fal

v1.0.1

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.

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Purpose & Capability
The skill is described as a fal.ai model runner and all runtime instructions call fal-related endpoints (api.fal.ai, queue.fal.run, fal.run); requesting an API key for fal.ai (FAL_KEY) is appropriate. However, the registry metadata lists no required environment variables while SKILL.md explicitly requires FAL_KEY, so the declared metadata does not fully match the runtime instructions.
Instruction Scope
SKILL.md contains concrete curl-based commands to search models, fetch schemas, submit jobs, poll status, download results, and upload files. It only references the fal domains and saves outputs to ~/.fal/sessions/${CLAUDE_SESSION_ID}. It does not instruct reading unrelated files or exfiltrating arbitrary data. Note: it references CLAUDE_SESSION_ID (a platform session variable) which is not declared in the metadata but is likely provided by the agent environment.
Install Mechanism
This is instruction-only (no install spec and no code files to execute). That minimizes on-disk installation risk.
!
Credentials
The skill needs one sensitive credential (FAL_KEY), which is reasonable for calling fal.ai APIs. However, the skill metadata failed to declare this required environment variable. The omission is an inconsistency that could confuse users and automated permission reviews; verify you are intentionally providing a fal.ai API key and not other platform secrets.
Persistence & Privilege
The skill does not request permanent/always-on inclusion (always: false) and does not modify other skills or global agent settings. It does write output files under the user's home (~/.fal/sessions), which is expected for this use case.
What to consider before installing
This skill appears to be a straightforward wrapper around fal.ai HTTP APIs and needs a single API key (FAL_KEY). Before installing: 1) Confirm the skill source — the registry metadata lacks a homepage and does not declare FAL_KEY even though SKILL.md requires it. Prefer official repos (e.g., fal.ai or fal-ai GitHub). 2) Only provide an API key you control; consider creating a limited-scope/test key if fal.ai supports it. 3) Be aware the skill will write generated media to ~/.fal/sessions/${CLAUDE_SESSION_ID} — check that location and remove any sensitive outputs. 4) If you’re concerned about network endpoints, verify the domains (api.fal.ai, queue.fal.run, fal.run) are legitimate before using your key. 5) If you need higher assurance, ask the publisher for a homepage and a signed/known source or inspect an actual implementation (code) rather than relying on instructions-only metadata.

Like a lobster shell, security has layers — review code before you run it.

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2.1kdownloads
1stars
2versions
Updated 1mo ago
v1.0.1
MIT-0

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:

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:

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:

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:

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

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

# 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

# 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:

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:

curl -s "https://queue.fal.run/$1/requests/$2" \
  -H "Authorization: Key $FAL_KEY" | jq '.'

If $0 = "upload"

Upload file $1 to fal CDN:

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

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