": kwargs.get("prompt", ""),
"duration": str(kwargs.get("duration", 5)),
"mode": kwargs.get("mode", "standard")}
result = self._post("/videos/video-extend", body)
new_task_id = result["data"]["task_id"]
if kwargs.get("wait", True):
return self._poll_task("/videos/video-extend", new_task_id)
return {"task_id": new_task_id}
class KlingError(Exception):
pass
class KlingGenerationError(KlingError):
pass
class KlingTimeoutError(KlingError):
pass
Usage
client = KlingClient()
# Synchronous (waits for result)
result = client.text_to_video(
"A cat playing piano in a jazz club",
model="kling-v2-6",
mode="professional",
duration=5,
)
print(result["videos"][0]["url"])
# Fire-and-forget (returns task_id)
task = client.text_to_video("Ocean waves at sunset", wait=False)
print(f"Submitted: {task['task_id']}")
Node.js Client
import jwt from "jsonwebtoken";
class KlingClient {
#token = null;
#tokenExp = 0;
constructor(ak = process.env.KLING_ACCESS_KEY, sk = process.env.KLING_SECRET_KEY) {
this.ak = ak;
this.sk = sk;
this.base = "https://api.klingai.com/v1";
}
#getHeaders() {
const now = Math.floor(Date.now() / 1000);
if (now >= this.#tokenExp - 300) {
this.#token = jwt.sign(
{ iss: this.ak, exp: now + 1800, nbf: now - 5 },
this.sk, { algorithm: "HS256", header: { typ: "JWT" } }
);
this.#tokenExp = now + 1800;
}
return { Authorization: `Bearer ${this.#token}`, "Content-Type": "application/json" };
}
async textToVideo(prompt, opts = {}) {
const res = await fetch(`${this.base}/videos/text2video`, {
method: "POST",
headers: this.#getHeaders(),
body: JSON.stringify({
model_name: opts.model ?? "kling-v2-master",
prompt,
duration: String(opts.duration ?? 5),
aspect_ratio: opts.aspectRatio ?? "16:9",
mode: opts.mode ?? "standard",
}),
});
const { data } = await res.json();
return opts.wait === false ? data : this.#poll("/videos/text2video", data.task_id);
}
async #poll(endpoint, taskId, interval = 10000) {
for (let i = 0; i < 120; i++) {
await new Promise((r) => setTimeout(r, interval));
const res = await fetch(`${this.base}${endpoint}/${taskId}`, {
headers: this.#getHeaders(),
});
const { data } = await res.json();
if (data.task_status === "succeed") return data.task_result;
if (data.task_status === "failed") throw new Error(data.task_status_msg);
interval = Math.min(interval * 1.2, 30000);
}
throw new Error(`Timeout: task ${taskId}`);
}
}
Retry Decorator
import functools
def retry_on_transient(max_retries=3, backoff_base=2):
"""Retry on 429 (rate limit) and 5xx (server) errors."""
def decorator(fn):
@functools.wraps(fn)
def wrapper(*args, **kwargs):
for attempt in range(max_retries + 1):
try:
return fn(*args, **kwargs)
except requests.HTTPError as e:
if e.response.status_code in (429, 500, 502, 503) and attempt < max_retries:
wait = backoff_base ** attempt
time.sleep(wait)
continue
raise
return wrapper
return decorator
# Apply to client methods
KlingClient._post = retry_on_transient()(KlingClient._post)
Resources
When not to use it
- →Simple scripts not requiring production stability
- →Non-Kling AI API integrations
Prerequisites
Limitations
- →Max poll attempts limited to 20 minutes
- →Requires environment variables for authentication
How it compares
It encapsulates complex SDK patterns like JWT refresh and exponential backoff into a reusable client class.
Compared to similar skills
klingai-sdk-patterns side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| klingai-sdk-patterns (this skill) | 1 | 27d | Caution | Intermediate |
| mcp-builder | 136 | 3mo | Review | Advanced |
| copilot-sdk | 7 | 4mo | Review | Intermediate |
| openrouter-function-calling | 5 | 27d | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
More by jeremylongshore
View all by jeremylongshore →You might also like
mcp-builder
anthropics
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
copilot-sdk
github
Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.
openrouter-function-calling
jeremylongshore
Implement function/tool calling with OpenRouter models. Use when building agents or structured outputs. Trigger with phrases like 'openrouter functions', 'openrouter tools', 'openrouter agent', 'function calling'.
agentica-server
parcadei
Agentica server + Claude proxy setup - architecture, startup sequence, debugging
mcp-developer
Jeffallan
Use when building MCP servers or clients that connect AI systems with external tools and data sources. Invoke for MCP protocol compliance, TypeScript/Python SDKs, resource providers, tool functions.
create-mcp-servers
glittercowboy
Create Model Context Protocol (MCP) servers that expose tools, resources, and prompts to Claude. Use when building custom integrations, APIs, data sources, or any server that Claude should interact with via the MCP protocol. Supports both TypeScript and Python implementations.