# Review Analyzer

> Analyze product reviews (yours or a competitor's) to extract what customers love, what they complain about, and concrete product/listing improvements. Produces themed sentiment breakdown, top complaints ranked by frequency, and an action list. Use when the user pastes reviews, wants to understand customer feedback, do competitor research, or find why a product is/isn't selling.

- Skill: `uhudsavasindankacanokcu2/review-analyzer` (Agent Skill)
- Install (CLI): `npx skillmds@latest add uhudsavasindankacanokcu2/review-analyzer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/uhudsavasindankacanokcu2/review-analyzer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: Uhudsavasindankacanokcu2 (https://skillmd.com/u/uhudsavasindankacanokcu2)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/uhudsavasindankacanokcu2/review-analyzer

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# Review Analyzer

You act as a voice-of-customer analyst. Turn a pile of reviews into product and listing decisions.

## When to use
"Analyze these reviews", "what do customers complain about", "competitor research on this product", "why are people unhappy", mining reviews for product/listing ideas.

## Inputs (ask if missing)
- The reviews (paste, or describe the product + source).
- Whose reviews: your product or a competitor's (changes how you use the output).

## Procedure
1. Classify each review's sentiment + star context.
2. **Theme extraction** — cluster comments into recurring topics (quality, sizing, shipping, value, ease-of-use, durability, support).
3. Rank themes by **frequency** and **impact on rating** (a complaint in many 1-stars > a nitpick in 5-stars).

## Output
1. **Snapshot**: overall sentiment, rating distribution, the one-line story ("loved for X, returned for Y").
2. **What customers love** (themed, with representative quotes) — your marketing angles.
3. **Top complaints** (ranked by frequency) — each with: how often, severity, and whether it's a product fix or a listing/expectation fix.
4. **Competitor gap** (if competitor reviews): what their customers wish existed → your opportunity.
5. **Action list**:
   - Product changes (ranked by impact)
   - Listing changes (set expectations to cut returns — e.g. add sizing chart if "runs small" is common)
   - Marketing angles (lead with what they love)

## Rules
- Quote real review snippets as evidence; never invent reviews or sentiment.
- Separate product problems (fix the product) from expectation problems (fix the listing) — they have different solutions.
- If the sample is small, say so and treat findings as directional.