--- name: klingai-text-to-video description: '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''. ' allowed-tools: Read, Write, Edit, Bash(npm:*), Grep version: 1.18.0 license: MIT author: Jeremy Longshore tags: - saas - kling-ai - text-to-video - video-generation compatibility: Designed for Claude Code --- # 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 | Parameter | Type | Required | Description | |-----------|------|----------|-------------| | `model_name` | string | Yes | Model version (see model catalog) | | `prompt` | string | Yes | Video description, max 2500 chars | | `negative_prompt` | string | No | What to exclude from generation | | `duration` | string | Yes | `"5"` or `"10"` seconds | | `aspect_ratio` | string | No | `"16:9"` (default), `"9:16"`, `"1:1"`, etc. | | `mode` | string | No | `"standard"` (default) or `"professional"` | | `cfg_scale` | float | No | Prompt adherence (0.0-1.0, default 0.5) | | `camera_control` | object | No | Camera movement config | | `callback_url` | string | No | Webhook URL for completion notification | ## Complete Example — Python ```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 ```python # 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) ```python 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 | Technique | Example | |-----------|---------| | 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 | Duration | Standard | Professional | |----------|----------|-------------| | 5 seconds | 10 credits | 35 credits | | 10 seconds | 20 credits | 70 credits | ## Error Handling | Error | Cause | Fix | |-------|-------|-----| | `400` invalid prompt | Empty or >2500 chars | Check prompt length | | `400` invalid model | Unsupported `model_name` | Use valid model ID from catalog | | `402` insufficient credits | Not enough credits | Top up account | | `task_status: failed` | Content policy violation or complexity | Simplify 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 - [Text-to-Video API](https://app.klingai.com/global/dev/document-api/apiReference/model/textToVideo) - [Camera Control Guide](https://app.klingai.com/global/quickstart/ai-camera-control-guide) - [Content Guidelines](https://app.klingai.com/global/dev/document-api/protocols/paidServiceProtocol) --- Source attribution: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/klingai-pack/skills/klingai-text-to-video/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Intent Solutions Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: jeremylongshore Original project: https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/ab85a061f7bcc039dac13ee193de7a4cd4388571/plugins/saas-packs/klingai-pack/skills/klingai-text-to-video/SKILL.md Attribution basis: Source repository record Fields of work: Content & Media, Marketing Roles: Content Marketer, Influencer Marketer, Video Producer Specialties: API, Backend, Design, Word Documents, Frontend, Remotion Tools: Kling Ai Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: c443b3b7a97c465e5122971d7fee93088ebcb6a9e61b942ba4cbe18f8dd63286