Higgsfield product photoshoot

/higgsfield-product-photoshoot — Brand Product Image Generation

Brand-image generation via the higgsfield product-photoshoot create command. The CLI calls a backend prompt enhancer that holds mode-specific photography vocabulary and structural templates, then submits to gpt_image_2 and returns image URLs.

When to use

• Any product visual with a specific output format: studio shot, Pinterest pin, hero banner, ad pack, carousel, model try-on
• User has a product photo and wants it adapted to a specific marketing context
• "make ads for my product", "make a hero banner", "create carousel images"
• "virtual try-on", "model wearing my jacket", "levitating product shot"
• Paid social creative packs (Meta, TikTok, Pinterest, Google Ads)

Route elsewhere if:

• No product, no brand context, just a generic image prompt → image-gen (Gemini, free, faster)
• User needs a branded video ad with an avatar → higgsfield-generate (Marketing Studio)
• User wants to train a reusable face identity → higgsfield-soul-id
• User needs general-purpose AI image/video generation → higgsfield-generate

On Activation

1. Read brand/voice-profile.md, brand/visual-style.md, and brand/creative-kit.md if present. Use brand colors, aesthetic language, and platform preferences to inform mode selection and interview answers.
2. Check CLI: higgsfield account status. If not on $PATH, surface install command. If session expired, prompt auth.
3. Run the pre-generation interview (see below) — at most 4 questions before submitting.

Optional dependency — Higgsfield account

This skill requires the @higgsfield/cli binary and a Higgsfield account.

Without the CLI installed, return a clear actionable error:

higgsfield CLI not found. Install with:
  curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh
Then authenticate:
  higgsfield auth login

Without an authed Higgsfield account, the CLI itself surfaces the auth prompt — no special handling needed in the skill.

Fallback for image generation only: if the user just needs a one-off image and doesn't have a Higgsfield account, route them to image-gen (Gemini, model gemini-3.1-flash-image-preview, free tier). The product-photoshoot mode enhancer and all product-specific modes require Higgsfield.

Step 0 — Bootstrap

Before any other command:

1. If higgsfield is not on $PATH, install it:

   curl -fsSL https://raw.githubusercontent.com/higgsfield-ai/cli/main/install.sh | sh
   

2. If higgsfield account status fails with Session expired / Not authenticated, ask the user to run higgsfield auth login (interactive) and wait for confirmation.

UX Rules

1. Be concise. Print only image URLs in the final reply.
2. Detect language, respond in it. Mode names and CLI flags stay English.
3. Ask at most 4 short questions before submitting. Use labeled options, never open-ended.
4. Skip questions whose answer is obvious from context (uploaded image, prior turn, brand memory).
5. Never write the gpt_image_2 prompt yourself — backend assembles it.
6. Polling is silent. Wait until URLs are ready, then deliver.

Modes

Mode	When user wants…
product_shot	Product on neutral / studio / catalog background
lifestyle_scene	Product in real-world environment, hands, action, atmosphere
closeup_product_with_person	Tight crop with hands / partial face — beauty application, holding, demonstrating
moodboard_pin	Vertical 2:3 Pinterest-native aesthetic, moodboard feel
hero_banner	Wide-format website / email / campaign header
social_carousel	3–10 connected slides for IG / LinkedIn / Facebook
ad_creative_pack	Coordinated pack of static ad variants for Meta / TikTok / Pinterest / Google Ads
virtual_model_tryout	Product worn or used by an AI-rendered model
conceptual_product	Surreal / CGI-style / levitating / splash / sculptural product
restyle	Transform an existing image's aesthetic, mood, or seasonal context

Mode selection

Pick by intent, not surface keyword. When two modes could apply, prefer the more specific one.

• product + neutral / clean / white / studio / catalog / Shopify → product_shot
• product + scene / in use / kitchen / outdoor / cafe / gym → lifestyle_scene
• hands holding / face with product / beauty application / demonstrating → closeup_product_with_person
• Pinterest, pin, vertical pin → moodboard_pin
• hero, banner, website header, landing page, email header, wide format → hero_banner
• carousel, slide post, multi-slide, swipeable → social_carousel
• ads, ad pack, paid social, Meta / TikTok / Pinterest ads → ad_creative_pack
• model wearing, virtual try-on, on body, fashion shoot, lookbook → virtual_model_tryout
• levitating, floating, splash, frozen motion, surreal, CGI, sculptural → conceptual_product
• modify EXISTING image's aesthetic, mood, season — without changing subject → restyle

Tie-breakers:

• "Pinterest pin of my product on a kitchen counter" → moodboard_pin (Pinterest is the platform)
• "Hero banner showing my product in use" → hero_banner (banner format wins)
• "Carousel of my product in different scenes" → social_carousel (multi-slide wins)
• "Closeup of person applying my serum" → closeup_product_with_person (specific genre wins)

Pre-generation interview

Ask 3–4 short questions before submitting. Always labeled options, never open-ended. Skip a question whose answer is obvious from context.

Type A — uploaded a product photo, "make me images / photoshoots"

1. How many? [1 / 3 / 5]
2. What style/mood? [Clean studio / Lifestyle / Conceptual / With a model / Other]
3. Where will you use them? [Shopify / Instagram / Pinterest / Paid ads / Website hero]
4. Brand colors to match? (skip if obvious)

Type B — uploaded a product photo, named a use case

E.g. "make ads for my product", "make a Pinterest pin", "make a hero banner". Mode is obvious. Ask only the gaps:

1. How many? (if multi-output mode)
2. What's the offer / mood / hook?
3. Anything in particular to emphasize?

Type C — text only, no product photo

1. Can you upload a product photo? (preferred — much higher fidelity)
2. If not, describe the product — category, packaging, color, distinctive features.
3. What style? (same options as Type A)
4. Where will you use it?

Type D — uploaded existing image, "redo / change vibe / different version"

→ restyle

1. What aesthetic? [Clean girl / Cottagecore / Quiet luxury / Dark academia / Y2K / Other]
2. Seasonal context? [Christmas / Valentine's / Halloween / Black Friday / None]
3. What to preserve, what to change? (only if ambiguous)

Type E — model wearing a product (fashion, accessories)

→ virtual_model_tryout

1. Model archetype? (suggest 2–3 based on brand audience)
2. Environment? [Studio clean / Outdoor natural / Street style / Editorial / Home cozy]
3. Framing? [Full body / Three-quarter / Waist up / Closeup on product area]

Type F — vague request, unclear subject

E.g. "make me something cool for my brand".

1. What product or topic?
2. Goal? [Sell on a marketplace / Build awareness / Run paid ads / Update website]
3. Upload a reference image?

After answers → return to the relevant Type A–E.

Generation

Single command. Backend assembles the final prompt and submits to gpt_image_2. URLs print on stdout.

higgsfield product-photoshoot create \
  --mode <mode> \
  --prompt "<short user-intent description from interview answers>" \
  [--image <path-or-upload-id>]... \
  [--count <1-10>] \
  [--aspect_ratio <override>]

Examples:

higgsfield product-photoshoot create \
  --mode lifestyle_scene \
  --prompt "bottle of cold-brew on a sunlit kitchen counter, IG feed" \
  --image bottle.jpg \
  --count 3

higgsfield product-photoshoot create \
  --mode moodboard_pin \
  --prompt "vertical pin for my candle brand, cottagecore mood" \
  --image candle.jpg

higgsfield product-photoshoot create \
  --mode restyle \
  --prompt "Christmas version, quiet-luxury aesthetic" \
  --image existing-shot.jpg

Image inputs

--image accepts a local file path (auto-uploaded) OR an existing upload UUID. Repeat the flag for multiple references.

Multi-variant

--count 3 returns 3 distinct image URLs. Backend asks the enhancer to vary preset, lighting, angle, and palette across variants — they will not be paraphrased copies of one another.

For social_carousel and ad_creative_pack, count = number of slides / variants in the pack. Backend locks the visual system across all slides automatically.

Aspect ratio

Backend picks a sensible default per mode. Override with --aspect_ratio only if the user explicitly asks for a different one. Allowed values: 1:1, 4:5, 5:4, 3:4, 4:3, 2:3, 3:2, 9:16, 16:9.

Resolution

Use 2k for every product-photoshoot job.

Delivering results

Print the image URLs as a short bulleted list. No JSON, no IDs, no internal model names, no enhanced prompt text. If a job failed, mention it briefly with the failure status.

3 lifestyle shots ready:
- https://cdn.higgsfield.ai/.../job_abc.jpg
- https://cdn.higgsfield.ai/.../job_def.jpg
- https://cdn.higgsfield.ai/.../job_ghi.jpg

Anti-Patterns

Anti-pattern	Why it fails	Instead
Calling higgsfield generate create gpt_image_2 --prompt ... directly	Bypasses the mode-specific prompt enhancer. Output quality for product shots is noticeably lower — wrong vocabulary, wrong structural guidance.	Always use higgsfield product-photoshoot create with a mode. The enhancer is the point of this skill.
Asking more than 4 interview questions in a single message	Users stall. The interview is a funnel, not a form.	Max 4 short labeled-option questions per turn. Skip anything that's obvious from context or brand memory.
Picking the wrong mode	product_shot for a Pinterest pin crops wrong, picks wrong aspect ratio.	Mode selection drives the enhancer's vocabulary. Use the tie-breaker rules in the Mode Selection section.
Pasting the assembled prompt back to the user	They don't want the enhancer's output; they want the image URLs.	Deliver only URLs.
Using a --mode value not in the table	The CLI rejects unknown mode strings.	Stay within the 10 documented modes.
Routing here for a generic one-off image with no product	Overkill. Slower. Higgsfield account required.	Use image-gen (Gemini, free tier) for generic images without a product or brand mode.

Attribution

Ported from higgsfield-ai/skills — MIT License, Copyright (c) 2026 Higgsfield AI. Adapted for mktg's drop-in contract on 2026-05-05.

Upstream version: 0.3.0 Upstream commit: 1dcfe2687c3a9092232bac55c2b6b9ae3fc717d7

Drift detection: if the upstream skill changes, re-run mktg-steal https://github.com/higgsfield-ai/skills to evaluate the diff.