mcpField
C
customer-review-aggregator

customer-review-aggregator

github.com/onewave-aiMarketing & growth393351 installsUpdated 1w ago
Repository

About customer-review-aggregator

A marketing skill for collecting and analyzing customer reviews across platforms. It combines sentiment analysis, pain-point and feature-request ranking, evidence-backed claims, and competitor comparisons.

Key features

  1. 01Collect reviews via paste, CSV, or URLs
  2. 02Score overall and feature sentiment
  3. 03Cluster and rank pain points
  4. 04Extract claims with quotes and confidence
  5. 05Compare competitors side by side
  6. 06Rank feature requests

Use cases

  1. 01Analyze feedback across review platforms
  2. 02Find recurring product pain points
  3. 03Build evidence-backed marketing claims
  4. 04Compare customer feedback on competitors

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/onewave-ai/claude-skills --skill customer-review-aggregator

After installing, type/customer-review-aggregatorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

references/
intake-prompts.md
output-templates.md
sources.md

Details

From the project README

Customer Review Aggregator & Analyzer

Pull reviews from multiple platforms and extract actionable insights with sentiment analysis, pain-point detection, marketing-claim extraction, and competitor comparison.

Contents

  • references/sources.md - supported platforms and sentiment dimensions
  • references/intake-prompts.md - scope and data-collection prompts, example use cases
  • references/output-templates.md - report templates for every analysis type

Workflow

  1. Define scope. Present the scope prompt from references/intake-prompts.md to capture product, platforms, competitors, analysis focus, and time period.
  2. Gather review data. Offer the three data-collection methods (paste, CSV upload, URLs via WebFetch) from references/intake-prompts.md and collect the reviews.
  3. Analyze sentiment. Score overall sentiment and feature-level sentiment using the dimensions in references/sources.md.
  4. Identify pain points. Cluster negative feedback by theme, rank by frequency, and assign impact plus a recommendation.
  5. Extract marketing claims. Derive evidence-backed claims from positive reviews, each with supporting quotes, a confidence level, and a use case.
  6. Compare competitors. When competitors are provided, build the side-by-side comparison and surface weaknesses to exploit.
  7. Analyze feature requests. Rank requested features by mention count, urgency, and competitor coverage.
  8. Assemble the report. Populate the full analysis report and offer deliverable formats.

For all output formats and tables, see references/output-templates.md.

Comments

Markdown supported