Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Meta Video Ad Deconstructor
Deconstruct video ad creatives into marketing dimensions using Gemini AI. Extracts hooks, social proof, CTAs, target audience, emotional triggers, urgency tactics, and more. Use when analyzing competitor ads, generating creative briefs, or understanding what makes ads effective.
skill openclawclawhub Free
Deconstruct video ad creatives into marketing dimensions using Gemini AI. Extracts hooks, social proof, CTAs, target audience, emotional triggers, urgency tactics, and more. Use when analyzing competitor ads, generating creative briefs, or understanding what makes ads effective.
β¬ 0 downloads β
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, prompts/marketing_analysis.md, scripts/models.py, scripts/deconstructor.py, scripts/prompt_manager.py
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- fortytwode
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Video Ad Deconstructor
AI-powered deconstruction of video ad creatives into actionable marketing insights.
What This Skill Does
Generate Summaries: Product, features, audience, CTA extraction Deconstruct Marketing Dimensions: Hooks, social proof, urgency, emotion, etc. Support Multiple Content Types: Consumer products and gaming ads Progress Tracking: Callback support for long analyses JSON Output: Structured data for downstream processing
1. Environment Variables
# Required for Gemini GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
2. Dependencies
pip install vertexai
Basic Ad Deconstruction
from scripts.deconstructor import AdDeconstructor from scripts.models import ExtractedVideoContent import vertexai from vertexai.generative_models import GenerativeModel # Initialize Vertex AI vertexai.init(project="your-project-id", location="us-central1") gemini_model = GenerativeModel("gemini-1.5-flash") # Create deconstructor deconstructor = AdDeconstructor(gemini_model=gemini_model) # Create extracted content (from video-ad-analyzer or manually) content = ExtractedVideoContent( video_path="ad.mp4", duration=30.0, transcript="Tired of messy cables? Meet CableFlow...", text_timeline=[{"at": 0.0, "text": ["50% OFF TODAY"]}], scene_timeline=[{"timestamp": 0.0, "description": "Person frustrated with tangled cables"}] ) # Generate summary summary = deconstructor.generate_summary( transcript=content.transcript, scenes="0.0s: Person frustrated with tangled cables", text_overlays="50% OFF TODAY" ) print(summary)
Full Deconstruction
# Deconstruct all marketing dimensions def on_progress(fraction, dimension): print(f"Progress: {fraction*100:.0f}% - Analyzed {dimension}") analysis = deconstructor.deconstruct( extracted_content=content, summary=summary, is_gaming=False, # Set True for gaming ads on_progress=on_progress ) # Access dimensions for dimension, data in analysis.dimensions.items(): print(f"\n{dimension}:") print(data)
Summary Output
Product/App: CableFlow Cable Organizer Key Features: Magnetic design: Keeps cables organized automatically Universal fit: Works with all cable types Premium materials: Durable silicone construction Target Audience: Tech users frustrated with cable management Call to Action: Order now and get 50% off
Deconstruction Output
{ "spoken_hooks": { "elements": [ { "hook_text": "Tired of messy cables?", "timestamp": "0:00", "hook_type": "Problem Question", "effectiveness": "High - directly addresses pain point" } ] }, "social_proof": { "elements": [ { "proof_type": "User Count", "claim": "Over 1 million happy customers", "credibility_score": 7 } ] }, # ... more dimensions }
Marketing Dimensions Deconstructed
DimensionWhat It Extractsspoken_hooksOpening hooks from transcriptvisual_hooksAttention-grabbing visualstext_hooksOn-screen text hookssocial_proofTestimonials, user counts, reviewsurgency_scarcityLimited time offers, stock warningsemotional_triggersFear, desire, belonging, etc.problem_solutionPain points and solutionscta_analysisCall-to-action effectivenesstarget_audienceWho the ad targetsunique_mechanismWhat makes product special
Customizing Prompts
Edit prompts in prompts/marketing_analysis.md to customize: What dimensions to analyze Output format Scoring criteria Gaming vs consumer product focus
Common Questions This Answers
"What hooks does this ad use?" "What's the emotional appeal?" "How does this ad create urgency?" "Who is this ad targeting?" "What social proof is shown?" "Deconstruct this competitor's ad"
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Scripts2 Docs
- SKILL.md
- prompts/marketing_analysis.md
- scripts/deconstructor.py
- scripts/models.py
- scripts/prompt_manager.py