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Product Shots

Organization
motiful
product-shots

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.

Overview

Publishermotiful
Repositoryproduct-shots
Skill nameproduct-shots
Stars
121
Forks
18
Bundled files
9
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by motiful on GitHub. Read the source before you install it.

Installation

Install the Product Shots AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/motiful/product-shots/tree/main/skills/product-shots
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/motiful/product-shots.git /tmp/product-shots
mkdir -p .claude/skills
cp -r /tmp/product-shots/skills/product-shots .claude/skills/product-shots
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Shots in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Product Shots on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Product Shots is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

  1. Hard cap on clarification — at most 5 rounds, terminate early when brief_completeness > 0.8. Never loop indefinitely.
  2. Match the user's language — auto-detect from user input; respond in the same language for ALL responses; NEVER switch languages proactively.
  3. 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.
  4. One dimension per question — each round asks one and only one dimension. NEVER chain several questions in one turn.
  5. 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.
  6. Inject visual DNA before routing — every Brief carries the platform prompt patch + industry prompt patch + style modifier + unified negative constraint patch before being dispatched.
  7. 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.
  8. 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).
  9. Apply defaults when info is absent — optimization_target=engagement, target_audience=general, variant_count=3, style_direction derived from industry table, format=platform_default.
  10. 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:

xml
<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 skillRouting condition
product-shots-main-imageE-commerce main / secondary image (1:1 carousel) — non-ad
product-shots-detail-pageAmazon A+ Content / 21:9 Hero Banner / 3:2 module
product-shots-multi-angleApparel / accessory model 9-angle consistency series
product-shots-ad-creativeasset_type == 'ad' OR is_promotion == True (across IG, FB, TikTok, LinkedIn, Google, YouTube, Pinterest, X)
product-shots-social-postOrganic 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:

FieldRequiredFormat
brand_colorsrecommendedhex codes or named palette
brand_fontsrecommendedfont family names (1 headline + 1 body)
brand_logorecommendedfile path or URL
brand_voiceoptionaltone descriptors (e.g., "warm / authoritative")
sample_assetsoptional1-3 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.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Product Shots AI skill do?

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", "...

Why use Product Shots on TypingMind?

Because you install it once and use it with any model. Product Shots is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Product Shots in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/motiful/product-shots/tree/main/skills/product-shots. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Product Shots?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Product Shots?

As many as you like. As long as a model supports skills, you can use Product Shots with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Product Shots AI skill free?

Yes. It is published on GitHub by motiful under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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