Update to fal

{
  "description": "Design multi-step fal.ai media workflows for MCP execution. Use when the user wants model chaining, batch generation, fan-out/fan-in, frame bridging, narrated video, dataset creation, reusable pipeline logic, or workflow JSON.\n",
  "included_files": [],
  "name": "fal-workflow",
  "skill_md_contents": "---\nname: fal-workflow\ndescription: >\n  Design multi-step fal.ai media workflows for MCP execution. Use when the user\n  wants model chaining, batch generation, fan-out/fan-in, frame bridging,\n  narrated video, dataset creation, reusable pipeline logic, or workflow JSON.\n---\n\n# fal.ai Workflows\n\nUse this skill when a single model call is not enough. A workflow is a planned\nsequence of model calls with dependencies, retries, quality gates, and a final\nmanifest.\n\n## Workflow Modes\n\n- Cloud workflow JSON: use when the user wants a reusable graph asset.\n- MCP-orchestrated workflow: use when the client should run a sequence now through\n  the fal.ai MCP tools.\n\nDo not call genmedia CLI from this plugin. If a source skill mentions CLI\norchestration, translate that intent into MCP tool usage.\n\n## Planning Pattern\n\n1. Define final deliverable and acceptance criteria.\n2. Split into nodes: input assets, generation, utility transforms, edits,\n   video/audio/3D steps, and final packaging.\n3. Choose model per node with `model-routing`.\n4. Capture dependencies: which output URL feeds which next input.\n5. Inspect schema for each endpoint before building payloads.\n6. Run independent expensive nodes async.\n7. Poll async nodes without restarting completed work.\n8. Gate each step: preserve identity/product, check duration, check format.\n9. Return a manifest: node, endpoint, request id, inputs, output URLs, defects.\n\n## Useful Patterns\n\n- Anchor first: generate or upload a strong still, then drive edits/video from\n  that approved asset.\n- Fan-out: create several variants from the same anchor, then select one.\n- Fan-in: combine product, logo, character, or style references into an edit.\n- Frame bridge: generate first and last frame, then use image-to-video or\n  supported controls to bridge motion.\n- Audio-first: generate/upload voice first, then match visuals to duration.\n- Utility cleanup: background removal, compression, upscaling, format\n  conversion, segmentation, or OCR as deterministic nodes.\n\n## Local File Inputs\n\n- With hosted MCP and local file/HTTP access, call `upload_file` with\n  `prepare_upload: true`, the basename as `file_name`, and the actual byte count\n  as `file_size` (up to 90MB). This uses the already connected fal account.\n- PUT the actual file bytes to the returned `upload_url` with its returned\n  headers. For example, a local HTTP client or `curl --upload-file` can stream\n  the file without putting its contents into the conversation. Keep the signed\n  URL private; send no Authorization header and do not follow redirects.\n- `status: upload_required` means no bytes have been uploaded yet. Only use\n  `file_url` as model input after the PUT returns 2xx. If the PUT fails, stop\n  before generation; do not guess that a file exists at the returned URL.\n- Never pass a local path to hosted MCP, copy base64 through chat, or obtain a\n  different API key to work around a rejection. If local file/HTTP access is\n  unavailable, ask for a public file URL. Stdio supports `file_path` when the\n  server's local-file allowlist permits it.\n\n## Failure Handling\n\n- 422/schema error: re-inspect schema and remove guessed fields.\n- Product/identity drift: switch to reference/edit workflow.\n- Video still running: keep polling the same request id.\n- Bad prompt alignment: reduce prompt scope and change one variable at a time.\n- Cost spike: reduce variants, duration, resolution, or use draft endpoints.\n"
}