PROMPTS BY MODALITY

Video Prompts — 38 curated examples

Prompt examples tuned for Video generation workflows across RunAPI models. Filter by category, copy the prompt, or open the model-specific API code.

CATEGORIES All categories abstract camera_motion cinematic explainer lifestyle macro nature product product_demo social-media timelapse

VI

Video

timelapse kling-3.0

Nature timelapse — clouds rolling over mountain pass

Timelapse of thick cumulus clouds pouring through a narrow mountain pass like a slow-motion waterfall of vapor. The camera is positioned on a ridge looking across the pass at eye level with the cloud tops. Clouds spill over the rocky saddle, cascade down the far slope, and dissipate as they descend into the warmer valley below. The sun moves across the sky creating shifting shadows on the mountain faces. Alpine meadow with wildflowers in the foreground remains in sharp focus while clouds are the primary motion element. Golden hour light transitions through the sequence.

/api/v1/kling/text_to_video

View API Code

curl -X POST https://runapi.ai/api/v1/kling/text_to_video \
  -H "Authorization: Bearer $RUNAPI_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
  "model": "kling-3.0",
  "prompt": "Timelapse of thick cumulus clouds pouring through a narrow mountain pass like a slow-motion waterfall of vapor. The camera is positioned on a ridge looking across the pass at eye level with the cloud tops. Clouds spill over the rocky saddle, cascade down the far slope, and dissipate as they descend into the warmer valley below. The sun moves across the sky creating shifting shadows on the mountain faces. Alpine meadow with wildflowers in the foreground remains in sharp focus while clouds are the primary motion element. Golden hour light transitions through the sequence."
}
JSON

FAQ

Working with Video prompts

What makes a good %{model} prompt?

A useful %{model} prompt names the subject, style, constraints, and output intent clearly. The examples here are short enough to copy, but specific enough for an agent or backend job to preserve the generation goal.

Can I reuse these prompts across models?

Often, yes. Start with a prompt in this modality, then adjust model-specific fields such as aspect ratio, duration, voice settings, or style controls. The detail page shows any saved parameters next to the prompt text.

Where do I find the right model slug?

Every card shows the RunAPI model slug. Open the model page when you want only examples for one model, or follow the model catalog link for pricing and capability details before making a request.

Can agents call these prompts directly?

Yes. After installing the RunAPI MCP Server, paste the agent instruction from a prompt detail page. The page keeps the prompt text, model slug, and endpoint path together so the agent has enough context.