Update to Pixara

{
  "description": "A coordinated static ad set from one product and one message: product intake, a headline under eight words from the user’s own claims, one key visual approved first, then hooks and sizes as variants with the headline rendered inside the image on a text-rendering model, the logo exact, spelling checked. Use when: paid-ad creatives with a product and a message; a static ad set, Meta or TikTok ad images, a billboard or out-of-home visual, a seasonal campaign key visual, a menu or poster with the product, banner ads in several sizes. Not for: organic posts with no product (social-graphics), product stills with no message (product-photoshoot), a video ad (product-reel), listing images (amazon-listing), a thumbnail (thumbnail).",
  "included_files": [],
  "name": "ad-creative",
  "skill_md_contents": "---\nname: \"ad-creative\"\ndescription: \"A coordinated static ad set from one product and one message: product intake, a headline under eight words from the user’s own claims, one key visual approved first, then hooks and sizes as variants with the headline rendered inside the image on a text-rendering model, the logo exact, spelling checked. Use when: paid-ad creatives with a product and a message; a static ad set, Meta or TikTok ad images, a billboard or out-of-home visual, a seasonal campaign key visual, a menu or poster with the product, banner ads in several sizes. Not for: organic posts with no product (social-graphics), product stills with no message (product-photoshoot), a video ad (product-reel), listing images (amazon-listing), a thumbnail (thumbnail).\"\n---\n\n# Ad creative\n\nProduce a coordinated set of static ad visuals. One product, one message, one key visual approved first, then the variants. The words live inside the image and are checked letter by letter.\n\n## Rules that never bend\n\n- The headline is under eight words and comes from the user's claims or their own line; a number, a price or a time appears only when the user wrote it. Body copy under twenty words when present. Nothing invented about the product, the offer or the results.\n- Text is rendered inside the image on a text-rendering model (google/nano-banana-pro at 2K for 4:5, 9:16 and every wide format; openai/gpt-image-2 only at 1:1, 2:3 or 3:2, the only aspects it renders here) with the exact string quoted and its placement named; spelling is checked on every render and a misspelling is re-rendered, never accepted.\n- The product is passed in the reference image field on every render and keeps its identity (the staging contract from intake); the logo is passed as a second reference when the brand has one and reproduced exactly.\n- One key visual (the idea, the palette, the composition) is approved before any variant; variants change the hook, the format or the size, never the idea.\n- Platform safe zones: at 9:16 the headline sits in the middle third; at 1:1 and 4:5 nothing important sits in the outer five percent; a billboard keeps the headline readable at a glance from the far side of a road.\n\n## Intake, only for the gaps\n\nParse the brief first, then ask in one message only for what it left out: the product (a photo to upload, a URL, or a description), the message or offer, the audience, the formats (1:1, 4:5, 9:16, a billboard at 3:1 or wider), the count, the season or occasion when the campaign has one, the brand palette and logo when there are any. Ask for a missing message when the brief carries no line and no claims, and for a missing product. Never ask about models.\n\n## Phases\n\n1. Product intake. Look at the product photo inline (or read the description) and write a one-paragraph staging contract you reuse verbatim: the object type and form, material and finish, colours, the label state (the exact printed text if legible, otherwise \"blank\" or \"unreadable\"), its size next to a hand, how it opens or is used, one honest imperfection. Lock the angle to the side the photo shows. Lock the claims as a short allowlist.\n2. Message. Write the headline and the optional body in two lines; show them; a nod proceeds.\n3. Key visual. One generate_image at the primary format on google/nano-banana-pro at 2K with the product (and the logo) in imageUrls; verified: the headline rendered exactly on the first try at 4:5 and again on every variant. Open with the manifest (\"image 1 is the product: same label, same visible side; image 2 is the logo, exact shape and colours\"), then the scene that dramatises the message, the product large and sharp, the headline as an exact string in quotation marks with its placement and the safe zone, the logo where the brand puts it, the palette, the light with a direction, the mood in two words; then the negatives: no extra product, no invented label text, no stock-photo gloss, no HDR glow, no lens flare, no watermark, no other text. Wait, look at it inline, read the text back letter by letter. Refine once at most with \"change only X, keep everything else exactly\" on the same model. The user approves it.\n4. Variants. One wave with generate_batch, the approved key visual passed as a reference and declared as the style and product reference beside the product photo: the other hooks when a set was asked (the problem, the mechanism, the result, a comparison of visible attributes only, the lifestyle) at the primary format, and the other formats by re-composing the same scene at each aspect, never by cropping text away. Each prompt repeats the manifest, the palette lines and the exact headline; only the hook or the aspect changes. wait_for_jobs.\n5. Check. Look at every variant inline: the spelling of every word, the product's identity against the contract, the logo exact, legibility at phone size. One refinement per variant with \"change only\"; a misspelling is re-rendered.\n6. Delivery. The set grouped by hook then by format, each with its headline, a note on which claims were used, and one next step (a product reel from the key visual, a second headline for a test).\n\n## Pixara tool mechanics\n\n- Discover: recommend_model for a goal, list_models for a service or a capability, describe_model for the exact parameters, the constraints and the price. The models named in this recipe are the ones verified for it; if one has left the catalog, take the newest model with the same tags.\n- Run: generate_image, generate_video, generate_voiceover, generate_music and the editing tools return a job id. wait_for_job(id) polls for up to 45 seconds; call it again until the job finishes. A failed job is refunded and its error names the constraint that was hit; fix that and retry once.\n- Waves: when several generations do not depend on each other (a shot list, a set of variants, the takes of a voiceover), queue them in one generate_batch call and wait for all of them with wait_for_jobs; one failing job never stops the others.\n- Results: every finished asset carries a URL and a key. Pass the URL as the input to the next generation (describe_model names the image, video and audio fields). Pass the KEY to assemble_video, and call get_media_metadata(key) for exact durations before sizing clips.\n- Inputs from the user: request_upload then finalize_upload (or upload_widget) for a local file, upload_asset for a URL, list_generations to reuse earlier work.\n- Money: prices are per model in list_models; estimate_cost before anything expensive; confirm before choosing a model that costs several times more than an alternative.\n- Seeing: images render inline, so look at every image before building on it. You cannot watch video here: when a clip finishes, tell the user what to check (face, hands, lips, unwanted text) and let them confirm before assembly, unless they asked for no check-ins.\n- Words on screen are minimal and live inside the picture: a title, a step name or a price is a designed frame from a text-rendering image model (google/nano-banana-pro at any aspect; openai/gpt-image-2 only at 1:1, 2:3 or 3:2, the only aspects it renders here) with the exact string, animated with image-to-video or placed as an image clip; the assembler's text clip is the last resort for a plain line nothing else can carry. Never ask a generation model to render text you did not receive verbatim from the user.\n- Captions never come from the assembler or a video prompt: when a piece has speech and the user wants captions, run the finished master through the subtitles tool on zapcap/subtitles with the language, a style as preset when the user wants a particular look (list_caption_styles shows them with previews; the default is clean plain text), position only when the style's own placement does not fit (a percentage of the height from 0 at the top to 80 at the bottom, around 70 for a 9:16 talking piece), and the authored script as referenceTranscript when one exists: one entry per cue with text, startTime and endTime in seconds, which keeps the script's spelling while word timing still comes from the audio. veed/subtitles is the fallback only: when ZapCap refuses the video (no audio track, longer than 30 minutes), when its job fails (one retry on VEED, never a second ZapCap run), when the captions are authored text on a piece with no speech (VEED burns them from srtContent), or when the user picked a VEED style. Never caption one piece with both. The clean master stays the master; the captioned file is the deliverable.\n- minimax/hailuo-3-max: resolution is the string \"768P\" exactly, capitals, its only value (never 480P, and a lowercase 768p is rejected); duration 5 to 15; referenceImageUrls holds at most nine.\n- Voices: eleven-labs/v4 for every voiceover (a voiceId from list_voices); minimax/speech-2.8 only when it fails; bytedance/seed-audio-1.0 for voice replication from a sample the user owns, a two-voice dialogue in one take, or spoken lines with sound effects.\n- Talk to the user in model display names, never ids, and never narrate these mechanics.\n"
}