--- name: design-engine description: Generate on-brand visual assets with AI image models: surreal editorial graphics for social posts, quote cards, newsletter headers, and memes, produced as scored candidates with a selection record. Use when the user says "make a post graphic", "newsletter header", "image for this hook", or "brand visuals". Not for LinkedIn carousel decks: use linkedin-carousel-generator or linkedin-design. Not for templated meme-format batches: use viral-asset-templates. license: MIT metadata: author: TechTide AI (Alex Cinovoj) provenance: original category: Design & Motion --- # Design Engine A post graphic has one job: stop the scroll before the copy is read. Generate multiple candidates against a locked visual style, score them, pick one, and record why. Never post a candidate that fails the style gate, and never fall back to text-only silently. ## Inputs Confirm before generating: the `hook` (first-line idea of the post), the `thesis` (the lesson or take behind it), the asset type (post graphic, quote card, newsletter header, meme), and the aspect ratio (default 4:5 for feed posts, wide for newsletter headers). Providers and models come from configuration (``, ``, ``), never hardcode them, model catalogs churn. ## Workflow 1. **Derive the metaphor.** Turn the hook and thesis into 3-5 distinct visual metaphors, one clear metaphor per candidate. The image must visually explain the hook, not decorate it. 2. **Write the prompts.** Apply the locked scene language and hard rules. Read references/visual-style.md before writing prompts: it holds the scene language, palette, and rejection rules. Non-negotiables: no text overlay, no logos, one metaphor per image. 3. **Generate candidates.** 3-5 images through the primary provider; on provider failure, retry through the fallback. Vertical output on some endpoints requires an explicit image size parameter, not just an aspect-ratio field; verify dimensions on the first return. 4. **Score every candidate** on the rubric: reference match (feels like the locked style, not generic AI art), metaphor clarity, scroll-stop strength, composition (clear subject, strong depth), brand fit, cleanliness (no text artifacts, broken anatomy, or model noise). 5. **Select or reject.** Pick one winner. If all candidates fail the reference-match gate, regenerate with a different model or provider. If they still fail, log the failure and stop. Do not let a text-only post ship as a silent fallback, and do not ship "close enough". 6. **Record the run.** Save under `design-engine///`: all candidates, `selection.md` (scores, winner, reason), `selection.json` (selectedPath, reason, scores, for automation), and a provider run log (provider, model, request id, latency, status, errors). 7. **Hand off** the selected path to the posting workflow. While the style is still being locked with the owner, require owner approval on the next few selections before autonomous use. ## Verification Open the selected image at full size. Expect: no rendered text or logo fragments, no broken hands or faces, a subject readable at thumbnail size, and dimensions matching the requested ratio. Then check `selection.json` parses and its `selectedPath` exists on disk. If any check fails, the selection record is invalid: rescore or regenerate before handoff. ## Good vs Bad Judgment call: hook is "Agent drift will embarrass a lot of teams." **Bad prompt:** "Futuristic AI dashboard with glowing neural network, robot hand, cyberpunk city, dramatic lighting." Generic AI wallpaper, zero metaphor, instant rejection. **Good prompt:** "A vast coastal highway veering imperceptibly off its painted lane toward a cliff edge, cinematic matte-painting style, cobalt sky, hot coral accents, extreme scale, one tiny maintenance figure looking away." One metaphor (drift), locked palette, expensive and strange. ## Footguns - **Prompting the topic instead of a metaphor.** "AI agents working" produces stock-photo mush. Fix: force one concrete visual metaphor per candidate before prompting. - **Trusting the aspect-ratio field.** Some endpoints ignore it and return landscape. Fix: pass explicit dimensions and check the first output's size. - **Style drift across a week of posts.** Each session re-invents the look and the feed turns incoherent. Fix: prompts pull scene language only from references/visual-style.md; propose style changes there, not inline. - **Silent text-only fallback.** A failed generation quietly becomes a plain post and nobody notices the pipeline broke. Fix: failure is logged and surfaced, the posting workflow decides, not this skill. ## Completion Checklist - [ ] Hook, thesis, type, and ratio confirmed - [ ] 3-5 candidates generated, one metaphor each - [ ] Every candidate scored on the full rubric - [ ] Winner passes the reference-match gate, or failure logged and surfaced - [ ] selection.md, selection.json, and provider run log saved - [ ] Verification checks passed on the winner Any box unchecked: not done. Fix or say so.