UGC Fashion & Activewear Product Video Generator — Fitness Ecommerce Content Creator for Social Media Influencers on TikTok, Instagram Reels

UGC video generator for fashion and activewear brands. Turns a single outfit image into TikTok and Instagram Reels ready product videos — talking head and vo...

Security Scan

The skill's requests and runtime instructions align with its stated purpose (generating UGC-style product videos via IMA Studio); it asks only for the IMA_API_KEY and to use the imastudio-cli, and does not contain hidden installs or unrelated credential requests.

Purpose & Capability

Name/description match the runtime instructions: the SKILL.md describes transforming an outfit image into short vertical videos, and the instructions call the imastudio CLI (ima) and require IMA_API_KEY which are exactly what a video-generation integration would need.

Instruction Scope

Instructions explicitly tell the agent to analyze the provided image, assemble prompts, upload the image, and call imastudio create-task endpoints for image-to-video and text-to-speech. This is within the skill's purpose, but it does involve uploading user-supplied images and generation prompts to a third-party service (imastudio.com) — users should be aware that any image or identifying content will be transmitted off-host.

Install Mechanism

The skill is instruction-only (no install spec). SKILL.md requires the imastudio-cli (ima) and package.json lists imastudio-cli as a dependency, but there is no explicit install/install script in the skill bundle. This is a small inconsistency (the skill assumes the CLI is available) but not inherently malicious. Operators must ensure the ima CLI is installed separately in their environment.

Credentials

Only one credential is requested (IMA_API_KEY), which directly maps to the external API/service used. No unrelated secrets, system paths, or other environment variables are requested.

Persistence & Privilege

The skill does not request always:true or any elevated persistence; it is user-invocable and can be invoked autonomously per platform defaults. It does not declare writes to other skills or global agent config.

Assessment

This skill appears coherent: it uses the imastudio CLI and an IMA_API_KEY to upload images and request video generation from imastudio.com. Before installing or using it, consider: (1) Any image you provide will be uploaded to a third-party service — avoid sending images containing real people's sensitive personal data or private contexts unless you trust the service and its privacy policy. (2) The skill assumes the imastudio CLI is available — you’ll need to install/verify that separately. (3) The generated content enforces a 'same face / same model' consistency rule — be cautious about creating or manipulating likenesses of real people. (4) Treat your IMA_API_KEY as a secret: provide a key with least privilege, and rotate/revoke it if you stop using the skill. If you need deeper assurance, request the maintainer/publisher information and read imastudio's terms/privacy before proceeding.

UGC Activewear Video Generator

Generate TikTok / Instagram Reels style activewear UGC product videos from a single outfit image.

Requires: imastudio-cli npm package (ima command) and IMA_API_KEY. Get your API key at: https://imastudio.com

Workflow

When the user provides an outfit image (local file or URL), execute these steps in order:

Step 1 — Analyze the Image

Use your vision capability to extract from the image:

• Model: gender, body type, vibe, hairstyle
• Outfit: type (set/top/bottom), silhouette, fabric texture, color palette
• Details: logo placement, accessories, fit and proportions
• Environment: studio / outdoor / gym / street

Step 2 — Lock Consistency Rules

Every generated video MUST maintain across all shots:

• Same face, body type, hairstyle, makeup
• Same outfit with exact color, silhouette, fit, logo placement
• Same accessories and styling energy

Tell the model explicitly in every prompt: "same model, same outfit, same styling throughout."

Step 3 — Generate Two Video Prompts

Build two 15-second video prompts:

A) Talking Head (influencer speaks to camera):

• 0–3s: Hook shot — direct-to-camera line, outfit visible
• 3–7s: Detail close-ups — fabric, waistband, body-line flattery
• 7–11s: Movement — walking, turning, lifestyle motion
• 11–15s: Hero shot — full-body, closing line

B) Voiceover (aesthetic b-roll + narration):

• 0–3s: Outfit entrance — hero reveal shot
• 3–7s: Detail shots — texture, silhouette, comfort cues
• 7–11s: Lifestyle motion — natural movement, posing
• 11–15s: Full-body hero + CTA

Visual rules for both: handheld but polished, punch-in zooms, natural daylight, clean transitions, strong silhouette emphasis, premium social-native look.

Step 4 — Upload Image (if local file)

ima upload <outfit-image> --json

Use the returned url as input for video generation.

Step 5 — Generate Videos

For each prompt (talking head + voiceover), run:

ima create-task \
  --task-type image_to_video \
  --model wan2.6-i2v \
  --param prompt="<assembled prompt>" \
  --param input_images="<image_url>" \
  --param duration=10 \
  --param aspect_ratio=9:16 \
  --wait --json

Model selection:

Priority	Model	model_id	Best for
Default	Wan 2.6	wan2.6-i2v	Balanced quality + speed
Premium	Kling O1	kling-video-o1	Best consistency
Fast	Seedance 2.0 Fast	ima-pro-fast	Quick iteration

Use 9:16 aspect ratio (vertical/portrait) for TikTok and Reels.

Step 6 — Generate TTS Narration (Voiceover only)

For the voiceover video, generate a spoken script:

ima create-task \
  --task-type text_to_speech \
  --model seed-tts-1.1 \
  --param prompt="<voiceover script>" \
  --wait --json

Script tone: confident, aspirational, social-native. Not salesy — like a friend recommending a find.

Step 7 — Deliver Results

Send each video to the user with:

• Video via media URL (inline playback)
• Which style it is (talking head / voiceover)
• Model used and generation time
• The prompt and script used (so they can iterate)

Prompt Assembly Guide

When building the video generation prompt, include ALL of these elements:

1. Scene setup: "A [gender] fitness influencer in [location], [lighting]"
2. Outfit description: exact details from Step 1 analysis
3. Action sequence: what happens in each time segment
4. Camera work: "handheld, slight movement, punch-in zoom on [detail]"
5. Mood/energy: "confident, premium athleisure, social-media-native"
6. Consistency anchor: "same model, same outfit, same styling throughout the video"

Example prompt:

A confident young woman in a modern minimalist apartment, natural daylight. She wears a matching sage-green ribbed sports bra and high-waisted leggings set with subtle logo on waistband. She looks at camera with a warm smile, then the camera punches in on the fabric texture and waistband detail. She turns showing the silhouette from the side, walks toward the window. Final full-body hero shot, hands on hips. Handheld camera, premium social-media look. Same model, same outfit, same styling throughout.

Script Templates

Talking head hook lines:

• "Okay this set is actually insane"
• "POV: you found the perfect gym-to-brunch set"
• "I need everyone to see this fabric up close"
• "This might be my new favorite workout set"

Voiceover narration example:

"When I say this set hits different — I mean it. The ribbing, the compression, the way it moves with you. From the gym to coffee runs, this is the one."

Input Parameters

Parameter	Required	Default	Description
image	Yes	—	Outfit photo (local path or URL)
mode	No	both	talking_head, voiceover, or both
scene_type	No	auto-detected	gym, street, studio, café, rooftop
brand	No	—	Brand name for script mentions
outfit_description	No	auto-analyzed	Override auto-analysis

Notes

• Always use image_to_video (not text_to_video) to maintain outfit consistency from the source image
• Vertical 9:16 is default — only use 16:9 if user explicitly asks for landscape
• If the first result has consistency issues, retry with kling-video-o1 which has stronger reference adherence
• For batch production (multiple outfits), process one at a time and deliver incrementally