From klingai-pack
Applies artistic styles and visual effects to Kling AI videos via prompt engineering, Effects API, and Kolors. Includes Python API examples and style recipes for cinematic, anime, watercolor.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin klingai-packThis skill is limited to using the following tools:
Apply artistic styles through prompt engineering, use the Effects API for pre-built visual transformations, and leverage Kolors for image-based style references. Available on v1.6+ models.
Creates isolated Git worktrees for feature branches with prioritized directory selection, gitignore safety checks, auto project setup for Node/Python/Rust/Go, and baseline verification.
Executes implementation plans in current session by dispatching fresh subagents per independent task, with two-stage reviews: spec compliance then code quality.
Dispatches parallel agents to independently tackle 2+ tasks like separate test failures or subsystems without shared state or dependencies.
Apply artistic styles through prompt engineering, use the Effects API for pre-built visual transformations, and leverage Kolors for image-based style references. Available on v1.6+ models.
The most direct approach -- include style descriptors in your prompt:
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"}
# Style: Studio Ghibli watercolor
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": "kling-v2-6",
"prompt": "A cozy cottage in a meadow, hand-painted watercolor style, "
"soft pastel colors, Studio Ghibli aesthetic, gentle breeze",
"negative_prompt": "photorealistic, harsh lighting, dark, gritty",
"duration": "5",
"mode": "professional",
"cfg_scale": 0.7, # higher = stricter prompt adherence
})
| Style | Prompt Keywords | cfg_scale |
|---|---|---|
| Cinematic | "cinematic lighting, anamorphic lens, film grain, 35mm" | 0.5-0.6 |
| Anime | "anime style, cel-shaded, vibrant colors, clean lines" | 0.6-0.7 |
| Watercolor | "watercolor painting, soft edges, pastel, hand-painted" | 0.7-0.8 |
| Oil painting | "oil painting, thick brushstrokes, impasto, canvas texture" | 0.7-0.8 |
| Neon/cyberpunk | "neon lights, cyberpunk, rain, dark city, purple and blue" | 0.5-0.6 |
| Vintage film | "vintage 8mm film, warm tones, light leaks, soft focus" | 0.6-0.7 |
| Pixel art | "pixel art style, retro 16-bit, limited palette" | 0.8-0.9 |
| Photorealistic | "photorealistic, 4K, natural lighting, DSLR quality" | 0.4-0.5 |
The Effects API applies pre-built transformations to existing images. Available on v1.6+.
Endpoint: POST https://api.klingai.com/v1/videos/effects
# Apply an effect to an image
response = requests.post(f"{BASE}/videos/effects", headers=get_headers(), json={
"model_name": "kling-v1-6",
"image": "https://example.com/portrait.jpg",
"effect_type": "hug", # effect to apply
"duration": "5",
"mode": "standard",
})
task_id = response.json()["data"]["task_id"]
# Poll for result as usual
| Effect | Description |
|---|---|
hug | Embrace/hug motion between subjects |
kiss | Kiss animation between subjects |
heart | Heart gesture or heart-shaped framing |
expand | Zoom/expand outward effect |
squish | Compression/squish animation |
Use Kolors to restyle images before converting to video:
# Generate styled image with Kolors
image_response = requests.post(f"{BASE}/images/kolors", headers=get_headers(), json={
"prompt": "A cyberpunk city street, neon signs, rain-slicked roads",
"aspect_ratio": "16:9",
"imageCount": 1,
})
# Then use the generated image as input for I2V
image_url = image_response.json()["data"]["images"][0]["url"]
video_response = requests.post(f"{BASE}/videos/image2video", headers=get_headers(), json={
"model_name": "kling-v2-1",
"image": image_url,
"prompt": "Camera slowly pushes forward through the rain, neon reflections",
"duration": "5",
"mode": "professional",
})
The cfg_scale parameter (0.0-1.0) controls how strictly the model follows your prompt:
| cfg_scale | Effect |
|---|---|
| 0.0-0.3 | More creative freedom, may drift from prompt |
| 0.4-0.5 | Balanced (default), natural results |
| 0.6-0.7 | Stronger prompt adherence |
| 0.8-1.0 | Very strict, may reduce quality/naturalness |
For style transfer: Use 0.6-0.8 to ensure the style keywords are respected.
# Use a consistent style template for all clips in a project
STYLE_TEMPLATE = {
"suffix": ", cinematic lighting, 35mm film grain, warm color grading, "
"anamorphic lens flare, shallow depth of field",
"negative": "cartoon, anime, painting, illustration, CGI, digital art",
"cfg_scale": 0.6,
"model": "kling-v2-6",
"mode": "professional",
}
def styled_generation(scene_prompt: str):
return requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": STYLE_TEMPLATE["model"],
"prompt": scene_prompt + STYLE_TEMPLATE["suffix"],
"negative_prompt": STYLE_TEMPLATE["negative"],
"cfg_scale": STYLE_TEMPLATE["cfg_scale"],
"duration": "5",
"mode": STYLE_TEMPLATE["mode"],
})