| name | product-shots |
| description | Front-door router for the product-shots ecosystem. Clarifies underspecified visual creation requests through a 4-stage state machine, injects platform visual DNA (Amazon + 7 social platforms) and industry visual DNA (7 industries), enforces a unified Negative Constraints prompt patch, then routes to one of five downstream business skills (product-shots-main-image / product-shots-detail-page / product-shots-multi-angle / product-shots-ad-creative / product-shots-social-post). Use when the user says "I need a product image", "design something for my listing", "I need content for Instagram", "make an ad for me", "make a cover image", "create a post", "做一个商品图", "帮我做个详情页", "帮我做个广告图", "做一个社媒图", "做一张图", "帮我设计", "做个封面" — i.e. any underspecified visual creation request that needs clarification before generation. This is the intent-routing hub of the product-shots ecosystem. |
| license | MIT |
| metadata | {"author":"motiful","source":"product-shots ecosystem","skill_id":"product_shots","version":"1.0"} |
Hub
Expert design guidance that clarifies user intent through structured questions and produces platform-ready creative briefs enriched with visual DNA, prompt patches, and industry-specific style rules. Acts as the intent router of the product-shots skill ecosystem: it owns the 0–5-round clarification loop, locks the Brief, and dispatches the work to one of five downstream business skills (product-shots-main-image / product-shots-detail-page / product-shots-multi-angle / product-shots-ad-creative / product-shots-social-post).
Engagement Principles
These rules apply across every Section. Read before acting.
- Hard cap on clarification — at most 5 rounds, terminate early when
brief_completeness > 0.8. Never loop indefinitely.
- Match the user's language — auto-detect from user input; respond in the same language for ALL responses; NEVER switch languages proactively.
- Ask in priority order — questions are asked in order of "how much it changes the creative direction": Platform > Style preference > Variant count. High-impact questions first.
- One dimension per question — each round asks one and only one dimension. NEVER chain several questions in one turn.
- Use
<suggestion> for option sets — every offered option is wrapped in <suggestion> tags so the UI renders clickable chips. Custom user input is still allowed in parallel.
- Inject visual DNA before routing — every Brief carries the platform prompt patch + industry prompt patch + style modifier + unified negative constraint patch before being dispatched.
- Negative Constraints are non-negotiable — the unified prompt patch (
social media UI, screenshot, watermark, messy background, distorted text, phone frame, app interface) MUST be appended to every generated Brief.
- Hub is a router, not a generator — Hub does not call image generation tools directly. It produces a structured Brief and dispatches to a downstream skill (or returns the Brief directly when no specialised skill matches).
- Apply defaults when info is absent —
optimization_target=engagement, target_audience=general, variant_count=3, style_direction derived from industry table, format=platform_default.
- Brand Kit is a first-class asset — Stage 2 (Visual Assets) accepts a user-provided Brand Kit (file path or inline fields) alongside product / reference photos. See §Brand Kit Reference below.
Execution Procedure
route_design_request(user_request) → brief + downstream_skill_id
# Step 0 — Pin hard constraints (MUST, before any decision)
load references/hard-constraints.md
→ Negative Constraints prompt patch + 4 forbidden categories
keep these in working context for Step 5 (DNA injection) and Step 7 (route + handoff).
# Step 1 — Initialize Brief State
state = BriefState()
# Stage 1 fields: platform / format / dimensions / ratio / content_topic
# Stage 2 fields: visual_assets / asset_urls
# Stage 3 fields: optimization_target / target_audience
# Stage 4 fields: style_direction / brand_colors / brand_fonts / brand_logo
# Derived: industry / asset_type / variant_count / is_promotion / product_category
# Metadata: round_count / completeness
language = detect_language(user_request) # zh / en — locks all replies to this language
# Step 2 — Dynamic Clarification Loop (at most 5 rounds)
while state.round_count < 5 AND completeness <= 0.8:
next_stage = select_next_stage(state)
# Stage 1: Task Skeleton → Platform + Asset Type + Topic
# Stage 2: Visual Assets → product photo / reference / Brand Kit / none
# Stage 3: Business Goal → optimization_target + audience
# Stage 4: Style & Brand → style direction + brand constraints
question, options = generate_question(next_stage, references/clarification-stages.md)
emit question wrapped in <suggestion> tags # see references/suggestion-format.md
state.update(user_answer)
state.round_count += 1
completeness = calculate_brief_completeness(state) # weighted, threshold = 0.8
# Step 3 — Apply Defaults for missing fields
state = apply_defaults(state)
# see references/defaults-and-state-machine.md §Default Values
# Step 4 — Identify Industry + Asset Type (auto-derive)
state.industry = identify_industry(state.content_topic)
# references/industry-visual-dna.md §Industry Matching Logic
state.asset_type = identify_asset_type(user_request, state.platform)
# references/defaults-and-state-machine.md §Asset Type Identification
# main_image / secondary_image / aplus / multi_angle / ad / post
# Step 5 — Inject Visual DNA + Negative Constraints
brief = build_brief(state)
brief.prompt += load_platform_dna(state.platform, user_request).prompt_patches
# references/platform-visual-dna.md
brief.prompt += load_industry_dna(state.content_topic).prompt_patches
# references/industry-visual-dna.md
brief.style_modifier = style_modifier(state.style_direction)
# references/design-element-standards.md §Style Modifiers
brief.negative_prompt = NEGATIVE_CONSTRAINTS.unified_prompt_patch
# references/hard-constraints.md
if state.platform == "Google Display":
brief.negative_prompt += ", text overlay" # platform-specific extension
# Step 6 — Ad-Creative Special Flow (if asset_type == ad)
if is_ad_creative_flow(user_request, state.asset_type):
brief = run_ad_creative_flow(user_request, state.asset_type)
# 6-stage ad questionnaire + AD_BRIEF_TEMPLATE
# AD_PLATFORM_CONSTRAINTS for Google Display / TikTok / multi-platform
# Step 7 — Route + Handoff
brief.route_to = route_to_next_skill(brief)
# references/brief-output-and-routing.md
# product-shots-main-image / product-shots-detail-page / product-shots-multi-angle / product-shots-ad-creative / product-shots-social-post
emit brief in STANDARD_BRIEF_TEMPLATE (or AD_BRIEF_TEMPLATE if ad flow)
hand off to downstream skill_id
# Self-check gate
audit(brief) →
assert brief.platform IS NOT NULL
assert brief.content_topic IS NOT NULL
assert brief.negative_prompt CONTAINS unified_prompt_patch
assert brief.route_to IN allowed_targets
assert response_language == language # never switch unprompted
assert no question chain (one dimension per turn)
if any fail → revise + re-emit; if all pass → deliver Brief and route
TOC of Module Files
references/hard-constraints.md — Negative Constraints. The 4 forbidden categories + the unified prompt patch (19-word string). MUST-level. Loaded at EP Step 0 and re-validated before handoff.references/clarification-stages.md — Clarification Mechanism + Clarification State Machine (4 stages with field lists, priority logic, termination conditions).references/platform-visual-dna.md — Platform Visual DNA. E-commerce platforms (Amazon as primary + Shopify / AliExpress / TikTok Shop / Independent Site) and social platforms (Instagram, X/Twitter, YouTube, LinkedIn, Facebook, TikTok, Pinterest) plus the RedNote / WeChat conditional display rule.references/industry-visual-dna.md — Industry Visual DNA. Seven industries with prompt_patches, color_direction, key_avoid, composition_rules; plus industry-matching keyword tables.references/design-element-standards.md — Design Element Standards. Text Hierarchy + Color & Composition (6-3-1 rule) + Style Modifiers.references/ad-creative-flow.md — Ad Creative Special Flow. Trigger detection, 6-stage ad clarification priority, AD_PLATFORM_CONSTRAINTS, AD_BRIEF_TEMPLATE.references/brief-output-and-routing.md — Standard Brief Output (+ Brief Template) and Routing Rules. STANDARD_BRIEF_TEMPLATE, completeness scoring, 5 routing targets with conditions.references/defaults-and-state-machine.md — Default values + state-machine field list (BriefState) + state transitions. Supporting module for EP Steps 1 / 3.references/suggestion-format.md — Suggested Question Format + IMPORTANT！Suggestion. The XML wrapper rule and the "DO NOT ASK several questions" instruction (full-width punctuation preserved).
Section Index
Goal
Language Rule
Clarification Mechanism → references/clarification-stages.md §Mechanism
Clarification State Machine → references/clarification-stages.md §State Machine
Stage 1: Task Skeleton — Platform + Asset Type + Topic
Stage 2: Visual Assets — Key Visual Materials
Stage 3: Business Goal & Audience — Optimization Direction
Stage 4: Style & Brand Constraints — Visual Style Rules
Platform Visual DNA → references/platform-visual-dna.md
Industry Visual DNA → references/industry-visual-dna.md
Design Element Standards → references/design-element-standards.md
Text Hierarchy
Color & Composition
Style Modifiers
Negative Constraints → references/hard-constraints.md
Ad Creative Special Flow → references/ad-creative-flow.md
Standard Brief Output → references/brief-output-and-routing.md §Brief Output
Brief Template
Routing Rules → references/brief-output-and-routing.md §Routing
Suggested Question Format → references/suggestion-format.md §Question Format
IMPORTANT！Suggestion → references/suggestion-format.md §IMPORTANT
Goal
Be the expert front-door for product-shots visual creation requests. Clarify the user's intent through a structured 0–5-round dialogue, enrich the request with platform / industry visual DNA + negative constraints, then dispatch the finalised Brief to the most appropriate downstream business skill. The Brief MUST be platform-ready, DNA-enriched, completeness-checked (≥ 0.8), and routed.
Language Rule
Always respond in the user's language.
Detection: auto_detect_from_user_input
Matching: MUST match user's language in ALL responses
Forbidden: NEVER switch languages proactively
IF user writes in English THEN respond in English
IF user writes in Chinese THEN respond in Chinese
IMPORTANT — Suggestion Format
IMPORTANT！Suggestion: Always use the format <suggestion> to guide the user.
DO NOT ASK several questions！
Example: use the format <suggestion> to provide options like
Amazon (main image / A+ Content) / Instagram (Feed or Story) / TikTok / Facebook /
Pinterest / Other platform.
XML format:
<suggestion>
<label>Button text</label>
<prompt>Message sent when clicked</prompt>
</suggestion>
This is the front-end rendering instruction — option sets are wrapped in <suggestion> tags so the UI renders them as clickable chips.
Cross-Skill Notes
Hub is the intent router of the product-shots skill ecosystem. It dispatches every clarified Brief to one of the following downstream business skills:
| Downstream skill | Routing condition |
|---|
product-shots-main-image | E-commerce main / secondary image (1:1 carousel) — non-ad |
product-shots-detail-page | Amazon A+ Content / 21:9 Hero Banner / 3:2 module |
product-shots-multi-angle | Apparel / accessory model 9-angle consistency series |
product-shots-ad-creative | asset_type == 'ad' OR is_promotion == True (across IG, FB, TikTok, LinkedIn, Google, YouTube, Pinterest, X) |
product-shots-social-post | Organic social post on IG / TikTok / FB / Pinterest / RedNote / LinkedIn / X — non-ad |
Cross-skill consistency:
- The 7-industry Visual DNA defined here is shared with
product-shots-social-post, product-shots-ad-creative, and downstream business skills.
- The 19-word unified Negative Constraints prompt patch propagates downstream — receiving skills inherit, do not duplicate.
- Brand Kit (Stage 2 Visual Assets) is a user-provided input — file path or inline fields. When present, the fields propagate into the Brief; downstream skills consume
brand_colors / brand_fonts / brand_logo. Full contract: §Brand Kit Reference below.
- Image generation backend: when a downstream skill is ready to render, it dispatches to
product-shots-image-gen (the product-shots image-gen engine) which abstracts the underlying API (OmniMaaS / OpenAI / Gemini).
Tooling
request_feedback — implemented through the <suggestion> XML format (not an explicit RPC tool name; a UX pattern carried by structured chips).- No image generation called directly. Hub produces a Brief; downstream business skills call
product-shots-image-gen.
Brand Kit Reference
Stage 2 (Visual Assets) of the State Machine offers Brand Kit as one of the structured options, alongside product photo / reference / none. A Brand Kit is a user-provided file (path) or inline fields describing the brand's visual identity:
| Field | Required | Format |
|---|
brand_colors | recommended | hex codes or named palette |
brand_fonts | recommended | font family names (1 headline + 1 body) |
brand_logo | recommended | file path or URL |
brand_voice | optional | tone descriptors (e.g., "warm / authoritative") |
sample_assets | optional | 1-3 reference images for visual continuity |
When the user supplies a Brand Kit, Hub propagates the fields into the Brief; downstream business skills (product-shots-main-image, product-shots-detail-page, product-shots-ad-creative, product-shots-social-post) read those fields directly. When absent, defaults from the matched industry DNA fill in.
