Falai OpenClaw Skill - ClawHub
Do you want your AI agent to automate Falai workflows? This free skill from ClawHub helps with ai & llms tasks without building custom tools from scratch.
What this skill does
Generate images and media using fal.ai API (Flux, Gemini image, etc.). Use when asked to generate images, run AI image models, create visuals, or anything involving fal.ai. Handles queue-based requests with automatic polling.
Install
npx clawhub@latest install falaiFull SKILL.md
Open original| name | description |
|---|---|
| fal-ai | Generate images and media using fal.ai API (Flux, Gemini image, etc.). Use when asked to generate images, run AI image models, create visuals, or anything involving fal.ai. Handles queue-based requests with automatic polling. |
fal.ai Integration
Generate and edit images via fal.ai's queue-based API.
Setup
Add your API key to TOOLS.md:
### fal.ai
FAL_KEY: your-key-here
Get a key at: https://fal.ai/dashboard/keys
The script checks (in order): FAL_KEY env var → TOOLS.md
Supported Models
fal-ai/nano-banana-pro (Text → Image)
Google's Gemini 3 Pro for text-to-image generation.
input_data = {
"prompt": "A cat astronaut on the moon", # required
"aspect_ratio": "1:1", # auto|21:9|16:9|3:2|4:3|5:4|1:1|4:5|3:4|2:3|9:16
"resolution": "1K", # 1K|2K|4K
"output_format": "png", # jpeg|png|webp
"safety_tolerance": "4" # 1 (strict) to 6 (permissive)
}
fal-ai/nano-banana-pro/edit (Image → Image)
Gemini 3 Pro for image editing. Slower (~20s) but handles complex edits well.
input_data = {
"prompt": "Transform into anime style", # required
"image_urls": [image_data_uri], # required - array of URLs or base64 data URIs
"aspect_ratio": "auto",
"resolution": "1K",
"output_format": "png"
}
fal-ai/flux/dev/image-to-image (Image → Image)
FLUX.1 dev model. Faster (~2-3s) for style transfers.
input_data = {
"prompt": "Anime style portrait", # required
"image_url": image_data_uri, # required - single URL or base64 data URI
"strength": 0.85, # 0-1, higher = more change
"num_inference_steps": 40,
"guidance_scale": 7.5,
"output_format": "png"
}
fal-ai/kling-video/o3/pro/video-to-video/edit (Video → Video)
Kling O3 Pro for video transformation with AI effects.
Limits:
- Formats: .mp4, .mov only
- Duration: 3-10 seconds
- Resolution: 720-2160px
- Max file size: 200MB
- Max elements: 4 total (elements + reference images combined)
input_data = {
# Required
"prompt": "Change environment to be fully snow as @Image1. Replace animal with @Element1",
"video_url": "https://example.com/video.mp4", # .mp4/.mov, 3-10s, 720-2160px, max 200MB
# Optional
"image_urls": [ # style/appearance references
"https://example.com/snow_ref.jpg" # use as @Image1, @Image2 in prompt
],
"keep_audio": True, # keep original audio (default: true)
"elements": [ # characters/objects to inject
{
"reference_image_urls": [ # reference images for the element
"https://example.com/element_ref1.png"
],
"frontal_image_url": "https://example.com/element_front.png" # frontal view (better results)
}
], # use as @Element1, @Element2 in prompt
"shot_type": "customize" # multi-shot type (default: customize)
}
Prompt references:
@Video1— the input video@Image1,@Image2— reference images for style/appearance@Element1,@Element2— elements (characters/objects) to inject
Input Validation
The skill validates inputs before submission. For multi-input models, ensure all required fields are provided:
# Check what a model needs
python3 scripts/fal_client.py model-info "fal-ai/kling-video/o3/standard/video-to-video/edit"
# List all models with their requirements
python3 scripts/fal_client.py models
Before submitting, verify:
- ✅ All
requiredfields are present and non-empty - ✅ File fields (
image_url,video_url, etc.) are URLs or base64 data URIs - ✅ Arrays (
image_urls) have at least one item - ✅ Video files are within limits (200MB, 720-2160p)
Example validation output:
⚠️ Note: Reference video in prompt as @Video1
⚠️ Note: Max 4 total elements (video + images combined)
❌ Validation failed:
- Missing required field: video_url
Usage
CLI Commands
# Check API key
python3 scripts/fal_client.py check-key
# Submit a request
python3 scripts/fal_client.py submit "fal-ai/nano-banana-pro" '{"prompt": "A sunset over mountains"}'
# Check status
python3 scripts/fal_client.py status "fal-ai/nano-banana-pro" "<request_id>"
# Get result
python3 scripts/fal_client.py result "fal-ai/nano-banana-pro" "<request_id>"
# Poll all pending requests
python3 scripts/fal_client.py poll
# List pending requests
python3 scripts/fal_client.py list
# Convert local image to base64 data URI
python3 scripts/fal_client.py to-data-uri /path/to/image.jpg
# Convert local video to base64 data URI (with validation)
python3 scripts/fal_client.py video-to-uri /path/to/video.mp4
Python Usage
import sys
sys.path.insert(0, 'scripts')
from fal_client import submit, check_status, get_result, image_to_data_uri, poll_pending
# Text to image
result = submit('fal-ai/nano-banana-pro', {
'prompt': 'A futuristic city at night'
})
print(result['request_id'])
# Image to image (with local file)
img_uri = image_to_data_uri('/path/to/photo.jpg')
result = submit('fal-ai/nano-banana-pro/edit', {
'prompt': 'Transform into watercolor painting',
'image_urls': [img_uri]
})
# Poll until complete
completed = poll_pending()
for req in completed:
if 'result' in req:
print(req['result']['images'][0]['url'])
Queue System
fal.ai uses async queues. Requests go through stages:
IN_QUEUE→ waitingIN_PROGRESS→ generatingCOMPLETED→ done, fetch resultFAILED→ error occurred
Pending requests are saved to ~/. openclaw/workspace/fal-pending.json and survive restarts.
Polling Strategy
Manual: Run python3 scripts/fal_client.py poll periodically.
Heartbeat: Add to HEARTBEAT.md:
- Poll fal.ai pending requests if any exist
Cron: Schedule polling every few minutes for background jobs.
Adding New Models
- Find the model on fal.ai and check its
/apipage - Add entry to
references/models.jsonwith input/output schema - Test with a simple request
Note: Queue URLs use base model path (e.g., fal-ai/flux not fal-ai/flux/dev/image-to-image). The script handles this automatically.
Files
skills/fal-ai/
├── SKILL.md ← This file
├── scripts/
│ └── fal_client.py ← CLI + Python library
└── references/
└── models.json ← Model schemas
Troubleshooting
"No FAL_KEY found" → Add key to TOOLS.md or set FAL_KEY env var
405 Method Not Allowed → URL routing issue, ensure using base model path for status/result
Request stuck → Check fal-pending.json, may need manual cleanup