fal
Search, explore, and run fal.ai generative AI models (image generation, video, audio, 3D). Use when user wants to generate images, videos, or other media with AI models.
Best use case
fal is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search, explore, and run fal.ai generative AI models (image generation, video, audio, 3D). Use when user wants to generate images, videos, or other media with AI models.
Teams using fal should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/fal/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How fal Compares
| Feature / Agent | fal | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Search, explore, and run fal.ai generative AI models (image generation, video, audio, 3D). Use when user wants to generate images, videos, or other media with AI models.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# fal.ai Model API Skill
Run 600+ generative AI models on fal.ai.
## Arguments
- **Command:** `$0` (search | schema | run | status | result | upload)
- **Arg 1:** `$1` (model_id, search query, or file path)
- **Arg 2+:** `$2`, `$3`, etc. (additional parameters)
- **All args:** `$ARGUMENTS`
## Session Output
Save generated files to session folder:
```bash
mkdir -p ~/.fal/sessions/${CLAUDE_SESSION_ID}
```
Downloaded images/videos go to: `~/.fal/sessions/${CLAUDE_SESSION_ID}/`
---
## Authentication
Requires `FAL_KEY` environment variable. If requests fail with 401, tell user:
```
Get an API key from https://fal.ai/dashboard/keys
Then: export FAL_KEY="your-key-here"
```
---
## Command: `$0`
### If $0 = "search"
Search for models matching `$1`:
```bash
curl -s "https://api.fal.ai/v1/models?q=$1&limit=15" \
-H "Authorization: Key $FAL_KEY" | jq -r '.models[] | "• \(.endpoint_id) — \(.metadata.display_name) [\(.metadata.category)]"'
```
For category search, use:
```bash
curl -s "https://api.fal.ai/v1/models?category=$1&limit=15" \
-H "Authorization: Key $FAL_KEY" | jq -r '.models[] | "• \(.endpoint_id) — \(.metadata.display_name)"'
```
Categories: `text-to-image`, `image-to-video`, `text-to-video`, `image-to-3d`, `training`, `speech-to-text`, `text-to-speech`
---
### If $0 = "schema"
Get input schema for model `$1`:
```bash
curl -s "https://api.fal.ai/v1/models?endpoint_id=$1&expand=openapi-3.0" \
-H "Authorization: Key $FAL_KEY" | jq '.models[0].openapi.components.schemas.Input.properties'
```
Show required vs optional fields to help user understand what inputs are needed.
---
### If $0 = "run"
Run model `$1` with parameters from remaining arguments.
**Step 1: Parse parameters**
Extract `--key value` pairs from `$ARGUMENTS` after the model_id to build JSON payload.
Example: `/fal run fal-ai/flux-2 --prompt "a cat" --image_size landscape_16_9`
→ Model: `fal-ai/flux-2`
→ Payload: `{"prompt": "a cat", "image_size": "landscape_16_9"}`
**Step 2: Submit to queue**
```bash
curl -s -X POST "https://queue.fal.run/$1" \
-H "Authorization: Key $FAL_KEY" \
-H "Content-Type: application/json" \
-d '<JSON_PAYLOAD>'
```
**Step 3: Poll until complete**
```bash
# Get request_id from response, then poll:
while true; do
STATUS=$(curl -s "https://queue.fal.run/$1/requests/$REQUEST_ID/status" \
-H "Authorization: Key $FAL_KEY" | jq -r '.status')
echo "Status: $STATUS"
if [ "$STATUS" = "COMPLETED" ]; then break; fi
if [ "$STATUS" = "FAILED" ]; then echo "Job failed"; break; fi
sleep 3
done
```
**Step 4: Get result and save**
```bash
# Fetch result
RESULT=$(curl -s "https://queue.fal.run/$1/requests/$REQUEST_ID" \
-H "Authorization: Key $FAL_KEY")
# Create session output folder
mkdir -p ~/.fal/sessions/${CLAUDE_SESSION_ID}
# Download images/videos
# For images: jq -r '.images[0].url' and curl to download
# Save as: ~/.fal/sessions/${CLAUDE_SESSION_ID}/<timestamp>_<model>.png
```
---
### If $0 = "status"
Check status of request `$2` for model `$1`:
```bash
curl -s "https://queue.fal.run/$1/requests/$2/status?logs=1" \
-H "Authorization: Key $FAL_KEY" | jq '{status: .status, queue_position: .queue_position, logs: .logs}'
```
---
### If $0 = "result"
Get result of completed request `$2` for model `$1`:
```bash
curl -s "https://queue.fal.run/$1/requests/$2" \
-H "Authorization: Key $FAL_KEY" | jq '.'
```
---
### If $0 = "upload"
Upload file `$1` to fal CDN:
```bash
curl -s -X POST "https://fal.run/fal-ai/storage/upload" \
-H "Authorization: Key $FAL_KEY" \
-F "file=@$1"
```
Returns URL to use in model requests.
---
## Quick Reference
**Popular models:**
- `fal-ai/flux-2` — Fast text-to-image
- `fal-ai/flux-2-pro` — High quality text-to-image
- `fal-ai/kling-video/v2/image-to-video` — Image to video
- `fal-ai/minimax/video-01/image-to-video` — Image to video
- `fal-ai/whisper` — Speech to text
**Common parameters for text-to-image:**
- `--prompt "description"` — What to generate
- `--image_size landscape_16_9` — Aspect ratio (square, portrait_4_3, landscape_16_9)
- `--num_images 1` — Number of images
**Example invocations:**
- `/fal search video` — Find video models
- `/fal schema fal-ai/flux-2` — See input options
- `/fal run fal-ai/flux-2 --prompt "a sunset over mountains"`
- `/fal status fal-ai/flux-2 abc-123`
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