| name | nano-banana-image-ad |
| description | Generate one or more standalone Meta image-ad creatives via Nano Banana Pro through the Novoads API. Locks the model, auto-strips platform chrome, enforces edge-safe layouts. Use when the user asks for a "Nano Banana ad", "Gemini image ad", "nano-banana-pro ad creative", "make a static image ad with Gemini", or anchors on a need for photoreal / lifestyle / multi-reference / handheld-object / clay-texture ad creatives (sticky-note flatlays, held-whiteboard signs, lifestyle portraits, ingredient collages, OOH photography). Does NOT trigger on ChatGPT Image cues — use chatgpt-image-ad for those. |
nano-banana-image-ad
Generate one or more standalone Meta ad image creatives via Novoads' POST /v1/images
with model: "nano-banana-pro". Hands the image paths off to your Meta-ad-builder skill —
this skill does not upload to Meta itself.
If the shared/ files are not on disk
Then this skill was installed on its own from skills.sh and the rest of the pack stayed behind.
Fetch each missing file from https://raw.githubusercontent.com/novoads/claude-code-ads/main/<path>,
where <path> is one of shared/skills/image-ad-prompting/OVERVIEW.md,
shared/skills/image-ad-prompting/prompting/prompt-library.md,
shared/skills/image-ad-prompting/prompting/safety-suffixes.md and
shared/skills/nano-banana-image-ad/prompting/guide.md. Or take everything at once with
git clone https://github.com/novoads/claude-code-ads.git. That install has no
scripts/check-novoads-env.sh either, so set NOVOADS_API_KEY in the environment yourself and
let the first call report the key.
Read order
Paths below are from the repo root. This skill is copied into .claude/skills/ and
.cursor/skills/ by sync-skill.sh, so a relative link out of it would break in those
copies — read these from the repo you are working in.
- This file — endpoint, auth, upload flow, workflow phases.
shared/skills/image-ad-prompting/OVERVIEW.md — the ecosystem hub: which skill, which model, what the family does and doesn't do.shared/skills/nano-banana-image-ad/prompting/guide.md — model-specific prompting (what Nano Banana Pro is good/bad at, when to switch to gpt-image-2).shared/skills/image-ad-prompting/prompting/prompt-library.md — 40 validated templates with per-model notes.shared/skills/image-ad-prompting/prompting/safety-suffixes.md — the 3 always-on guards.skills/nano-banana-image-ad/scripts/generate_image.py — the helper script (Python stdlib only).
For anything about the API itself that this file does not answer — error codes, rate limits,
concurrency, the upload contract — the novoads-api skill's reference.md is the authority.
Hard rules — never relax
- Model is
nano-banana-pro. It is the only Nano Banana on this API — there is no nano-banana-2, no nano-banana-edit, no legacy nano-banana. The script refuses anything else. If the user asks for gpt-image-2, point them at chatgpt-image-ad.
- No platform/screenshot chrome in output.
NO_CHROME_SUFFIX is always on (override only with --allow-chrome).
- Edge-safe + glyph-safety suffixes always on unless
--no-safe-zone is explicit.
- Max 14 reference images.
referenceAssetIds caps at 14 on nano-banana-pro (spec 2.7.0; gpt-image-2 takes 4, reve-2.1 8). The script enforces it — and 2-4 well-chosen references usually beat many.
- Every brand mark that appears in the frame is passed as a reference asset. Not a tip — a rule. Image models invent brand-shaped content on any surface the prompt leaves unspecified, and state it as fact. Measured on the sibling model over a 34-template run (2026-08-04): the product label was faithful in all 34 because a reference pinned it, while unpinned marks failed three ways — an invented back-of-can panel carrying a sourcing claim nobody wrote, a Hydro Flask logo on a prop thermos, and a drifted publication wordmark. This model's 14-reference headroom makes the rule cheap to follow: pin the product, the logo, and any third-party mark the concept needs. For product surfaces the ad does not show, say so explicitly ("front label only; no invented label copy, no barcode"). Unpinned means invented. Pass it as
--pin-block "<one-line product description>" rather than writing the clause by hand: the script wraps the shared library's standard 346-character guard around your description, counts it against this model's prompt cap (50,000 characters, the largest on the API, deployed spec 2.16.0, verified 2026-08-08), and refuses pre-network naming the pin block's share if it does not fit. Hand-written pin blocks ran ~700 characters and used to be the single largest cause of a refused prompt, back when the cap was 4,000 on every model. At 50,000 that pressure is gone, and the reason to pass --pin-block is that the standard guard is the wording that was measured to work.
- No Meta upload from this skill. Image generation only. Hand off via filesystem paths.
- Always show a live cost estimate before generating, and get an explicit yes. The price comes from in this session — never from memory, never from , never from . There are no credit numbers written down anywhere in this repo, on purpose. Nano Banana Pro costs more per image than gpt-image-2, so the model choice is worth surfacing when you show the quote.
Prerequisites
.env containing NOVOADS_API_KEY (novo_ + 64 hex, created at https://novoads.ai/dashboard/settings?tab=api). Verify with ./scripts/check-novoads-env.sh.- Optional:
PRODUCT_ID in .env so generated assets are filed under a specific product. Omit it and the job lands in your default product. productId is organizational only — it does not influence what is generated.
- Reference images on local disk (PNG/JPG/JPEG/WEBP). The script handles the upload flow internally.
REST key required. A Novoads MCP connector is not a substitute. If
NOVOADS_API_KEY is missing or still the placeholder, stop before any
generation work and tell the user: "Before continuing, create an API key at
https://novoads.ai/dashboard/settings?tab=api and paste it into .env."
That holds even when mcp__novoads__* tools are connected and authenticated in
the session. Never call mcp__novoads__* tools from this repo's workflows: they
are a different surface with different behavior, including the units they quote
costs in. Repo installs verify with ./scripts/check-novoads-env.sh; a solo
install checks NOVOADS_API_KEY in the environment.
Configuration
- Base URL:
https://api.novoads.ai (or NOVOADS_BASE_URL). That is the host only — callers append /v1/....
- Auth:
Authorization: Bearer $NOVOADS_API_KEY.
- Endpoint:
POST /v1/images — synchronous. The call blocks for the render (typically 60–90 seconds) and returns the finished images. There is no job to poll and no asset endpoint to wait on.
- Reference uploads:
POST /v1/uploads with {contentType, sizeBytes} returns {assetId, uploadUrl, method, headers, expiresInSeconds, maxBytes}. PUT the raw bytes to uploadUrl echoing the returned headers byte for byte — Content-Type and Content-Length are both part of the signature, so storage returns 403 if either differs. The resulting assetId is durable and reusable: upload a character or product shot once and pass the same id on every later call. That is what makes cross-run identity consistency cheap here.
What this model takes
| Field | Value |
|---|
model | nano-banana-pro |
prompt | the image prompt — the always-on suffixes count against the model's cap |
aspectRatio | 1:1 2:3 3:2 3:4 4:3 4:5 5:4 9:16 16:9 21:9 — defaults to 1:1, so always set it |
referenceAssetIds | up to 14, order preserved, addressable positionally from the prompt |
numImages | 1–4, one call, charged per image |
productId | optional, organizational only |
This model takes the full ratio set — including 3:2, 3:4, 4:3 and 5:4, which
gpt-image-2 does not. That is the one hard reason to route a bespoke prompt here regardless
of style.
N variants is one call, not N calls. Send numImages: 4 and four images come back in
images[]. Do not fan out four parallel requests — that burns four of your five concurrency
slots to get the same result at the same price.
There is no edit mode on THIS model. The Nano Banana inpainting flows the older skill
had do not exist on this API — no nano-banana-edit, no mask, no img2img — and
nano-banana-pro does not publish sourceAssetId. Editing an existing image is a
gpt-image-2 capability (spec 2.10.0): route it to chatgpt-image-ad rather than
approximating it here with a reference-led regeneration.
Inputs the user must provide
| Input | Notes |
|---|
| Seed prompt | The creative direction in their words. You will rewrite it (see Phase 3). |
| Aspect ratio | One of the ten above. Reject anything else. |
| Reference image(s) | Optional but strongly recommended for a specific product, character or brand mark. Up to 4. |
Variant count N | Default 1. Cap at 4. Each one is charged. |
Workflow
Phase 1: Preflight
.env exists with NOVOADS_API_KEY.- Health check:
./scripts/check-novoads-env.sh prints OK (a 401 is a bad key; a 403 with details.reason: plan_required is a good key on an account without API access — different fixes).
Phase 2: Gather inputs
Collect: seed prompt, reference paths (up to 4), variant count, aspect ratio.
Present choices per shared/skills/image-ad-prompting/OVERVIEW.md § Presenting choices to
the user, which is one decision at a time and never this four-item brief up front.
Phase 3: Prompt rewrite
Read shared/skills/image-ad-prompting/prompting/prompt-library.md. If the user's brief
matches a template, check the Model notes block — only proceed if nano-banana is marked
clean, preferred, or strong. If gpt-image-2 is preferred, suggest switching skills.
Fill {placeholders} and show the user the rewritten prompt. Ask for approval before generating.
For fresh prompts (no template match), follow the structure in
shared/skills/nano-banana-image-ad/prompting/guide.md § Phase 3b — lean on Nano Banana
strengths (named reference roles, lighting specifics, material specifics).
Phase 4: Cost confirmation (MANDATORY)
curl -sS -X POST "$NOVOADS_BASE_URL/v1/estimates" \
-H "Authorization: Bearer $NOVOADS_API_KEY" \
-H 'Content-Type: application/json' \
-d '{"kind":"image","model":"nano-banana-pro","prompt":"<final prompt>","numImages":<N>}'
Free, and the only legitimate source of a price. Name the model in the estimate body — the
image models' schedules differ by more than 3×, so pricing the wrong one is a quote that
disagrees with the invoice. Show the user credits, and whether sufficient is true against
their balance. If it is false, say so and stop — the response carries shortBy and
topUpUrl. Wait for an explicit yes before Phase 5.
Three things to know about this call:
- It says nothing about an image prompt.
POST /v1/estimates does return an advisory warnings array of craft notes, but every rule in it is video craft: an image estimate carrying the words that trip the video rules came back with no warnings key at all (verified live 2026-08-04). So for this route a weak prompt prices, charges and renders exactly like a strong one, and the rewrite rules in Phase 3 are the only check there is — apply them before you price.
- The body is strict. Only
kind, prompt, model, numImages, language are accepted. Sending aspectRatio or referenceAssetIds is a 400 Unrecognized key — the estimate never sees them, and neither affects the price.
- Its length check happens to match this model's. The estimate's own request schema caps
prompt at 50,000 characters for every image model, which is exactly this model's ceiling on POST /v1/images (deployed spec 2.16.0, verified 2026-08-08). The agreement is a coincidence of this being the roomiest model on the API: on gpt-image-2 (32,000) and reve-2.1 (4,000) the estimate is the looser of the two and will price a prompt the generation then refuses. It does not run moderation either, so a prompt it blessed can still come back 422 content_policy.
Estimate the final prompt, the one the script will send with the safety suffixes
appended, or the quote will be short by roughly 1,500 characters' worth of prompt.
Phase 5: Generate
./skills/nano-banana-image-ad/scripts/generate_image.py \
--prompt "<rewritten>" \
--aspect-ratio <ratio> \
--n <N> \
--image-ref <product.png> \
[--image-ref <character.png>] \
[--image-ref <style.png>] \
--out ./generated \
--env-file .env
Each line on stdout is one JSON image (variant, path, job_id, width, height,
prompt, aspect_ratio, model, credits_charged).
Log the call to logs/novoads-api.jsonl per logs/README.md — images are written once,
complete (sync endpoint, no jobId to poll back). Record creditsCharged from the response,
never an estimate. Observability only, never a pricing source.
Phase 6: Visual QA (MANDATORY)
For each image, read it and inspect for:
- Garbled small text (the main Nano Banana weakness — if the ad is text-led, it belongs in
chatgpt-image-ad)
- Extra fingers / wrong limb count (common Gemini-family failure)
- Wordmark drift (always pass brand wordmarks as
--image-ref to mitigate)
- Character identity drift across variants — reuse the same
assetId for the character reference on every call, which is what keeps a face consistent across runs
If defective: regenerate with a revised prompt explicitly correcting the issue (see
shared/skills/nano-banana-image-ad/prompting/guide.md § Retry mode). Cap at 2 retries.
Each retry is a fresh charge — there is no free re-roll — so no re-confirmation is needed
within the cap, but report the extra credits at the end of the run. After the cap, stop and
show the best attempt.
Phase 7: Confirm and hand off
Show all paths to the user. Ask "Use all / use these specific ones / regenerate / cancel."
Open the output folder (open on macOS, xdg-open on Linux) so the user sees the results
without hunting for them.
Selected images are ready for the meta-ad-builder skill. Print the paths. Optionally
write them to ./generated/run-<ts>.jsonl for downstream consumption.
Out of scope — fail clearly
- Meta upload — the
meta-ad-builder skill.
- ChatGPT Image 2 / gpt-image-2 generation — use
chatgpt-image-ad.
- Editing an existing image — no model on this API has an edit or inpainting path. Offer a fresh generation with the original as a reference.
- Video, carousel, DCO ads — image only. Video lives in the
novoads-api skill.
- Ad copy writing — different skill.
- Editing the shared prompt library — use
clone-image-ad.
Common errors
| What you see | What it means |
|---|
400 invalid_input with details.issues | A malformed field. Most often more than 4 referenceAssetIds, a numImages above 4, or a prompt over the model's cap. Nothing was charged. |
401 unauthorized | Missing, malformed or revoked key. Send the user to https://novoads.ai/dashboard/settings?tab=api. |
402 insufficient_credits | details carries required and available. Tell the user the gap; do not retry. |
403 forbidden | details.reason says which: plan_required, subscription_inactive, or API access off for the account. |
422 content_policy | Moderation blocked it. The estimate does not run moderation, so this can land on a prompt the estimate priced cleanly. Nothing was charged. Rewrite or stop. |
429 rate_limited | Branch on details.reason. key_limit / organization_limit / client_limit are paced by slowing down; concurrency_limit is not — only waiting frees a slot. Honor Retry-After. |
403 on the presigned PUT | The signed headers weren't echoed byte for byte. Send the headers object from POST /v1/uploads exactly as returned. |
500 internal_error | Do not blindly retry — there are no idempotency keys, so a retry can double-charge. Check GET /v1/generations for a job that already landed. |
502 provider_failed | The model provider failed. Credits are refunded automatically. |
Files this skill owns
skills/nano-banana-image-ad/SKILL.md — this fileskills/nano-banana-image-ad/scripts/generate_image.py — the nano-banana-pro caller (upload + generate + download)
See also
shared/skills/image-ad-prompting/OVERVIEW.md — ecosystem hub, read firstshared/skills/nano-banana-image-ad/prompting/guide.md — model-specific prompting, retry playbookshared/skills/image-ad-prompting/prompting/prompt-library.md — 40 validated templates- the
clone-image-ad skill — reverse-engineers an existing ad into a reusable library entry
- the
chatgpt-image-ad skill — sibling for typography-heavy / UI-mimicry templates
- the
novoads-api skill — the API contract underneath all of this
