# Reputation Reviews > Multi-platform review aggregation and thematic analysis for any business - Skill: `zubair-trabzada/reputation-reviews` (Agent Skill) - Install (CLI): `npx skillmds@latest add zubair-trabzada/reputation-reviews` - Raw SKILL.md: https://api.skillmd.com/api/skills/zubair-trabzada/reputation-reviews/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: zubair-trabzada (https://skillmd.com/u/zubair-trabzada) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/zubair-trabzada/reputation-reviews --- # Reviews Analysis Skill You are an expert reputation analyst. When the user runs `/reputation reviews `, execute a comprehensive review analysis across all major review platforms. ## Input The user provides a business name. If the business name is ambiguous (e.g., common name with multiple locations), ask the user to clarify with a city/state or URL before proceeding. ## Execution Phases ### Phase 1: Business Discovery Use WebSearch to identify the business and gather baseline information. **Search queries to run:** 1. `"" reviews` 2. `"" Google reviews` 3. `"" Yelp` 4. `"" Trustpilot` 5. `"" BBB rating` 6. `"" G2 reviews` (if B2B/software) 7. `"" Capterra reviews` (if software) From search results, extract: - Full legal business name - Business category/industry - Location(s) - URLs for each review platform where the business appears ### Phase 2: Platform-by-Platform Review Collection For each platform found, use WebSearch and WebFetch to gather review data. Collect as many individual reviews as possible. **For each platform, extract:** - Overall star rating (out of 5) - Total number of reviews - Rating distribution (5-star, 4-star, 3-star, 2-star, 1-star counts or percentages) - Date range of reviews (oldest to newest found) - Up to 20 individual reviews per platform, capturing: - Reviewer name (first name/initial only) - Star rating - Date posted - Full review text - Business response (if any) **Platform-specific search strategies:** | Platform | Search Query Pattern | |----------|---------------------| | Google | `site:google.com/maps "" reviews` and `"" google reviews` | | Yelp | `site:yelp.com ""` | | Trustpilot | `site:trustpilot.com ""` | | G2 | `site:g2.com "" reviews` | | Capterra | `site:capterra.com "" reviews` | | BBB | `site:bbb.org ""` | | TripAdvisor | `site:tripadvisor.com ""` (hospitality/food) | | Glassdoor | `site:glassdoor.com "" reviews` (employer reputation) | | Facebook | `"" facebook reviews recommendations` | Use WebFetch on each discovered URL to pull actual review content. If a page blocks scraping, fall back to search snippet extraction. ### Phase 3: Thematic Analysis Categorize every collected review into one or more themes. A single review can map to multiple themes. **Standard theme categories:** | Theme | Trigger Keywords/Phrases | |-------|-------------------------| | Service Quality | service, helpful, attentive, rude, slow service, great service, above and beyond | | Pricing/Value | expensive, overpriced, worth it, good value, cheap, affordable, rip-off, fair price | | Wait Times | wait, waited, slow, quick, fast, took forever, prompt, on time, delayed | | Staff/Personnel | staff, employee, team, friendly, knowledgeable, unprofessional, rude, courteous | | Product Quality | quality, broken, defective, well-made, durable, flimsy, excellent product | | Communication | communication, responded, ghosted, follow-up, kept informed, no response, transparent | | Cleanliness/Atmosphere | clean, dirty, atmosphere, ambiance, filthy, spotless, comfortable, cramped | | Location/Accessibility | location, parking, easy to find, convenient, hard to get to, accessible | | Food/Menu | food, taste, menu, portions, fresh, stale, delicious, bland (restaurants only) | | Technical Support | support, help desk, ticket, resolved, unresolved, bug, downtime (tech/SaaS) | | Onboarding/Setup | setup, onboarding, getting started, learning curve, easy to use, confusing | | Reliability | reliable, consistent, inconsistent, hit or miss, always, never | | Management Response | owner responded, management, addressed, ignored, apologized | **For each theme, calculate:** - Frequency: how many reviews mention this theme - Frequency percentage: out of total reviews collected - Average sentiment: positive, mixed, or negative (with a numeric score from -1.0 to +1.0) - Representative quotes: 2-3 direct quotes that exemplify the theme (one positive, one negative if both exist) ### Phase 4: Complaint and Praise Identification **Top 3 Complaints (most frequently mentioned negative themes):** For each complaint, provide: - The specific issue - How many reviews mention it - Severity rating (Minor / Moderate / Severe / Critical) - A direct quote exemplifying the complaint - Whether the business has responded to reviews about this issue **Top 3 Praise Points (most frequently mentioned positive themes):** For each praise point, provide: - The specific strength - How many reviews mention it - A direct quote exemplifying the praise - Whether this strength appears consistent across time ### Phase 5: Platform Health Assessment For each platform, assess: - **Review velocity**: How many new reviews per month (estimate from dates) - **Response rate**: What percentage of reviews (especially negative ones) received a business response - **Response quality**: Are responses generic copy-paste or personalized and empathetic - **Rating trend**: Is the rating trending up, down, or stable based on recent vs older reviews - **Review authenticity signals**: Any signs of fake reviews (burst of 5-stars on same day, generic language, reviewer has no other reviews) ## Output Format Write the output to `REVIEW-ANALYSIS-[business-name-slugified].md` in the current working directory. ```markdown # Review Analysis: [Business Name] **Generated:** [date] **Platforms Analyzed:** [count] **Total Reviews Collected:** [count] --- ## Executive Summary [3-5 sentence summary of overall reputation health. Include the aggregate rating across platforms, the single biggest strength, and the single biggest vulnerability.] --- ## Platform Overview | Platform | Rating | Reviews | Response Rate | Trend | |----------|--------|---------|---------------|-------| | Google | X.X/5 | NNN | XX% | [up/down/stable arrow] | | Yelp | X.X/5 | NNN | XX% | [up/down/stable arrow] | | [etc.] | | | | | **Aggregate Rating:** X.X/5 (weighted by review volume) --- ## Rating Distribution | Stars | Count | Percentage | Visual | |-------|-------|------------|--------| | 5 | NNN | XX% | [bar representation] | | 4 | NNN | XX% | [bar representation] | | 3 | NNN | XX% | [bar representation] | | 2 | NNN | XX% | [bar representation] | | 1 | NNN | XX% | [bar representation] | --- ## Thematic Analysis ### Theme Frequency Ranking | Rank | Theme | Mentions | % of Reviews | Avg Sentiment | Sentiment Score | |------|-------|----------|-------------|---------------|-----------------| | 1 | [theme] | NN | XX% | [pos/mix/neg] | [+/-X.X] | | 2 | [theme] | NN | XX% | [pos/mix/neg] | [+/-X.X] | | [etc.] | | | | | | ### Detailed Theme Breakdown #### [Theme Name] — [Mention Count] mentions ([Sentiment]) **What reviewers say:** - Positive: "[direct quote]" — [Reviewer], [Platform], [Date] - Negative: "[direct quote]" — [Reviewer], [Platform], [Date] **Pattern:** [1-2 sentence description of the pattern within this theme] [Repeat for each theme] --- ## Top 3 Complaints ### 1. [Complaint Title] — [Severity: Minor/Moderate/Severe/Critical] - **Mentioned in:** NN reviews (XX%) - **Platforms:** [which platforms this appears on] - **Representative quote:** "[quote]" - **Business responding:** [Yes/No/Partially — X of Y negative reviews received responses] - **Impact assessment:** [How this likely affects new customer decisions] ### 2. [Complaint Title] — [Severity] [same structure] ### 3. [Complaint Title] — [Severity] [same structure] --- ## Top 3 Praise Points ### 1. [Praise Title] - **Mentioned in:** NN reviews (XX%) - **Platforms:** [which platforms] - **Representative quote:** "[quote]" - **Consistency:** [Is this consistent over time or only recent] - **Leverage opportunity:** [How the business could amplify this strength] ### 2. [Praise Title] [same structure] ### 3. [Praise Title] [same structure] --- ## Platform Health Scorecard | Metric | Google | Yelp | [Platform] | Overall | |--------|--------|------|------------|---------| | Review Volume | [low/med/high] | | | | | Review Velocity (per month) | ~NN | | | | | Response Rate | XX% | | | | | Response Quality | [poor/fair/good/excellent] | | | | | Rating Trend | [declining/stable/improving] | | | | | Authenticity Confidence | [low/med/high] | | | | --- ## Recommendations ### Immediate Actions (This Week) 1. [Specific actionable recommendation] 2. [Specific actionable recommendation] ### Short-Term (This Month) 1. [Specific actionable recommendation] 2. [Specific actionable recommendation] ### Ongoing 1. [Specific actionable recommendation] 2. [Specific actionable recommendation] --- ## Raw Data: Recent Reviews [Include the 10 most recent reviews across all platforms, formatted as:] ### [Platform] — [Star Rating] — [Date] **Reviewer:** [Name] > [Review text] **Business Response:** [Response text or "No response"] --- *Report generated by AI Reputation Manager* ``` ## Important Guidelines - Never fabricate reviews or ratings. Only report what you actually find through search and fetch. - If a platform returns no results, note it as "No presence found on [platform]" rather than omitting it. - If review data is limited (fewer than 10 total reviews found), flag this clearly in the executive summary and note that analysis confidence is low. - Always attribute quotes to their source platform and approximate date. - Weight Google and Yelp reviews more heavily in aggregate calculations as they have the highest consumer visibility. - If the business is B2B or SaaS, prioritize G2 and Capterra over Yelp and TripAdvisor. - If the business is a restaurant or hotel, include TripAdvisor and OpenTable in the platform search.