plugins/fal-ai-master/skills/fal-text-to-video/SKILL.md
Complete fal.ai text-to-video system. PROACTIVELY activate for: (1) Kling 2.0/2.5/2.6 Pro video generation, (2) Sora 2 for creative videos, (3) LTX Video with audio, (4) Runway Gen-3 Turbo for fast iteration, (5) Luma Dream Machine, (6) Video duration and aspect ratio, (7) Motion prompt engineering, (8) Camera movement keywords. Provides: Model endpoints, quality tiers, prompt structure, duration options. Ensures cinematic video generation with proper motion description.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace fal-text-to-videoInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
| Model | Endpoint | Duration | Audio | Speed |
|-------|----------|----------|-------|-------|
| Kling 2.6 Pro | fal-ai/kling-video/v2.6/pro/text-to-video | 5-10s | Yes | Slow |
| Sora 2 | fal-ai/sora | 5-20s | Optional | Slow |
| LTX-2 Pro | fal-ai/ltx-2-pro | 5s | Yes | Medium |
| Runway Turbo | fal-ai/runway-gen3/turbo/text-to-video | 5-10s | No | Fast |
| Luma | fal-ai/luma-dream-machine | 5s | No | Medium |
| Aspect Ratio | Value | Use Case |
|--------------|-------|----------|
| Landscape | "16:9" | Standard video |
| Portrait | "9:16" | Social/mobile |
| Square | "1:1" | Social posts |
| Prompt Structure | Example | |------------------|---------| | Subject + Action | "A woman walks" | | + Setting | "through a forest" | | + Style | "cinematic lighting" | | + Camera | "tracking shot" |
Use for text-to-video generation:
Related skills:
fal-image-to-videofal-video-to-videofal-model-guideComplete reference for all text-to-video generation models on fal.ai.
Endpoint: fal-ai/kling-video/v2.0/text-to-video
Best For: Good quality, cost-effective
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/kling-video/v2.0/text-to-video", {
input: {
prompt: "A majestic eagle soaring over snow-capped mountains at golden hour, cinematic lighting",
duration: "5", // "5" or "10" seconds
aspect_ratio: "16:9",
negative_prompt: "blurry, distorted, low quality"
}
});
console.log(result.video.url);
import fal_client
result = fal_client.subscribe(
"fal-ai/kling-video/v2.0/text-to-video",
arguments={
"prompt": "A majestic eagle soaring over mountains",
"duration": "5",
"aspect_ratio": "16:9",
"negative_prompt": "blurry, distorted"
}
)
print(result["video"]["url"])
Endpoint: fal-ai/kling-video/v2.5/pro/text-to-video
Best For: Professional quality videos
const result = await fal.subscribe("fal-ai/kling-video/v2.5/pro/text-to-video", {
input: {
prompt: "A luxury car driving through a mountain road, drone shot, cinematic",
duration: "5",
aspect_ratio: "16:9",
negative_prompt: "shaky, amateur, blurry"
}
});
Endpoint: fal-ai/kling-video/v2.6/pro/text-to-video
Best For: Highest quality with native audio generation
The latest Kling model with built-in audio generation.
const result = await fal.subscribe("fal-ai/kling-video/v2.6/pro/text-to-video", {
input: {
prompt: "Ocean waves crashing on rocky cliffs at sunset, seagulls flying, ambient sounds",
duration: "5",
aspect_ratio: "16:9",
negative_prompt: "static, boring, low quality"
}
});
console.log(result.video.url);
console.log(result.audio?.url); // Native audio included
Kling Parameters:
| Parameter | Type | Values | Description |
|-----------|------|--------|-------------|
| prompt | string | - | Video description |
| duration | string | "5", "10" | Duration in seconds |
| aspect_ratio | string | "16:9", "9:16", "1:1" | Video dimensions |
| negative_prompt | string | - | What to avoid |
Endpoint: fal-ai/sora
Best For: Advanced, creative video generation
const result = await fal.subscribe("fal-ai/sora", {
input: {
prompt: "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots. She carries a black purse. She wears sunglasses and red lipstick. She walks confidently. The street is damp and reflective, creating a mirror effect of the colorful lights.",
duration: 10,
aspect_ratio: "16:9",
resolution: "1080p"
}
});
console.log(result.video.url);
Sora Parameters:
| Parameter | Type | Values | Description |
|-----------|------|--------|-------------|
| prompt | string | - | Detailed video description |
| duration | number | 5-20 | Duration in seconds |
| aspect_ratio | string | "16:9", "9:16", "1:1" | Video dimensions |
| resolution | string | "720p", "1080p" | Output resolution |
Endpoint: fal-ai/ltx-video
Best For: Fast, efficient generation
const result = await fal.subscribe("fal-ai/ltx-video", {
input: {
prompt: "A beautiful sunset over the ocean with gentle waves",
negative_prompt: "low quality, blurry, distorted",
num_inference_steps: 30,
guidance_scale: 7.5,
aspect_ratio: "16:9"
}
});
Endpoint: fal-ai/ltx-video/v2
Best For: Improved quality over v1
const result = await fal.subscribe("fal-ai/ltx-video/v2", {
input: {
prompt: "A futuristic city with flying cars and neon lights",
negative_prompt: "ugly, distorted",
num_inference_steps: 35,
guidance_scale: 7.0,
resolution: "720p"
}
});
Endpoint: fal-ai/ltx-2-pro
Best For: High fidelity with audio support
const result = await fal.subscribe("fal-ai/ltx-2-pro", {
input: {
prompt: "A jazz band performing in a dimly lit club, smooth music, intimate atmosphere",
negative_prompt: "worst quality, inconsistent motion, blurry",
num_inference_steps: 30,
guidance_scale: 3.5,
resolution: "720p",
enable_audio: true // Generate matching audio
}
});
console.log(result.video.url);
console.log(result.audio?.url); // Audio track if enabled
LTX Parameters:
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| prompt | string | - | Video description |
| negative_prompt | string | - | What to avoid |
| num_inference_steps | number | 30 | Quality steps |
| guidance_scale | number | 7.5 | Prompt adherence |
| resolution | string | "720p" | "720p" or "480p" |
| aspect_ratio | string | "16:9" | Video dimensions |
| enable_audio | boolean | false | Generate audio (LTX-2 Pro) |
Endpoint: fal-ai/wan/v2.1/1.3b/text-to-video
Best For: Lightweight, fast generation
const result = await fal.subscribe("fal-ai/wan/v2.1/1.3b/text-to-video", {
input: {
prompt: "A cat playing with a ball of yarn, cute, playful",
num_frames: 81, // Number of frames
resolution: "480p"
}
});
Endpoint: fal-ai/wan/v2.1/14b/text-to-video
Best For: Higher quality, larger model
const result = await fal.subscribe("fal-ai/wan/v2.1/14b/text-to-video", {
input: {
prompt: "A professional chef preparing sushi, precise movements, high-end restaurant",
num_frames: 81,
resolution: "720p"
}
});
Endpoint: fal-ai/minimax-video/text-to-video
Best For: Balanced quality and speed
const result = await fal.subscribe("fal-ai/minimax-video/text-to-video", {
input: {
prompt: "A butterfly emerging from a cocoon, macro shot, nature documentary style",
prompt_optimizer: true // Enhance prompt automatically
}
});
MiniMax Parameters:
| Parameter | Type | Description |
|-----------|------|-------------|
| prompt | string | Video description |
| prompt_optimizer | boolean | Auto-enhance prompt |
Endpoint: fal-ai/runway-gen3/turbo/text-to-video
Best For: Fast iteration, previews
const result = await fal.subscribe("fal-ai/runway-gen3/turbo/text-to-video", {
input: {
prompt: "A dancer performing ballet in an empty theater, spotlight, graceful movements",
duration: 5,
ratio: "16:9"
}
});
Runway Parameters:
| Parameter | Type | Values | Description |
|-----------|------|--------|-------------|
| prompt | string | - | Video description |
| duration | number | 5, 10 | Seconds |
| ratio | string | "16:9", "9:16", "1:1" | Aspect ratio |
Endpoint: fal-ai/luma-dream-machine
Best For: Creative, artistic videos
const result = await fal.subscribe("fal-ai/luma-dream-machine", {
input: {
prompt: "A magical forest with glowing mushrooms and fireflies at night",
aspect_ratio: "16:9",
loop: false
}
});
Luma Parameters:
| Parameter | Type | Description |
|-----------|------|-------------|
| prompt | string | Video description |
| aspect_ratio | string | "16:9", "9:16", "1:1" |
| loop | boolean | Create looping video |
Endpoint: fal-ai/cogvideox
Best For: Open-source alternative
const result = await fal.subscribe("fal-ai/cogvideox", {
input: {
prompt: "A timelapse of a flower blooming, from bud to full bloom",
num_inference_steps: 50,
guidance_scale: 6.0
}
});
Endpoint: fal-ai/cogvideox-5b
Best For: Higher quality open-source
const result = await fal.subscribe("fal-ai/cogvideox-5b", {
input: {
prompt: "An astronaut floating in space with Earth in the background",
num_inference_steps: 50,
guidance_scale: 6.0,
num_frames: 49
}
});
Endpoint: fal-ai/hunyuan-video
Best For: Chinese model, good quality
const result = await fal.subscribe("fal-ai/hunyuan-video", {
input: {
prompt: "A traditional Chinese dragon dance during festival celebration",
num_inference_steps: 50,
guidance_scale: 6.0
}
});
| Tier | Models | Quality | Speed | Cost | |------|--------|---------|-------|------| | Premium | Kling 2.6 Pro, Sora 2 | Highest | Slow | $$$ | | Professional | Kling 2.5 Pro, LTX-2 Pro | High | Medium | $$ | | Standard | Kling 2.0, Runway Turbo | Good | Fast | $ | | Budget | LTX v1, CogVideoX | Acceptable | Fast | $ |
| Model | Audio | Duration | Resolution | Best Use | |-------|-------|----------|------------|----------| | Kling 2.6 Pro | Native | 5-10s | 1080p | Cinematic | | Sora 2 | Optional | 5-20s | 1080p | Creative | | LTX-2 Pro | Yes | 5s | 720p | Fast HQ | | Runway Turbo | No | 5-10s | 720p | Iteration | | MiniMax | No | 6s | 720p | Balanced | | Luma | No | 5s | 720p | Artistic |
| Model | ~Generation Time (5s video) | |-------|-----------------------------| | Runway Gen-3 Turbo | 30-60s | | LTX Video | 45-90s | | MiniMax | 60-120s | | Kling 2.5 Pro | 120-180s | | Kling 2.6 Pro | 150-240s | | Sora 2 | 180-300s |
[Subject] + [Action] + [Setting] + [Style] + [Camera/Technical]
Examples:
// Good: Specific, action-oriented
"A golden retriever running through autumn leaves in a forest, slow motion, cinematic, warm lighting, tracking shot"
// Bad: Vague, static
"A dog in a forest"
| Keyword | Effect | |---------|--------| | "walking", "running", "jumping" | Subject movement | | "slow motion", "timelapse" | Speed modification | | "tracking shot", "pan", "zoom" | Camera movement | | "floating", "flying", "falling" | Spatial movement | | "dancing", "spinning", "waving" | Rhythmic movement |
| Keyword | Effect | |---------|--------| | "cinematic" | Movie-like quality | | "documentary" | Realistic style | | "anime" | Animated style | | "noir" | Dark, contrasty | | "vibrant" | Saturated colors |
| Term | Description | |------|-------------| | "tracking shot" | Camera follows subject | | "pan left/right" | Horizontal camera sweep | | "tilt up/down" | Vertical camera sweep | | "zoom in/out" | Focal length change | | "drone shot" | Aerial perspective | | "POV shot" | First-person view | | "dolly shot" | Camera moves on track |
interface TextToVideoInput {
// Required
prompt: string;
// Duration (varies by model)
duration?: number | string; // seconds
// Dimensions
aspect_ratio?: "16:9" | "9:16" | "1:1" | "4:3" | "21:9";
resolution?: "480p" | "720p" | "1080p";
// Quality control
negative_prompt?: string;
num_inference_steps?: number;
guidance_scale?: number;
// Reproducibility
seed?: number;
// Model-specific
prompt_optimizer?: boolean; // MiniMax
enable_audio?: boolean; // LTX-2 Pro, Kling 2.6
loop?: boolean; // Luma
num_frames?: number; // Wan, CogVideoX
}
Production workflow and batch-generation examples for fal.ai text-to-video models, including fast preview iteration with Runway Turbo and final rendering with Kling Pro, live in references/workflow-examples.md. Load that reference when turning model-selection guidance into executable generation code.
subscribe() not run() - Video generation takes timedevelopment
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.