marketing

Marketing production with genmedia

Use this skill when the user wants a campaign system, not one isolated asset. Load references as needed:

• references/campaign-patterns.md
• references/workflows.md
• references/examples.md

Load model-routing alongside this skill for default endpoint choices.

Marketing output should be executable: a brief, asset matrix, genmedia command plan, downloaded files, and clear defects. Do not add fake claims, fake social proof, fake partner logos, fake award badges, or invented legal copy.

Inputs to collect

Only ask when missing details would change the asset plan.

• Objective: launch, acquisition, retargeting, activation, education, event.
• Audience: persona, market, use case, awareness level, objections.
• Offer: product, feature, bundle, trial, waitlist, event, promotion.
• Channels: paid social, organic social, landing page, email, display, app.
• Required assets: stills, video, thumbnails, hero image, carousel, banner.
• Brand rules: colors, logo use, typography, tone, taboo visuals, competitors.
• Source media: product images, screenshots, app UI, people, logo, prior ads.
• Claims: exact approved copy, proof points, disclaimers, compliance limits.
• Budget preference: final-quality, draft exploration, or mixed pipeline.

Genmedia workflow

1. Start from routed endpoint IDs.

   genmedia models --endpoint_id openai/gpt-image-2 --json
   genmedia models --endpoint_id fal-ai/nano-banana-pro/edit --json
   genmedia models --endpoint_id fal-ai/nano-banana-2 --json
   genmedia models --endpoint_id bytedance/seedance-2.0/image-to-video --json
   genmedia models --endpoint_id bytedance/seedance-2.0/text-to-video --json
   genmedia models --endpoint_id veed/fabric-1.0 --json

Use text search only as fallback discovery for a missing channel role:

   genmedia models "marketing banner image typography" --json
   genmedia models "social ad video product campaign" --json
   genmedia docs "image generation text rendering" --json

1. Inspect the selected endpoint before planning exact payloads.

   genmedia schema <endpoint_id> --json
   genmedia pricing <endpoint_id> --json

1. Upload all source media.

   genmedia upload ./product.png --json
   genmedia upload ./logo.svg --json
   genmedia upload ./screenshot.png --json
   genmedia upload ./creator.jpg --json

1. Build the campaign matrix before generating.

Minimum matrix:

• Hook asset: earns attention in the first frame.
• Proof asset: shows product, result, process, feature, or evidence.
• Context asset: places product in audience use case.
• Conversion asset: clean end frame with safe space for external copy.

1. Run still assets synchronously when quick.

   genmedia run <endpoint_id> \
     --prompt "<marketing asset prompt>" \
     --download "./outputs/marketing/{request_id}_{index}.{ext}" \
     --json

1. Run video assets async and download from status.

   genmedia run <endpoint_id> \
     --prompt "<campaign video prompt>" \
     --async \
     --json

   genmedia status <endpoint_id> <request_id> \
     --download "./outputs/marketing/{request_id}_{index}.{ext}" \
     --json

1. Use schema fields exactly. Do not pass guessed flags. Mirror names such as

image_urls, image_url, reference_image_url, aspect_ratio, duration, quality, seed, prompt, or negative_prompt.

Brief build order

Build the campaign brief before prompts:

1. Product truth: what the product is and what must stay accurate.
2. Audience tension: problem, desire, objection, or trigger moment.
3. Campaign promise: user-approved benefit or visible outcome.
4. Asset roles: hook, proof, context, conversion, retention, reminder.
5. Channel specs: crop, runtime, safe zones, text needs, file count.
6. Variation axis: audience, setting, proof point, visual metaphor, offer.
7. Guardrails: claims, logo, legal, copy, product fidelity, banned imagery.

Prompt build order

Write every asset prompt in this order:

1. Asset role and channel: launch hero, Meta ad, TikTok hook, email header.
2. Product or subject invariant: exact product, UI, feature, person, logo rule.
3. Audience context: where, who, problem state, usage moment.
4. Visual system: camera, lighting, color, composition, texture, motion.
5. Copy handling: no generated text, safe space, or exact provided wording.
6. Variant axis: what differs from the other assets.
7. Guardrails: no fake claims, no extra logos, no distorted product or UI.

Model routing

• Text-heavy campaign key art, posters, landing heroes, app/UI visuals, and

ads with exact copy: openai/gpt-image-2 at quality=high.

• Premium product or brand stills: openai/gpt-image-2, then

fal-ai/nano-banana-pro, then fal-ai/nano-banana-2.

• Edits from source assets: fal-ai/nano-banana-pro/edit, then

openai/gpt-image-2/edit, then fal-ai/bytedance/seedream/v5/lite/edit.

• Fast visual exploration: fal-ai/flux-2/klein/9b.
• Product reveal or social video: bytedance/seedance-2.0/image-to-video.
• Text-to-video concept or brand film: bytedance/seedance-2.0/text-to-video.
• Creator or spokesperson ad: load ugc and use veed/fabric-1.0,

veed/fabric-1.0/text, or fal-ai/creatify/aurora.

• Multi-shot narrative: load storytelling.
• Single polished product asset: load commercial.

Quality bar

Before returning:

• Every asset maps to a campaign role and channel.
• Product, UI, logo, and packaging are stable.
• Claims are user-supplied, observable, or removed.
• Text is either exact and schema-supported or reserved for external editing.
• Variants differ by one clear axis, not random style drift.
• Safe zones support overlays, cropping, and platform UI.
• Output paths come from downloaded_files[].
• The final answer includes a campaign manifest: asset role, endpoint, request

id, local path, prompt summary, and defects.

If the campaign feels generic, reduce the asset count and make each role more specific before generating more variants.