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",
  "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 — link in bio",
  "cta_timing": "end"
}

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": 4, "video": 3, "carousel": 1 },
    "recommendations": [
      "Hooks are strong but CTAs lack urgency — test adding 'today only' or limited quantity",
      "All videos open with talking head — test a demonstration hook for variety",
      "Aspiration dominates — test a fear/pain angle to broaden audience response"
    ]
  }
}

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