Generating Images

Image Generation

Turn a brief — text, optionally with reference image(s) — into a still image via the image_generate tool. Two decisions drive quality: which model (always) and which format recipe (only when the deliverable is a known one).

Before you route: if the brief is commercial product photography — a packshot, a product in a styled scene, a hero or banner for a product, an on-model or try-on shot, or restyling an existing product photo — hand off to the generating-product-photos skill, which owns that surface. If it is a product advertisement — a headline, an offer or a call to action drawn over the product, a promotional before/after or comparison — hand off to generating-image-ads. Stay here for everything else: general images, graphics and posters, portraits, illustrations, cinematic stills, infographics, and one-off reference edits.

Workflow

Step 1: Route to a model

Route by what the image has to do — read the brief for intent. The descriptions below are signals to weigh, not a literal router — the examples are illustrative, not a checklist to match against. Then read the chosen model's prompt guide before writing anything.

| Route when the brief is about… | Model | Prompt guide | | --- | --- | --- | | Rendering words legibly, or a designed layout where elements sit in deliberate positions — for example a poster, ad, banner, thumbnail, or infographic | gpt-image-2 | references/prompt-gpt-image-2.md | | A drawn or rendered look rather than a photograph — for example a cartoon, anime, illustration, flat vector, or 3D render | nano-banana-2 | references/prompt-nano-banana.md | | A convincing real person, or a photographic frame with deliberate cinematography — for example a creator or influencer portrait, UGC, or a film-like still | nano-banana-pro | references/prompt-nano-banana.md | | Altering a supplied image — for example swapping a background, removing or replacing an element, or restaging the scene | gpt-image-2 | references/prompt-gpt-image-2.md | | Altering a supplied image where a real face must stay recognisable | seedream-5 | references/prompt-seedream.md |

When more than one applies, take them in this order:

1. The user named a model → use it.
2. Legible text is required → gpt-image-2, even over a person or a scene.
3. The look is non-photographic → nano-banana-2, even when a person is involved.
4. A supplied image is being altered → gpt-image-2; switch to seedream-5 when a real person

must stay recognisable.

1. A supplied image is only a style or mood cue (match this look, don't edit that image) →

nano-banana-2, or seedream-5 if a specific face must carry over too.

If nothing clearly fits — an ordinary object or scene — use nano-banana-2 (read references/prompt-nano-banana.md); or call list_image_models and pick by strengths. Use grok-imagine (references/prompt-grok.md), flux-2-pro, or flux-2-klein-4b (both references/prompt-flux.md) only when the user names them.

Step 2: Read a format recipe if one fits (optional)

Many briefs are a standard deliverable with a layout worth following. Check the table below. If a row matches, read that recipe and follow its sections and example. If none matches, skip this step — the model's prompt guide is enough.

| Deliverable | Recipe | | ------------------------------------ | -------------------------------- | | Poster / ad / banner / social graphic | references/format-poster.md | | Portrait / avatar / influencer | references/format-portrait.md | | Cinematic still | references/format-cinematic.md |

Step 3: Get any missing inputs

Ask only for what would change the result and can't be sensibly defaulted — for example the exact text that has to appear on the image, a source image for an edit, or the desired aspect ratio when that decides the crop. Bundle everything you need into one ask rather than a back-and-forth; if the user has signalled they don't want questions, choose sensible defaults, state them in a line, and proceed.

Step 4: Look at the reference images — only when references are supplied

Call image_analysis on each reference, asking whatever the prompt will have to carry — enough to name what must stay fixed and to avoid contradicting the source.

Skip when you already know what's in the image — you generated it this turn, or it arrived with a description.

If what you find changes the routing — a face to keep recognisable, a stylized source — go back to Step 1.

Step 5: Write the prompt

Build the prompt as the chosen model's prompt guide specifies. If you read a format recipe, start from its sections and worked example. With a reference image, use what Step 4 turned up to say what stays fixed (the product, the face, the palette) and what changes.

Write a complete, self-contained prompt — the model sees only this one prompt. Specify the whole frame: subject, wardrobe / materials, setting, lighting, colour palette, camera / lens, medium, and mood. A dense, concrete, technical description beats a thin one-liner or vague adjectives ("nice", "cool") — that density is what separates an editorial result from a generic one.

Step 6: Generate

Call image_generate with a requests list (one object per image):

• Per object: prompt (required); model from Step 1; aspect_ratio; resolution —

1k unless the user asked for a higher tier; reference_images for a supplied source.

• For several different images (an A/B set, a carousel), add one request object per image to the

same call.

Same subject or style across a set. Requests are generated independently, so a back-reference ("the same woman", "same outfit") produces a different result each time. Write the appearance, wardrobe, and style once and repeat that description word-for-word in every request; vary only the shot, framing, and aspect ratio, and hold one lighting and palette across the set.

Images are polled for you, but a heavy one — a large model, 4k, or a big batch — can come back as {status: "pending", …} (a job handle, not a failure). Pass that exact handle to job_status to retrieve the finished image — never re-run a pending image with image_generate; that starts a new, separately-billed job. If it's still pending, call job_status again with the same handle.

Step 7: Return

Once every image has finished (rejoin any pending ones via job_status first), share the resulting image URL(s) and local file path(s) with the user.

Edge cases

• Fits no kind → use nano-banana-2 (read references/prompt-nano-banana.md) or call

list_image_models.

• Fits no format → skip Step 2; the prompt guide alone is enough.
• Safety/NSFW rejection → name a workable stand-in for whatever tripped the filter (cover the

wardrobe, change the setting) and resubmit. A second rejection: tell the user which element is blocked instead of retrying blind.

• Still generating (status: "pending") or the call times out → the image is still rendering on

the server, not a failure — do not resubmit (that starts a second billed job). If you got a pending handle, call job_status with it to rejoin; if the call timed out with no handle, wait and tell the user it's still processing rather than firing a fresh generation.

• Generic failure (an explicit error result, not a timeout) → read the error. If it names a

parameter or a limit, correct that and resubmit. Otherwise resubmit once; if it fails again, give the user the error text rather than guessing.

• reference_images rejected on count → the error states the model's limit; drop to it.
• error: "no_provider_configured" → relay the tool's hint (the user must set their key).

Reference

Prompt guides — read the one for the chosen model:

• references/prompt-gpt-image-2.md — labeled blocks, verbatim text rendering, reference identity.
• references/prompt-nano-banana.md — instruction-following (subject/scene → style → instructions → constraints).
• references/prompt-seedream.md — instruction-style editing, compositing, and preservation.
• references/prompt-flux.md — linear prompt sequence, no negatives, camera/lens specs.
• references/prompt-grok.md — natural-language director style.

Format recipes — read only if the deliverable matches:

• references/format-poster.md, references/format-portrait.md, references/format-cinematic.md.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: SupercmoHQ
• Source: SupercmoHQ/superCMO-skills
• License: Apache-2.0
• Homepage: https://getsupercmo.ai

Install and usage instructions live in the source repository linked above.