This does one thing well: it turns a pile of customer reviews into organized creative ammunition. You paste in reviews, it scores them for quality, then buckets everything into five categories: pain points, trigger moments, objections, transformations, and standout language. The real value is in how it handles quotes. Instead of paraphrasing or summarizing, it pulls exact customer language that's ready to drop into ad copy. If you're writing hooks or need voice of customer data for creative strategy, this gives you the raw material without the usual fluff. Works best when you have a decent volume of long, detailed reviews to mine.
npx -y skills add motion-team/creative-strategy-skills --skill review-audit --agent claude-codeInstalls into .claude/skills of the current project.
This system mines positive customer reviews to extract the insights that make ad copy actually work — the real language, real pain, real moments, and real transformations that customers experienced. The output feeds directly into creative strategy and hook writing.
The goal is not to summarize reviews. The goal is to find the raw material for ads.
Reviews can be provided in any format:
If multiple products are present in the review set, identify them before beginning. All output is separated by product.
If the format is unclear or product attribution is ambiguous, ask before proceeding.
If the brand sells multiple products, sort all reviews by product first. Every subsequent step runs separately per product.
If all reviews are for a single product, skip grouping and proceed.
Before analysis, score every review for quality. This determines what gets analyzed and what gets discarded.
| Score | What it looks like |
|---|---|
| 1 | Garbage — gibberish, swear words, 2–3 meaningless words, zero signal ("great product", "love it", "👍") |
| 2 | Low signal — very short, vague, no specific detail or emotion |
| 3 | Moderate — mentions the product, some specificity, but no vivid detail or emotional depth |
| 4 | High quality — specific, describes a real experience, references a before/after or a feeling |
| 5 | Gold — long, emotional, vivid, paragraph-level detail; the customer was so moved they wrote an essay about it |
Score 5 reviews are the priority. They contain the most usable language and the deepest insight.
Discard all reviews scored 1. Do not include them in analysis.
Analyze scores 2–5, with emphasis on 4s and 5s. Low-scoring reviews (2–3) can contribute to pattern identification but should not be the source of pulled quotes.
Run this analysis separately for each product. Within each bucket, group similar insights together and write a brief summary of the pattern. Then pull the best word-for-word quotes that exemplify it.
Do not editorialize the quotes. Pull them exactly as written.
What problem were they experiencing before they found this product?
Look for: descriptions of the problem they had, how long they'd had it, what they'd tried before, how it affected their life, the emotional weight of living with it.
For each pain theme identified:
What finally made them buy?
Look for: the specific moment, event, or realization that pushed them over the edge. This is the thing that turned a maybe into an add-to-cart. It could be a life event (wedding, diagnosis, vacation), a recommendation (friend, doctor, TikTok), hitting a breaking point, or running out of patience with other solutions.
For each trigger theme identified:
What almost stopped them from buying?
Look for: skepticism they mention having had, comparisons to other products they'd tried, price hesitation, disbelief that this would actually work, fear of wasting money again.
Note: In positive reviews, objections are almost always mentioned in past tense — "I was skeptical but..." or "I almost didn't try it because..." These are gold for objection-handling ad copy.
For each objection theme identified:
What changed for them after using the product?
Look for: the specific result they experienced, how their life is different now, the emotional shift (confidence, relief, freedom, pride), and — most importantly — how they describe the transformation in their own words. The more specific and visceral, the better.
For each transformation theme identified:
Exact language worth stealing for ads.
This bucket is different from the others. It is not organized by theme — it is a curated collection of the most vivid, emotionally charged, specific, and scroll-stopping phrases pulled from across all buckets. These are the lines that made you stop while reading. The ones that don't need to be rewritten. The ones a copywriter would highlight and build an ad around.
Pull these verbatim. Note which product they're from.
What to look for:
Produce a separate full output for each product. Structure:
─────────────────────────────────────
PRODUCT: [Product Name]
Reviews analyzed: [X] | Discarded (score 1): [X]
─────────────────────────────────────
BUCKET 1: PAIN POINTS
[Theme Name]
Summary: [2–3 sentences on the pattern]
[Theme Name]
Summary: [2–3 sentences on the pattern]
---
BUCKET 2: TRIGGER MOMENTS
[Theme Name]
Summary: [2–3 sentences on the pattern]
---
BUCKET 3: OBJECTIONS BEFORE PURCHASING
[Theme Name]
Summary: [2–3 sentences on the pattern]
---
BUCKET 4: TRANSFORMATIONS
[Theme Name]
Summary: [2–3 sentences on the pattern]
---
BUCKET 5: STANDOUT LANGUAGE & AD-READY PHRASES
"[Exact quote]"
"[Exact quote]"
"[Exact quote]"
[etc.]
─────────────────────────────────────
All word-for-word quotes are collected in Bucket 5. Do not scatter quotes throughout buckets 1–4 — keep the summaries clean and let the swipe file be the dedicated place for raw language.
The output of this analysis plugs directly into creative strategy and execution:
cursor/plugins
github/awesome-copilot
alirezarezvani/claude-skills
microsoft/win-dev-skills