# Marketing > Plan and run campaign-level marketing asset production with genmedia. Use this for launch kits, campaign matrices, paid social variants, landing-page visuals, email and banner imagery, hook/proof/conversion assets, creator ad packages, and channel-specific marketing deliverables. - Skill: `fal-ai-community/marketing` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add fal-ai-community/marketing` - Raw SKILL.md: https://api.skillmd.com/api/skills/fal-ai-community/marketing/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: fal-ai-community (https://skillmd.com/u/fal-ai-community) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/fal-ai-community/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. ```bash 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: ```bash genmedia models "marketing banner image typography" --json genmedia models "social ad video product campaign" --json genmedia docs "image generation text rendering" --json ``` 2. Inspect the selected endpoint before planning exact payloads. ```bash genmedia schema --json genmedia pricing --json ``` 3. Upload all source media. ```bash genmedia upload ./product.png --json genmedia upload ./logo.svg --json genmedia upload ./screenshot.png --json genmedia upload ./creator.jpg --json ``` 4. 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. 5. Run still assets synchronously when quick. ```bash genmedia run \ --prompt "" \ --download "./outputs/marketing/{request_id}_{index}.{ext}" \ --json ``` 6. Run video assets async and download from status. ```bash genmedia run \ --prompt "" \ --async \ --json genmedia status \ --download "./outputs/marketing/{request_id}_{index}.{ext}" \ --json ``` 7. 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.