.cursor/.agents/skills/fal-ai-media/SKILL.md
Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.
npx skillsauth add LUAgam/stage-harness fal-ai-mediaInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Generate images, videos, and audio using fal.ai models via MCP.
fal.ai MCP server must be configured. Add to ~/.claude.json:
"fal-ai": {
"command": "npx",
"args": ["-y", "fal-ai-mcp-server"],
"env": { "FAL_KEY": "YOUR_FAL_KEY_HERE" }
}
Get an API key at fal.ai.
The fal.ai MCP provides these tools:
search — Find available models by keywordfind — Get model details and parametersgenerate — Run a model with parametersresult — Check async generation statusstatus — Check job statuscancel — Cancel a running jobestimate_cost — Estimate generation costmodels — List popular modelsupload — Upload files for use as inputsBest for: quick iterations, drafts, text-to-image, image editing.
generate(
model_name: "fal-ai/nano-banana-2",
input: {
"prompt": "a futuristic cityscape at sunset, cyberpunk style",
"image_size": "landscape_16_9",
"num_images": 1,
"seed": 42
}
)
Best for: production images, realism, typography, detailed prompts.
generate(
model_name: "fal-ai/nano-banana-pro",
input: {
"prompt": "professional product photo of wireless headphones on marble surface, studio lighting",
"image_size": "square",
"num_images": 1,
"guidance_scale": 7.5
}
)
| Param | Type | Options | Notes |
|-------|------|---------|-------|
| prompt | string | required | Describe what you want |
| image_size | string | square, portrait_4_3, landscape_16_9, portrait_16_9, landscape_4_3 | Aspect ratio |
| num_images | number | 1-4 | How many to generate |
| seed | number | any integer | Reproducibility |
| guidance_scale | number | 1-20 | How closely to follow the prompt (higher = more literal) |
Use Nano Banana 2 with an input image for inpainting, outpainting, or style transfer:
# First upload the source image
upload(file_path: "/path/to/image.png")
# Then generate with image input
generate(
model_name: "fal-ai/nano-banana-2",
input: {
"prompt": "same scene but in watercolor style",
"image_url": "<uploaded_url>",
"image_size": "landscape_16_9"
}
)
Best for: text-to-video, image-to-video with high motion quality.
generate(
model_name: "fal-ai/seedance-1-0-pro",
input: {
"prompt": "a drone flyover of a mountain lake at golden hour, cinematic",
"duration": "5s",
"aspect_ratio": "16:9",
"seed": 42
}
)
Best for: text/image-to-video with native audio generation.
generate(
model_name: "fal-ai/kling-video/v3/pro",
input: {
"prompt": "ocean waves crashing on a rocky coast, dramatic clouds",
"duration": "5s",
"aspect_ratio": "16:9"
}
)
Best for: video with generated sound, high visual quality.
generate(
model_name: "fal-ai/veo-3",
input: {
"prompt": "a bustling Tokyo street market at night, neon signs, crowd noise",
"aspect_ratio": "16:9"
}
)
Start from an existing image:
generate(
model_name: "fal-ai/seedance-1-0-pro",
input: {
"prompt": "camera slowly zooms out, gentle wind moves the trees",
"image_url": "<uploaded_image_url>",
"duration": "5s"
}
)
| Param | Type | Options | Notes |
|-------|------|---------|-------|
| prompt | string | required | Describe the video |
| duration | string | "5s", "10s" | Video length |
| aspect_ratio | string | "16:9", "9:16", "1:1" | Frame ratio |
| seed | number | any integer | Reproducibility |
| image_url | string | URL | Source image for image-to-video |
Text-to-speech with natural, conversational quality.
generate(
model_name: "fal-ai/csm-1b",
input: {
"text": "Hello, welcome to the demo. Let me show you how this works.",
"speaker_id": 0
}
)
Generate matching audio from video content.
generate(
model_name: "fal-ai/thinksound",
input: {
"video_url": "<video_url>",
"prompt": "ambient forest sounds with birds chirping"
}
)
For professional voice synthesis, use ElevenLabs directly:
import os
import requests
resp = requests.post(
"https://api.elevenlabs.io/v1/text-to-speech/<voice_id>",
headers={
"xi-api-key": os.environ["ELEVENLABS_API_KEY"],
"Content-Type": "application/json"
},
json={
"text": "Your text here",
"model_id": "eleven_turbo_v2_5",
"voice_settings": {"stability": 0.5, "similarity_boost": 0.75}
}
)
with open("output.mp3", "wb") as f:
f.write(resp.content)
If VideoDB is configured, use its generative audio:
# Voice generation
audio = coll.generate_voice(text="Your narration here", voice="alloy")
# Music generation
music = coll.generate_music(prompt="upbeat electronic background music", duration=30)
# Sound effects
sfx = coll.generate_sound_effect(prompt="thunder crack followed by rain")
Before generating, check estimated cost:
estimate_cost(model_name: "fal-ai/nano-banana-pro", input: {...})
Find models for specific tasks:
search(query: "text to video")
find(model_name: "fal-ai/seedance-1-0-pro")
models()
seed for reproducible results when iterating on promptsestimate_cost before running expensive video generationsvideodb — Video processing, editing, and streamingvideo-editing — AI-powered video editing workflowscontent-engine — Content creation for social platformsdevelopment
在 generate-test-cases 阶段之后执行,逐个验证测试用例并在失败时修复项目代码、重新编译部署、再次验证, 直到通过或达到最大修复次数。覆盖 UI / API / API+UI / 性能测试四个维度,UI 测试通过浏览器真实模拟用户操作并截图, API 测试根据项目代码生成可执行的接口脚本,性能测试调用现有性能/质量技能全量执行。 涉及真实用户登录信息(如手机号+验证码、账号密码、JWT)时必须中断要求用户提供,禁止编造无效凭证。 所有 case 状态变更必须通过 e2e-case-tracker.sh 脚本持久化,确保中途崩溃可恢复、无 case 遗漏。
development
# SKILL: e2e > **核心原则**: > 1. 测试范围跟着本次变动走。后端接口改了,对应的前端流程必须做联调验证;与本次需求无关的功能不测。对于涉及算法、转换准确率等质量敏感型需求,需额外生成专项质量测试。 > 2. **覆盖完整性优先于执行便利性**。不得以"链路复杂"、"需要外部依赖"为由跳过本次变动相关的用例;凡是受变动影响的接口和 UI 流程,都必须生成真实调用/操作用例。 > 3. **UI 测试必须模拟真实用户操作**(定位元素、点击、键入、等待渲染、断言可见文本/状态)。**禁止**将 UI 套件退化为浏览器上下文里的 `page.evaluate(fetch(...))` API 验证——那只是把 API 测试换了执行环境,没有额外价值,不算 UI 测试。 > 4. **通用性**:本 skill 不假设具体业务域,所有规则均以抽象变动面(文件、接口、页面、用户动作)为单位组织,不针对任何特定项目的数据库/领域词汇。 > 5. **E2E 套件必须验证运行时行为**。严禁把"读取源码/配置文件并做字符串/结构匹配"的检查封装成独立 E2E 套件——这类检
tools
# SKILL: deploy ## CLI Bootstrap 在执行任何 `harnessctl` 命令前,先解析本地 CLI 路径: ```bash if [ -z "${HARNESSCTL:-}" ]; then candidates=( "./stage-harness/scripts/harnessctl" "../stage-harness/scripts/harnessctl" "$(git rev-parse --show-toplevel 2>/dev/null)/stage-harness/scripts/harnessctl" ) for candidate in "${candidates[@]}"; do if [ -n "$candidate" ] && [ -x "$candidate" ]; then HARNESSCTL="$candidate" break fi done fi test -n "${HARNESSCTL:-}" && test -x "$H
tools
# SKILL: build ## CLI Bootstrap 在执行任何 `harnessctl` 命令前,先解析本地 CLI 路径: ```bash if [ -z "${HARNESSCTL:-}" ]; then candidates=( "./stage-harness/scripts/harnessctl" "../stage-harness/scripts/harnessctl" "$(git rev-parse --show-toplevel 2>/dev/null)/stage-harness/scripts/harnessctl" ) for candidate in "${candidates[@]}"; do if [ -n "$candidate" ] && [ -x "$candidate" ]; then HARNESSCTL="$candidate" break fi done fi test -n "${HARNESSCTL:-}" && test -x "$HA