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muapi-ugc-lifestyle-try-on

Generate UGC-style (User Generated Content) lifestyle photos of a person wearing or using your product — authentic, relatable, social-media-native imagery.

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Download samuraigpt-generative-media-skills-library_motion_ugc-lifestyle-try-on-74df8cb.zip · 1 KB
Part of samuraigpt/generative-media-skills — 72 skills

Install

skills CLI npx skills add https://github.com/SamurAIGPT/Generative-Media-Skills/tree/main/library/motion/ugc-lifestyle-try-on
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install samuraigpt-generative-media-skills@llmmart
Git git clone https://github.com/SamurAIGPT/Generative-Media-Skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole samuraigpt/generative-media-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

UGC Lifestyle Try-On

Generate UGC-style (User Generated Content) lifestyle photos of a person wearing or using your product — authentic, relatable, social-media-native imagery.

Inputs

Name Type Required Default Description
product_name text yes — The product to feature (e.g. "white oversized hoodie", "blue light blocking glasses", "leather crossbody bag").
product_image image_url yes — Product image or photo URL to use as reference for the try-on.
model_description text no woman, 25-30 years old, natural look, diverse Description of the model (e.g. "man, athletic build, 20s", "woman, curvy, 30s, warm skin tone").
setting text no casual lifestyle, natural lighting Scene and mood (e.g. "urban street style", "cozy home morning routine", "gym workout", "coffee shop").
platform text no instagram Target platform — "instagram", "tiktok", "pinterest", "amazon".

Steps

Submit the plan with TWO steps:

Step 1 — Outfit/Product Try-On

  1. Try-on generation — muapi image edit (model=ai-dress-change) if product is wearable clothing, otherwise muapi image edit (model=flux-kontext-pro-i2i):
    • For clothing/wearables with ai-dress-change:
      • Product: {{product_image}}
      • Prompt: {{model_description}} wearing the product naturally in a {{setting}} environment. Authentic UGC-style photo, candid pose, natural expression.
    • For accessories/non-clothing with flux-kontext-pro-i2i:
      • Prompt: {{model_description}} using/wearing {{product_name}} in a {{setting}}. The product from the reference image is clearly visible and featured. Natural UGC-style lifestyle photography, authentic candid feel.
    • Aspect ratio: 4:5 for Instagram, 9:16 for TikTok, 2:3 for Pinterest

Step 2 — UGC Lifestyle Variant

  1. Lifestyle context shot — muapi image edit (model=gpt4o-edit) using the output from Step 1:
    • Prompt: Make this look like authentic UGC content — add realistic environment context for {{setting}}, adjust lighting to feel natural and unposed, subtle film grain, candid photography style. Keep product {{product_name}} clearly visible and well-lit.

After generation:

  • Present both the try-on and lifestyle variant
  • Offer to generate a 3-image carousel set with different poses/settings
  • Suggest adding a short UGC-style video with kling-v3.0-pro-image-to-video

Notes

  • UGC performs best when it looks "accidental" — avoid overly polished or symmetrical compositions.
  • For TikTok/Reels, suggest animating the best static shot into a video.
  • For Amazon, refer back to the amazon-product-listing skill for white-background variants.

Trigger Keywords

ugc, try on, lifestyle photo, model wearing, outfit photo, wear product, user generated, ugc content, lifestyle try on


Notes for the Executing Agent

  • This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call muapi CLI commands. Use muapi auth configure first if MUAPI_API_KEY is unset.
  • For model IDs without a CLI alias yet, fall back to the raw endpoint via curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}' and poll with muapi predict wait <request_id>.
  • Substitute {{input_name}} placeholders with the user's actual inputs before issuing each call.
Files (generative-media-skills)
  • SKILL.md 3.7 KB
    ---
    slug: muapi-ugc-lifestyle-try-on
    name: muapi-ugc-lifestyle-try-on
    version: "1.0.0"
    description: Generate UGC-style (User Generated Content) lifestyle photos of a person wearing or using your product — authentic, relatable, social-media-native imagery.
    acceptLicenseTerms: true
    ---
    
    
    # UGC Lifestyle Try-On
    
    **Generate UGC-style (User Generated Content) lifestyle photos of a person wearing or using your product — authentic, relatable, social-media-native imagery.**
    
    ## Inputs
    
    | Name | Type | Required | Default | Description |
    |:---|:---|:---|:---|:---|
    | `product_name` | text | yes | — | The product to feature (e.g. "white oversized hoodie", "blue light blocking glasses", "leather crossbody bag"). |
    | `product_image` | image_url | yes | — | Product image or photo URL to use as reference for the try-on. |
    | `model_description` | text | no | woman, 25-30 years old, natural look, diverse | Description of the model (e.g. "man, athletic build, 20s", "woman, curvy, 30s, warm skin tone"). |
    | `setting` | text | no | casual lifestyle, natural lighting | Scene and mood (e.g. "urban street style", "cozy home morning routine", "gym workout", "coffee shop"). |
    | `platform` | text | no | instagram | Target platform — "instagram", "tiktok", "pinterest", "amazon". |
    
    
    ## Steps
    
    Submit the plan with TWO steps:
    
    ### Step 1 — Outfit/Product Try-On
    
    1. **Try-on generation** — `muapi image edit` (model=`ai-dress-change`) if product is wearable clothing, otherwise `muapi image edit` (model=`flux-kontext-pro-i2i`):
       - For clothing/wearables with `ai-dress-change`:
         - Product: `{{product_image}}`
         - Prompt: `{{model_description}} wearing the product naturally in a {{setting}} environment. Authentic UGC-style photo, candid pose, natural expression.`
       - For accessories/non-clothing with `flux-kontext-pro-i2i`:
         - Prompt: `{{model_description}} using/wearing {{product_name}} in a {{setting}}. The product from the reference image is clearly visible and featured. Natural UGC-style lifestyle photography, authentic candid feel.`
       - Aspect ratio: 4:5 for Instagram, 9:16 for TikTok, 2:3 for Pinterest
    
    ### Step 2 — UGC Lifestyle Variant
    
    2. **Lifestyle context shot** — `muapi image edit` (model=`gpt4o-edit`) using the output from Step 1:
       - Prompt: `Make this look like authentic UGC content — add realistic environment context for {{setting}}, adjust lighting to feel natural and unposed, subtle film grain, candid photography style. Keep product {{product_name}} clearly visible and well-lit.`
    
    After generation:
    - Present both the try-on and lifestyle variant
    - Offer to generate a 3-image carousel set with different poses/settings
    - Suggest adding a short UGC-style video with `kling-v3.0-pro-image-to-video`
    
    ## Notes
    - UGC performs best when it looks "accidental" — avoid overly polished or symmetrical compositions.
    - For TikTok/Reels, suggest animating the best static shot into a video.
    - For Amazon, refer back to the `amazon-product-listing` skill for white-background variants.
    
    ## Trigger Keywords
    
    `ugc`, `try on`, `lifestyle photo`, `model wearing`, `outfit photo`, `wear product`, `user generated`, `ugc content`, `lifestyle try on`
    
    
    ---
    
    ## Notes for the Executing Agent
    
    - This recipe is LLM-orchestrated: read each phase, gather any missing inputs from the user, then call `muapi` CLI commands. Use `muapi auth configure` first if `MUAPI_API_KEY` is unset.
    - For model IDs without a CLI alias yet, fall back to the raw endpoint via `curl -X POST https://api.muapi.ai/api/v1/<endpoint> -H "x-api-key: $MUAPI_API_KEY" -H 'content-type: application/json' -d '{...}'` and poll with `muapi predict wait <request_id>`.
    - Substitute `{{input_name}}` placeholders with the user's actual inputs before issuing each call.
    

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