본문으로 건너뛰기
cccskills
무료GitHub에 공개

fal-ai-media

Create images, videos, and audio with fal.ai through MCP, a connection to external tools. Find suitable models, estimate costs, edit source images, generate speech, and create sounds that match video content.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

연동 도구ElevenLabs
설치 방법 보기

이럴 때 유용합니다

  • Creating thumbnails or product images from text
  • Changing an existing image's style
  • Turning a still image into a video
  • Generating narration from text
  • Creating sounds that match a video

포함된 파일(1)

  • SKILL.md7.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

fal.ai Media Generation

Drift-prone skill. fal.ai model IDs, pricing, inputs, and MCP tool names change quickly. Search or fetch the current model metadata before promising a specific model, parameter, output format, or cost.

Generate images, videos, and audio using fal.ai models via MCP.

When to Activate

  • User wants to generate images from text prompts
  • Creating videos from text or images
  • Generating speech, music, or sound effects
  • Any media generation task
  • User says "generate image", "create video", "text to speech", "make a thumbnail", or similar

MCP Requirement

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.

MCP Tools

The fal.ai MCP provides these tools:

  • search — Find available models by keyword
  • find — Get model details and parameters
  • generate — Run a model with parameters
  • result — Check async generation status
  • status — Check job status
  • cancel — Cancel a running job
  • estimate_cost — Estimate generation cost
  • models — List popular models
  • upload — Upload files for use as inputs

Image Generation

Nano Banana 2 (Fast)

Best for: quick iterations, drafts, text-to-image, image editing.

generate(
  app_id: "fal-ai/nano-banana-2",
  input_data: {
    "prompt": "a futuristic cityscape at sunset, cyberpunk style",
    "image_size": "landscape_16_9",
    "num_images": 1,
    "seed": 42
  }
)

Nano Banana Pro (High Fidelity)

Best for: production images, realism, typography, detailed prompts.

generate(
  app_id: "fal-ai/nano-banana-pro",
  input_data: {
    "prompt": "professional product photo of wireless headphones on marble surface, studio lighting",
    "image_size": "square",
    "num_images": 1,
    "guidance_scale": 7.5
  }
)

Common Image Parameters

ParamTypeOptionsNotes
promptstringrequiredDescribe what you want
image_sizestringsquare, portrait_4_3, landscape_16_9, portrait_16_9, landscape_4_3Aspect ratio
num_imagesnumber1-4How many to generate
seednumberany integerReproducibility
guidance_scalenumber1-20How closely to follow the prompt (higher = more literal)

Image Editing

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(
  app_id: "fal-ai/nano-banana-2",
  input_data: {
    "prompt": "same scene but in watercolor style",
    "image_url": "<uploaded_url>",
    "image_size": "landscape_16_9"
  }
)

Video Generation

Seedance 1.0 Pro (ByteDance)

Best for: text-to-video, image-to-video with high motion quality.

generate(
  app_id: "fal-ai/seedance-1-0-pro",
  input_data: {
    "prompt": "a drone flyover of a mountain lake at golden hour, cinematic",
    "duration": "5s",
    "aspect_ratio": "16:9",
    "seed": 42
  }
)

Kling Video v3 Pro

Best for: text/image-to-video with native audio generation.

generate(
  app_id: "fal-ai/kling-video/v3/pro",
  input_data: {
    "prompt": "ocean waves crashing on a rocky coast, dramatic clouds",
    "duration": "5s",
    "aspect_ratio": "16:9"
  }
)

Veo 3 (Google DeepMind)

Best for: video with generated sound, high visual quality.

generate(
  app_id: "fal-ai/veo-3",
  input_data: {
    "prompt": "a bustling Tokyo street market at night, neon signs, crowd noise",
    "aspect_ratio": "16:9"
  }
)

Image-to-Video

Start from an existing image:

generate(
  app_id: "fal-ai/seedance-1-0-pro",
  input_data: {
    "prompt": "camera slowly zooms out, gentle wind moves the trees",
    "image_url": "<uploaded_image_url>",
    "duration": "5s"
  }
)

Video Parameters

ParamTypeOptionsNotes
promptstringrequiredDescribe the video
durationstring"5s", "10s"Video length
aspect_ratiostring"16:9", "9:16", "1:1"Frame ratio
seednumberany integerReproducibility
image_urlstringURLSource image for image-to-video

Audio Generation

CSM-1B (Conversational Speech)

Text-to-speech with natural, conversational quality.

generate(
  app_id: "fal-ai/csm-1b",
  input_data: {
    "text": "Hello, welcome to the demo. Let me show you how this works.",
    "speaker_id": 0
  }
)

ThinkSound (Video-to-Audio)

Generate matching audio from video content.

generate(
  app_id: "fal-ai/thinksound",
  input_data: {
    "video_url": "<video_url>",
    "prompt": "ambient forest sounds with birds chirping"
  }
)

ElevenLabs (via API, no MCP)

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)

VideoDB Generative Audio

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")

Cost Estimation

Before generating, check estimated cost:

estimate_cost(
  estimate_type: "unit_price",
  endpoints: {
    "fal-ai/nano-banana-pro": {
      "unit_quantity": 1
    }
  }
)

Model Discovery

Find models for specific tasks:

search(query: "text to video")
find(endpoint_ids: ["fal-ai/seedance-1-0-pro"])
models()

Tips

  • Use seed for reproducible results when iterating on prompts
  • Start with lower-cost models (Nano Banana 2) for prompt iteration, then switch to Pro for finals
  • For video, keep prompts descriptive but concise — focus on motion and scene
  • Image-to-video produces more controlled results than pure text-to-video
  • Check estimate_cost before running expensive video generations

Related Skills

  • tasteforge-video — Offline taste distillation and modality planning. Its endpoint candidates and request manifests are reference-only, not submitted jobs or saved Fal workflows. A TasteForge handoff does not authorize upload or generation; use a separately authorized provider workflow and verify its current endpoint schema before executing.
  • videodb — Video processing, editing, and streaming
  • video-editing — AI-powered video editing workflows
  • content-engine — Content creation for social platforms

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Design and audit interfaces for Web, iOS, and Android using WCAG 2.2 accessibility guidance. Check screen-reader labels, keyboard navigation, contrast, and control sizes to identify barriers and guide implementation for people using assistive technology.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Adding descriptive labels to icon-only buttons
  • Reviewing keyboard navigation and modal focus
  • Checking contrast and control sizes
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Investigate unreliable AI agents across 12 layers, including instructions, memory, tool execution, and display. Use code and logs to trace failures, rank findings by severity, and produce an ordered repair plan that prioritizes code changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Investigating failures that appear only inside an agent application
  • Tracing old conversation content leaking into new sessions
  • Checking skipped or falsely claimed tool execution
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Comparing coding agents on real development tasks
  • Evaluating a new tool or model before adoption
  • Checking performance after agent updates
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Design and refine an AI agent’s available actions, tool inputs, and result formats. Define recovery steps for errors, manage the information kept in context, and evaluate changes through completion rates, retries, and cost per successful task.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Define an AI agent’s tool set and input formats
  • Make tool results and next steps easier to interpret
  • Specify safe retries and stopping conditions
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Diagnose failed AI agent runs by capturing errors, checking likely causes, and choosing small, reversible recovery actions. The workflow produces a structured report of the failure, recovery steps, and evidence, helping explain repeated retries or stalled progress.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Diagnosing repeated tool calls with no progress
  • Investigating quality loss as context grows
  • Checking file paths and environment mismatches
affaan-m/ECC28.3만2026년 10월 16일 업데이트

Diagnose AI agent runs that fail repeatedly, loop through tools, or lose track of the task. Capture errors and environment details, test a focused recovery action, and produce a report explaining the evidence and outcome.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Diagnosing repeated failures in an agent run
  • Investigating loops through the same tool calls
  • Reviewing output degradation as context grows
affaan-m/ECC28.3만2026년 10월 5일 업데이트

affaan-m의 스킬 모두 보기

이 스킬의 문제 신고