Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Ad Creative Analysis
Analyze ad creatives (images and videos) extracted from competitor research. Use when given a directory of ad images, video files, or transcripts to evaluate...
skill openclawclawhub Free
Analyze ad creatives (images and videos) extracted from competitor research. Use when given a directory of ad images, video files, or transcripts to evaluate...
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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, references/analysis-framework.md
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
- baitoxkevin
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Ad Creative Analysis
Analyze a directory of competitor or reference ad creatives. Produce a per-creative JSON analysis and a cross-creative pattern summary.
Step 1 โ Accept Inputs
Expect one of: A directory path containing image files (.jpg, .jpeg, .png, .webp, .gif) and/or video files (.mp4, .mov, .avi, .webm) An optional metadata.json file in that directory with fields per filename: platform, spend, duration_days, impressions, format If no path is given, ask the user: "Please provide the directory path containing the ad creatives." List all files in the directory. Separate into image ads and video ads. Log the count of each before proceeding.
Step 2 โ Analyze Image Ads
For each image file, use vision/image analysis to evaluate the following.
Design Evaluation
Assess these five dimensions: Visual hierarchy โ Is the eye drawn to the right element first? Is there a clear focal point? Color usage โ Does the palette create contrast, evoke emotion, and maintain brand coherence? Text overlay readability โ Is copy legible at a glance? Font size, contrast, placement? CTA prominence โ Is the call-to-action visually distinct, clearly placed, and easy to act on? Brand consistency โ Logo placement, color adherence, font alignment with brand identity.
Image Scores (1-10 each)
attention_grab โ How fast and strongly does the creative stop a scroll? message_clarity โ How clearly is the core message communicated without needing context? cta_strength โ How compelling and action-oriented is the CTA?
Image Extraction
Extract: primary_message โ The single core thing this ad is communicating (one sentence) emotion_appeal โ One of: fear, aspiration, social_proof, urgency, curiosity, humor, trust, belonging, exclusivity target_audience โ Inferred from visuals, copy, and context (e.g., "women 25-35 interested in fitness") hook_text โ The first piece of copy the eye lands on (headline or main text)
Step 3 โ Analyze Video Ads
For each video file, analyze the video directly using vision. If a transcript file exists alongside the video (same filename, .txt or .srt extension), read and use it.
Video Evaluation
Assess these four dimensions: Hook quality (first 3 seconds) โ Does it immediately create curiosity, shock, or recognition? Would someone stop scrolling? Script structure โ Does it follow a logical persuasion arc (problem, solution, proof, CTA)? Pacing โ Is the editing rhythm appropriate for platform and audience? Not too slow or rushed? CTA placement โ Is the call-to-action clear, timed well, and repeated if needed?
Video Scale Score (1-10)
Assign a single scale_score representing the ad's viral and engagement potential at scale: 9-10: Exceptional hook, tight script, clear CTA. Likely to perform well at high spend. 7-8: Strong fundamentals, minor weaknesses. Good candidate for testing. 5-6: Average execution. Needs a stronger hook or clearer CTA before scaling. 3-4: Core idea present but poor execution. Requires significant rework. 1-2: Unlikely to perform. Fundamental issues with hook, message, or CTA. See references/analysis-framework.md for detailed scale score rubric.
Video Extraction
Extract: hook_text โ Exact words spoken or shown in the first 3 seconds hook_type โ One of: question, bold_claim, pain_point, curiosity_gap, social_proof, before_after, demonstration main_message โ The core value proposition stated in the ad emotion_appeal โ One of: fear, aspiration, social_proof, urgency, curiosity, humor, trust, belonging, exclusivity cta_text โ The exact CTA spoken or shown cta_timing โ When the CTA appears (e.g., "end", "middle", "repeated throughout")
Step 4 โ Universal Metadata (All Ad Types)
For every creative, regardless of type, record: filename โ The file name ad_format โ One of: single_image, carousel, video, story, reel aspect_ratio โ Detected or inferred (e.g., 1:1, 9:16, 16:9, 4:5) dimensions โ Width x height in pixels if detectable ad_objective โ Inferred from content and CTA: awareness, consideration, or conversion platform_fit โ Which platforms this format and ratio suits best (e.g., ["Instagram Feed", "Facebook Feed"])
Step 5 โ Output Per-Creative JSON
Output one JSON object per creative. Print all results together in a single JSON array.
Image ad example structure
{ "filename": "ad_001.jpg", "type": "image", "ad_format": "single_image", "aspect_ratio": "1:1", "dimensions": "1080x1080", "ad_objective": "conversion", "platform_fit": ["Instagram Feed", "Facebook Feed"], "scores": { "attention_grab": 8, "message_clarity": 7, "cta_strength": 9 }, "primary_message": "Lose 10kg in 30 days without giving up your favourite food", "emotion_appeal": "aspiration", "target_audience": "Women 28-45 who have tried dieting before", "hook_text": "Still counting calories? There's a better way." }
Video ad example structure
{ "filename": "ad_002.mp4", "type": "video", "ad_format": "video", "aspect_ratio": "9:16", "dimensions": "1080x1920", "ad_objective": "consideration", "platform_fit": ["TikTok", "Instagram Reels", "Facebook Reels"], "scale_score": 8, "hook_text": "I was $40,000 in debt until I found this", "hook_type": "before_after", "main_message": "This budgeting app helped me pay off debt in 18 months", "emotion_appeal": "fear", "cta_text": "Download free โ link in bio", "cta_timing": "end" }
Step 6 โ Generate Cross-Creative Summary
After analyzing all creatives, produce a summary object appended to the output. Include: total_analyzed โ Count of creatives analyzed (split by type) top_performers โ Filenames of the top 3 creatives by score (images by average score, videos by scale score) dominant_emotion โ Most frequently detected emotion appeal across all ads common_hooks โ List of recurring hook patterns or phrases observed cta_patterns โ Most common CTA structures seen (e.g., "verb + free + urgency") dominant_objective โ Most common inferred ad objective format_breakdown โ Count per ad format recommendations โ 3-5 actionable observations for improving or scaling these creatives
Summary example structure
{ "summary": { "total_analyzed": { "images": 5, "videos": 3 }, "top_performers": ["ad_004.jpg", "ad_002.mp4", "ad_007.jpg"], "dominant_emotion": "aspiration", "common_hooks": [ "Question-based hook challenging a common belief", "Before/after framing in first sentence" ], "cta_patterns": [ "Shop now + scarcity signal", "Free trial + no credit card" ], "dominant_objective": "conversion", "format_breakdown": { "single_image": 4, "video": 3, "carousel": 1 }, "recommendations": [ "Hooks are strong but CTAs lack urgency โ test adding 'today only' or limited quantity", "All videos open with talking head โ test a demonstration hook for variety", "Aspiration dominates โ test a fear/pain angle to broaden audience response" ] } }
Step 7 โ Handle Missing or Unreadable Files
If a file cannot be analyzed (corrupted, unsupported format, too dark/blurry for vision): Include the filename in the output with "status": "unreadable" and a brief "reason" field Continue analyzing remaining files, do not stop
Reference Material
Consult skills/ad-creative-analysis/references/analysis-framework.md for: Detailed scoring rubrics per metric Ad psychology pattern definitions Hook formula templates Extended example output
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- references/analysis-framework.md