Individual skill
fal
Use when calling a fal.ai endpoint by id to generate image, audio, or video from JS/Python/curl: subscribe vs submit, queue states, ED25519 webhook signature verification, per-call cost, or migrating off @fal-ai/serverless-client. NOT which model or art direction (that is ai-media); NOT the same models on another platform (that is replicate).
skills/fal/SKILL.md
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- Last checked
- Nov 6, 2026
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- 561
- Repository last updated
- Nov 4, 2026
- License
- MIT
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
- cline Repository topic
- codex Repository topic
- coding-assistant Repository topic
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- fal Declared skill tag
- fal-ai Declared skill tag
- inference Declared skill tag
- image-generation Declared skill tag
- video-generation Declared skill tag
- queue Declared skill tag
- webhooks Declared skill tag
- serverless Declared skill tag
- engineering Derived category
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SKILL.mdcore · 8,732 bytesSKILL.mdcore · 8,732 bytesSKILL.mdcore · 8,732 bytesSKILL.mdcore · 8,732 bytesSKILL.mdcore · 8,732 bytesevals/README.mddoc · 1,105 bytesevals/cases.yamlasset · 2,497 bytesreferences/models-and-cost.mddoc · 2,729 bytesreferences/queue-and-webhooks.mddoc · 6,123 bytesscripts/verify.shruns · 3,143 bytes
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# fal
The wire to fal.ai's fast, pre-warmed media endpoints: call a model by id, control the queue, get the file back. fal is the **fast-media path** — latency-optimized image (FLUX, Seedream, SD), audio (TTS, music), and video (Veo, Wan, Kling, Hailuo) endpoints you invoke by id with `FAL_KEY`.
You own the *mechanics*: auth, call mode, queue states, webhook signatures, file I/O, per-call cost.
## When NOT to use
| You want… | Go to |
| --- | --- |
| Which model / what to generate / art direction / multi-provider media pipeline | `ai-media` |
| The same kind of models on **Replicate** (`replicate.run` / predictions) | `replicate` — images-specifically `replicate-images` |
| Renting a **raw GPU pod** you SSH into to train or custom-serve | `runpod` |
| Deploying **your own** Python function as an autoscaling endpoint | `modal` |