name: image version: 2.1.2 description: | Generate, edit, cut out, and inspect images with the best hosted image models (Nano Banana 2 / Pro, GPT Image 2, Seedream, Bria). Use for ANY image request — create from text, edit or retouch a photo, swap a logo/text, change background, upscale, product shots, portraits, try-on, 3D renders. This is THE image skill on Starchild — the former image-create / image-edit / image-3d / image-ecommerce / image-portrait / image-tryon / image-bg-remove skills are retired and folded in here. metadata: starchild: emoji: "🎨" skillKey: image user-invocable: true disable-model-invocation: false

image

One skill, four verbs. You write the prompt; the skill validates, calls, downloads, and remembers.

python3 - <<'EOF'
import sys; sys.path.insert(0, "skills/image")
from exports import generate, edit, remove_background, inspect, list_models
r = edit("Replace the first letter W of WALL ST with the logo in image 2, matching the sign's paint and perspective",
         ["uploads/sign.png", "uploads/logo.png"],
         keep="every other letter, the sign colour, background and lighting",
         model="nanopro", resolution="2K")
print(r["images"][0]["local_path"] if r["success"] else r["error"])
EOF

Always run in a bash python3 - <<'EOF' heredoc (not python3 -c). Result → {"success", "images":[{"local_path","dims"}], "job_dir", "request_id", "cost_usd", "prompt", "params"}; on failure {"success": False, "error"}. Every job gets its own directory — nothing is overwritten.

The four verbs

list_models() tells you each model's real capabilities. Read it when you're unsure; don't memorise.

Picking a model — call list_models(), then:

• Drafts / exploring → nano2 (fastest, cheapest).
• Default, multi-reference, instruction-heavy edits → nanopro. Output is 1K unless you pass resolution="2K"|"4K" — always set it when the source is large, or you'll shrink the user's image.
• Region-locked edit (change only this area, pixels elsewhere must not move) → gpt with mask_path (white = editable). It's the only model with a real mask. Slow (~2 min) — say so.
• Many references / asset composition → seedream (candidate: name it explicitly).
• "Best quality" requests → tell the user what you picked and why; run 2 candidates if the budget allows rather than guessing.

Unknown model, unsupported parameter, too many references, mask on a model without mask → the call refuses before spending. Read the error; it lists what is supported. Never work around it by dropping a parameter silently — that's how outputs come back the wrong size.

Principles that prevent the known failures

1. Restate the goal before you write the prompt. Two sentences to yourself: change = … , keep = … . Put the keep into keep=. "Replace the first W, keep ALL ST" must never become "only the letter W" in the prompt.
2. Name every reference's role. "Image 1 is the base. Image 2 is the logo to place. Image 3 is the colour reference." Models can't infer roles from file order.
3. Precise text, logos, exact type → AI models are unreliable at this. Prefer: clear the region with edit (or mask), then composite the real vector/text deterministically (PIL), then optionally one edit pass "blend the pasted element's lighting and grain". Don't ask a diffusion model to draw a brand mark from memory.
4. Look before you loop. If you don't know where something is or what text says, inspect(..., mode="inspect") with a concrete question. Don't crop-and-ask five times; one good question with coordinates request suffices. Treat returned boxes as approximate.
5. QA against the stage, not the final product. An intermediate "W removed, logo still to be added" image must pass mode="qa" with that goal. Pass goal= and keep= explicitly.
6. One automatic retry, then ask. For multi-step edits open a transaction (start_transaction(goal, base_path, keep=, logo=..., end_frame=...)), pass tx_id= to each edit, mark auto_fix=True on your own retries. The budget is one; after that show candidates with a one-line diff each and let the user choose (approve(tx_id, rev)). Rejected outputs never become the next base.
7. Deterministic ops stay deterministic. Colour grading, blur, crop, resize, shadow, perspective — PIL/OpenCV, not a paid regeneration. Real upscaling = target pixel size with resolution/image_size, not the word "upscale" in a prompt.
8. Video frames: a start/end frame pair must be two approved revisions. If the video model takes one image, say so instead of hoping.

Domain know-how

Product shots for Amazon/Xiaohongshu, ID photos, try-on wording, 3D render levers, the 20 old edit actions and where they went — references/domain-guides.md (read once when the request is in one of those domains).

Recovery & cost

• Timeout or dropped connection → recover(job_id) re-fetches the paid result; don't resubmit.
• Costs land on the user's turn automatically; cost_usd in every result. Mention it when >$0.10 or when running multiple candidates.
• Local testing without the gateway: set FAL_KEY.

Boundaries

Real people / faces: fine for the user's own photos; refuse impersonation or sexual content. Brands: use assets the user provides. When in doubt about a person's identity or consent, ask first.