Atlas / Skills / jeremylongshore / Klingai Sdk Patterns

Klingai Sdk PatternsSAFE

skills/jeremylongshore/klingai-sdk-patterns

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
1.18.0
Hosts
1 documented
License
MIT
Stars
2,824
01

Overview

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Read from source at commit 4f83675ca38aOBSERVED · 2026-10-09
02

Host compatibility

What the documentation claims. We have not run a compatibility test.

HostStatusNotes
claude-codementioned
03

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: klingai-sdk-patterns
description: 'Production SDK patterns for Kling AI: client wrapper, retry logic, async
  polling, and error

  handling. Use when building robust integrations. Trigger with phrases like ''klingai
  sdk'',

  ''kling ai client'', ''klingai patterns'', ''kling ai wrapper''.

  '
allowed-tools: Read, Write, Edit, Bash(npm:*), Grep
version: 1.18.0
license: MIT
author: Jeremy Longshore <[email protected]>
tags:
- saas
- kling-ai
- sdk
- patterns
compatibility: Designed for Claude Code
---
# 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

```python
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

```python
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
04

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codePASS
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-09 · audit v0.4.1 · source sha 4f83675ca38afull audit observations/trust-audit/skill/jeremylongshore__klingai-sdk-patterns.json · Report an issue / request a re-scan
05

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-094f83675ca38aSAFEB89first audit
06

Questions

What does the Klingai Sdk Patterns skill do?

Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.

Is Klingai Sdk Patterns safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Klingai Sdk Patterns access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

Which assistants does Klingai Sdk Patterns work with?

Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.

How current is this page?

The grade is for one exact copy of the source (4f83675ca38a), read on 2026-10-09. The repository is watched, and a new audit runs when it changes — this is the first audit.

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