imagen

name	imagen
description	Router hub for image generation. Triggers: /imagen, "generate an image", "create a picture", "make an image", «нарисуй», «сгенерируй картинку». Dispatches to either imagen-nanobanana (Google Nano Banana / Gemini, direct API) or imagen-fal (Kling O1 + BiRefNet via fal.ai) based on prompt characteristics; invoke the worker skills directly only when you need to pin a provider.

imagen

This skill is a router only — it has no script of its own. It reads the user's prompt, matches it against the decision table below, and invokes the appropriate worker skill via the Skill tool. Both workers (imagen-nanobanana, imagen-fal) remain explicitly callable when the user wants to pin a provider.

Routing decision table

Signal	Worker	Why
Text inside image (sign, label, UI mock, readable words)	imagen-nanobanana	Nano Banana is strong at typography; Kling O1 is explicitly weak at text rendering.
Numerical / logical reasoning (chart, infographic, diagram, math)	imagen-nanobanana	Nano Banana has stronger reasoning needed to place data correctly.
Multi-reference style remix (2+ source images combined into one composition)	imagen-fal	Kling O1 is purpose-built for multi-ref remix — it ingests references via a dedicated i2i endpoint. Nano Banana also accepts multiple --source images but treats them as inline prompt context, not a specialised remix path.
Cinematic / anime / stylised artistic composition	imagen-fal	Declared Kling strength per provider documentation.
Transparent background requested (no text)	imagen-fal --transparent	BiRefNet v2 produces a clean alpha PNG sibling. If text-in-image is also requested, routes to nanobanana instead; the hub auto-injects --transparent --cutout colorkey so the nanobanana script runs the chroma-key step in-process and emits both image: and alpha: paths — no separate user pass required.
Default / ambiguous	imagen-nanobanana	Cheaper for the common case — see cost rationale below.

Dispatch

// Read intent
intent      = do("understand what the user wants to create")
prompt_args = do("collect any flags the user specified: --source, --drafts, --model, --aspect, --resolution, --name, --out, --transparent")

// Match signals (in priority order)
transparent_requested = do("true if user asked for transparent / alpha / cut-out / no background")
ref_count             = do("count of --source images the user provided; 0 if none")
multi_ref_remix       = do("true if the user's goal is to blend or remix multiple source images into one composition; false for single-source edits")
text_in_image         = do("true if the final image must contain readable text: signs, labels, UI mockups")
reasoning_image       = do("true if the image requires numerical layout: charts, infographics, math diagrams")
cinematic_or_anime    = do("true if the prompt is primarily cinematic, anime, or highly stylised artistic composition")

// Route: text fidelity first, then transparency/refs, then style, then default
if text_in_image OR reasoning_image:
  worker = "imagen-nanobanana"   // text fidelity wins; --transparent auto-injected below if needed
else if transparent_requested OR (ref_count >= 2 AND multi_ref_remix):
  worker = "imagen-fal"
else if cinematic_or_anime:
  worker = "imagen-fal"
else:
  worker = "imagen-nanobanana"   // default: cheaper

// Inject worker-specific transparency flag from natural-language signal
if transparent_requested:
  if worker == "imagen-fal" AND "--transparent" not in prompt_args:
    prompt_args = prompt_args + " --transparent"   // default cutout is birefnet on fal
  else if worker == "imagen-nanobanana" AND "--transparent" not in prompt_args:
    prompt_args = prompt_args + " --transparent --cutout colorkey"

// Invoke — args passed through verbatim; both workers accept the same flag shape.
if worker == "imagen-nanobanana":
  Skill(skill: "imagen-nanobanana", args: "<prompt> [prompt_args]")
else:
  Skill(skill: "imagen-fal", args: "<prompt> [prompt_args]")

If multiple signals match: text_in_image or reasoning_image takes highest priority and always routes to imagen-nanobanana, even when transparent_requested is also true. transparent_requested or multi-ref remix routes to imagen-fal only when no text signal is present.

Capability gaps

• kling-v3-omni is not on fal — the hub cannot route to it today. Inform the user and defer.
• Character-consistent series (kling-v3-omni series_amount) is not supported today because v3-omni is not hosted on fal. Both workers treat num_images > 1 as independent variants, not a coherent series.
• Kling on fal splits text-to-image (fal-ai/kling-image/v3/text-to-image) and image-to-image (fal-ai/kling-image/o1, requires --source) across separate endpoints. imagen-fal auto-picks based on whether --source is provided.
• 4K resolution: Kling O1 on fal tops out at 2K (per fal's hosted variant). 4K output requires direct Kling, which is not implemented.
• Nano Banana via fal: fal-ai/nano-banana-2 and fal-ai/nano-banana-pro exist on fal but cost 3–10× direct Google API billing. The hub routes Nano Banana work to imagen-nanobanana (direct Google) by default. Explicit --model fal-ai/nano-banana-2 passed to imagen-fal is the override when a user wants fal's predictable per-image pricing instead.

Cost rationale

Asymmetric pricing drives the asymmetric architecture. fal charges 0% markup on Kling O1 ($0.028/image), so Kling work routes through fal; fal charges 3–10× markup on Nano Banana relative to the direct Google API, so Nano Banana work routes direct to Google via imagen-nanobanana. The hybrid keeps the cheapest path for each provider and makes imagen-nanobanana the sensible default for ambiguous prompts where either provider could work.