genmedia

genmedia workflow

Use this skill when the user wants to execute a fal.ai model — either by task description (e.g. "generate a video of a dog running") or by a specific endpoint_id (e.g. fal-ai/flux/dev). Load genmedia-ref alongside this skill for the full command reference.

Steps

1. Discover — If no endpoint_id is given, search for a suitable model:

   genmedia models "<task>" --json
   

2. Inspect — Get the model's input parameters:

   genmedia schema <endpoint_id> --json
   

   Read all required fields before running.

3. Upload files (if inputs include images/video/audio):

   genmedia upload <local_file_or_url> --json
   

   Use the returned url as the parameter value.

4. Run the model:

   • Fast model (completes in seconds):

     genmedia run <endpoint_id> --<param> <value> ... --json
     

   • Slow model (video generation, large jobs):

     genmedia run <endpoint_id> --<param> <value> ... --async --json
     genmedia status <endpoint_id> <request_id> --result --json
     

5. Save outputs — when the user expects files on disk, add --download to run or status. The CLI writes every media URL from the result to the filesystem and returns the local paths in downloaded_files[]. Do not curl the URLs yourself; use the flag.

   genmedia run fal-ai/flux/dev --prompt "a cat" --download --json                            # cwd, source file names
   genmedia run fal-ai/flux/dev --prompt "a cat" --num_images 3 --download "./out/{index}.{ext}" --json
   genmedia status <endpoint_id> <request_id> --download ./out/ --json                        # implies --result
   

   Use {index}, {name}, {ext}, {request_id} placeholders in the template when the model returns multiple files (images[], image_urls[], etc.) to avoid filename collisions. A trailing / or an existing directory path saves files under that directory using their source names.