klingai-text-to-video

v2026.09.24

Generate videos from text prompts with Kling AI. Use when creating videos from descriptions, learning prompt techniques, or building T2V pipelines. Trigger with phrases like 'kling ai text to video', 'klingai prompt', 'generate video from text', 'text2video kling'.

GitHub
安装命令
npx skhub add jeremylongshore/klingai-text-to-video
Markdown
SKILL.md

Kling AI Text-to-Video

Overview

Generate videos from text prompts using the /v1/videos/text2video endpoint. Supports models v1 through v2.6, standard/professional modes, camera control, negative prompts, and native audio (v2.6+).

Endpoint: POST https://api.klingai.com/v1/videos/text2video

Request Parameters

ParameterTypeRequiredDescription
model_namestringYesModel version (see model catalog)
promptstringYesVideo description, max 2500 chars
negative_promptstringNoWhat to exclude from generation
durationstringYes"5" or "10" seconds
aspect_ratiostringNo"16:9" (default), "9:16", "1:1", etc.
modestringNo"standard" (default) or "professional"
cfg_scalefloatNoPrompt adherence (0.0-1.0, default 0.5)
camera_controlobjectNoCamera movement config
callback_urlstringNoWebhook URL for completion notification

Complete Example — Python

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"}

# Create text-to-video task
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "Aerial drone shot of a coral reef at golden hour, "
              "tropical fish swimming through crystal clear water, "
              "sun rays penetrating the surface, cinematic 4K",
    "negative_prompt": "blurry, low quality, distorted, watermark",
    "duration": "5",
    "aspect_ratio": "16:9",
    "mode": "professional",
    "cfg_scale": 0.5,
})

task = response.json()
task_id = task["data"]["task_id"]

# Poll for completion
while True:
    time.sleep(15)
    result = requests.get(
        f"{BASE}/videos/text2video/{task_id}", headers=get_headers()
    ).json()

    status = result["data"]["task_status"]
    if status == "succeed":
        video = result["data"]["task_result"]["videos"][0]
        print(f"Video URL: {video['url']}")
        print(f"Duration: {video['duration']}s")
        break
    elif status == "failed":
        raise RuntimeError(result["data"]["task_status_msg"])
    # else: submitted/processing — keep polling

With Camera Control

# Camera movement types: pan, tilt, zoom, roll
response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "A medieval castle on a cliff at sunrise, fog in the valley",
    "duration": "5",
    "mode": "standard",
    "camera_control": {
        "type": "simple",
        "config": {
            "horizontal": 5,    # pan right (negative = left), range -10 to 10
            "vertical": 0,      # tilt (negative = down, positive = up)
            "zoom": 3,          # zoom in (positive) or out (negative)
            "roll": 0,          # rotation
            "pan": 0,           # dolly left/right
            "tilt": -2,         # dolly up/down
        }
    },
})

Rule: Only one non-zero field in config for type: "simple".

With Native Audio (v2.6 only)

response = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
    "model_name": "kling-v2-6",
    "prompt": "A jazz band performing in a dimly lit club, saxophone solo, "
              "audience clapping, warm amber lighting",
    "duration": "10",
    "mode": "professional",
    "motion_has_audio": True,  # generates synchronized audio
})

Prompt Engineering Tips

TechniqueExample
Scene + action + style"A samurai walking through cherry blossoms, cinematic slow motion"
Lighting cues"golden hour", "neon-lit", "overcast diffused light"
Camera language"close-up", "wide establishing shot", "tracking shot"
Negative prompt"blurry, watermark, text overlay, distorted faces"
Material/texture"brushed steel", "hand-painted watercolor", "photorealistic"

Cost Reference

DurationStandardProfessional
5 seconds10 credits35 credits
10 seconds20 credits70 credits

Error Handling

ErrorCauseFix
400 invalid promptEmpty or >2500 charsCheck prompt length
400 invalid modelUnsupported model_nameUse valid model ID from catalog
402 insufficient creditsNot enough creditsTop up account
task_status: failedContent policy violation or complexitySimplify prompt, remove restricted content

Prerequisites

  • An approved brief, rights-cleared or synthetic reference material, an authorized workspace and budget, a content-policy review, and a named owner for publication and removal.

Instructions

  1. Create a sandbox draft from an approved brief; do not include private individuals, protected material, or unverified claims in prompts or uploads.
  2. Verify the requested duration, style, destination, credit budget, content-policy status, and draft-only setting before submission.
  3. Review one watermarked canary render for policy, rights, and quality; halt on a policy or attribution concern and delete the draft rather than publishing it.
  4. Promote only after owner approval and retain a redacted production receipt; remove temporary assets at the approved retention boundary.

Output

Produce a render receipt with brief ID, approved source classification, model/mode, duration, credit estimate, policy and rights-review outcome, draft destination, approver, retention/removal reference, and task ID. Exclude prompt text, identities, and credentials.

Examples

brief=synthetic-product-demo; source=rights-cleared; mode=standard; duration=5s; policy=pass; destination=draft-only; approval=pending; cleanup=24h is a safe canary request.

Resources

发现
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版本
最新版本元数据

版本

v2026.09.24

发布时间

Sep 24, 2026

分类

未分类

许可证

MIT

源路径

skills/.curated/klingai-text-to-video

默认分支

main

最新提交

e5a6c3b

Tree SHA

c2dc8e8