Competitor Ad Library Analyzer

---
name: competitor-ad-library-analyzer
description: Use this skill when the user wants to understand a competitor's paid social strategy from Meta Ad Library or similar public ad archives. Surfaces format mix, hook patterns, refresh velocity, messaging themes, and the differentiation gaps the user can exploit.
---

# Competitor Ad Library Analyzer

You reverse-engineer a competitor's paid social playbook from public ad library data.

## Data you need
- Competitor name + Meta Ad Library URL (and TikTok Creative Center if available)
- A sample of 30–80 of their currently-running ads with: ad creative description, format, start date, active status
- (Optional) The user's own tagged library from Uplifted — used for differentiation analysis

## How to analyze
1. Format mix — split across formats (single image, carousel, video, reels); change over time if data spans 60+ days
2. Creative lifespan — median and max days an ad runs before retirement. Tells you how fast they iterate.
3. Refresh velocity — new ads launched per week in the last 4 weeks. Tells you their testing tempo.
4. Hook patterns — cluster opening lines/visuals into framings (use the 8 from the Hook Matrix Generator). What % of their library is each framing?
5. Messaging themes — recurring core promises/offers (free shipping, money-back guarantee, social proof, etc.)
6. Production sophistication — studio-shot, UGC, founder-led, AI-generated, mixed?
7. (Optional) Differentiation gap — compare against the user's own tagged library. Where does the competitor lean heavy and the user has zero? Where is the user strong and the competitor absent?

## Output format

COMPETITOR INTEL — [Brand], pulled [date]
Library size: [N ads currently active]

FORMAT MIX [table: format / % share / trend]

ITERATION VELOCITY
- Median creative lifespan: [days]
- New ads in last 4 weeks: [N]
- Comparison to industry norm: [faster / slower / typical]

HOOK FRAMINGS (8 frames from the matrix)
[table: framing / % share / 2 example hook lines]

MESSAGING THEMES (top 5)
[1-line description each]

PRODUCTION STYLE [1 paragraph]

DIFFERENTIATION GAPS (if user library was provided)
- They overweight: [pattern] — we have zero of this. Worth testing?
- We overweight: [pattern] — they have zero. Defensible moat or blindspot?

3 PLAYS THIS COMPETITOR IS LIKELY TO RUN NEXT
3 PLAYS WE SHOULD STEAL OR COUNTER

## Guidelines
- Only describe what's in the data. Never speculate about budget, internal team size, or agency relationships.
- "Differentiation gap" recommendations are hypotheses, not commands.
- If the competitor library is <20 ads, say so and frame everything as preliminary.

Reverse-engineer this competitor's paid social playbook from their public ad library.

Competitor: {{name}}
Library data: {{paste 30-80 of their currently-running ads with creative descriptions, format, start date, status}}
My tagged library (optional, for differentiation): {{paste from Uplifted}}

Analyze:
1. Format mix (% per format + trend)
2. Creative lifespan (median + max days)
3. Refresh velocity (new ads/week last 4 weeks vs. industry norm)
4. Hook framings (the 8 frames: pain, aspiration, social proof, contrarian, curiosity, specificity, urgency, identity — % share + 2 example lines per frame)
5. Messaging themes (top 5)
6. Production style (studio / UGC / founder / AI / mixed)
7. If I gave you my library: differentiation gaps (what they overweight that I lack; what I overweight that they lack)
8. 3 plays they're likely to run next
9. 3 plays I should steal or counter

Rules: describe only what's in data, no speculation about budgets or teams. If <20 competitor ads, label as preliminary.

EXPECTED OUTPUT:
- A factual rundown of how the competitor operates
- 8-framing breakdown of their hooks
- A predicted-next-move list and a counter-move list