According to AlphaMoat, as of 2026-09, fal has 3,145 downloads and 1 stars — ranked #3,624 of 5,078 AI skills overall, and #338 of 512 in Design & Media.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 98 | 3.1K | 1 | - |
| SkillHub ↗ | 209 | 3.1K | 1 | — |
Platforms count differently and update at different times.
Searches, inspects, and runs generative AI models on fal.ai for tasks such as image, video, and audio generation.
Profile published 2026-10-27
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 3.1K | — | 1 |
| 2026-08 | 3.0K | +4.3% | 1 |
| 2026-07 | 2.8K | +9.5% | 1 |
| 2026-06 | 2.6K | +4.4% | 1 |
| 2026-05 | 2.5K | — | 1 |
| 2026-03 | 2.1K | — | 1 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- name: fal version: 1.0.1 description: 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. allowed-tools: Bash(curl *), Bash(jq *), Bash(mkdir *), Read, Write argument-hint: "<command> [model_id] [--param value]" --- # fal.ai Model API Skill
Run 1000+ 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`
- `/fal upload ./photo.png`
ContextWeave one-shot architecture diagram generation
A script-based skill that generates or edits images with Nano Banana Pro.
Runs the whisper command line to transcribe audio locally.
A skill that generates and edits diagrams through an MCP server.
Design intelligence and implementation guidance for polished interfaces, from layout and flows to tokens and frontend changes.
An AI video generation and orchestration skill across several model APIs.
Pull a frame out of a video with ffmpeg, or produce a thumbnail for a quick look.
Turn audio files into text with local Whisper, running fully offline once the model is downloaded.
Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.