ad-creative-analysis

name

ad-creative-analysis

description

Analyze ad creatives (images and videos) extracted from competitor research. Use when given a directory of ad images, video files, or transcripts to evaluate ad quality, score visual and messaging effectiveness, assign a scale score for viral/engagement potential, and generate a cross-creative pattern summary. Triggered by requests like "analyze these ads", "score these creatives", "what hooks are competitors using", "evaluate the ad library", "give me a scale score", "analyze the ad folder", or "what's working in these ads".

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 — 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 — Analyze Image Ads

For each image file, use vision/image analysis to evaluate the following.

Design Evaluation

Assess these five dimensions:

1. Visual hierarchy — Is the eye drawn to the right element first? Is there a clear focal point?
2. Color usage — Does the palette create contrast, evoke emotion, and maintain brand coherence?
3. Text overlay readability — Is copy legible at a glance? Font size, contrast, placement?
4. CTA prominence — Is the call-to-action visually distinct, clearly placed, and easy to act on?
5. Brand consistency — Logo placement, color adherence, font alignment with brand identity.

Image Scores (1-10 each)

• attention_grab — How fast and strongly does the creative stop a scroll?
• message_clarity — How clearly is the core message communicated without needing context?
• cta_strength — How compelling and action-oriented is the CTA?

Image Extraction

Extract:

• primary_message — The single core thing this ad is communicating (one sentence)
• emotion_appeal — One of: fear, aspiration, social_proof, urgency, curiosity, humor, trust, belonging, exclusivity
• target_audience — Inferred from visuals, copy, and context (e.g., "women 25-35 interested in fitness")
• hook_text — The first piece of copy the eye lands on (headline or main text)

Step 3 — 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:

1. Hook quality (first 3 seconds) — Does it immediately create curiosity, shock, or recognition? Would someone stop scrolling?
2. Script structure — Does it follow a logical persuasion arc (problem, solution, proof, CTA)?
3. Pacing — Is the editing rhythm appropriate for platform and audience? Not too slow or rushed?
4. CTA placement — 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 — Exact words spoken or shown in the first 3 seconds
• hook_type — One of: question, bold_claim, pain_point, curiosity_gap, social_proof, before_after, demonstration
• main_message — The core value proposition stated in the ad
• emotion_appeal — One of: fear, aspiration, social_proof, urgency, curiosity, humor, trust, belonging, exclusivity
• cta_text — The exact CTA spoken or shown
• cta_timing — When the CTA appears (e.g., "end", "middle", "repeated throughout")

Step 4 — Universal Metadata (All Ad Types)

For every creative, regardless of type, record:

• filename — The file name
• ad_format — One of: single_image, carousel, video, story, reel
• aspect_ratio — Detected or inferred (e.g., 1:1, 9:16, 16:9, 4:5)
• dimensions — Width x height in pixels if detectable
• ad_objective — Inferred from content and CTA: awareness, consideration, or conversion
• platform_fit — Which platforms this format and ratio suits best (e.g., ["Instagram Feed", "Facebook Feed"])

Step 5 — 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",
   
   

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": 
   

Step 6 — Generate Cross-Creative Summary

After analyzing all creatives, produce a summary object appended to the output. Include:

• total_analyzed — Count of creatives analyzed (split by type)
• top_performers — Filenames of the top 3 creatives by score (images by average score, videos by scale score)
• dominant_emotion — Most frequently detected emotion appeal across all ads
• common_hooks — List of recurring hook patterns or phrases observed
• cta_patterns — Most common CTA structures seen (e.g., "verb + free + urgency")
• dominant_objective — Most common inferred ad objective
• format_breakdown — Count per ad format
• recommendations — 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":      
     
      
      
      
    
  

Step 7 — 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