A sophisticated diagnostic tool that converts unstructured customer reviews into prioritized action plans and pattern-based reports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install review-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install review-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Review Analysis?
Review Analysis is a high-level diagnostic skill designed to extract operational intelligence from customer feedback, complaints, and reviews. Unlike basic sentiment tools, this skill within the Openclaw Skills ecosystem focuses on clustering feedback into meaningful hierarchies to distinguish between product defects, messaging mismatches, and logistical friction.
It serves as a bridge between raw customer data and cross-functional decision-making, helping teams identify which issues are frequent versus merely loud. By normalizing messy data into structured patterns, it allows product, marketing, and support teams to align on a single source of truth regarding customer satisfaction and friction points.
Review Analysis Use Cases
- Summarizing product reviews from global marketplaces or app stores to identify core themes.
- Clustering repeated customer complaints to separate product quality issues from messaging mismatches.
- Identifying primary drivers for refunds and returns to reduce churn.
- Extracting buyer-loved features and product strengths to inform marketing copy and ad creative.
- Preparing concise, evidence-backed decision memos for product, ops, and CX leadership teams.
How Review Analysis Works
- Define the Review Set: Establish the scope of analysis, including sources like marketplace reviews, support tickets, or post-purchase surveys.
- Normalize and Cluster: Group unstructured feedback into functional buckets such as logistics, pricing, usability, or feature gaps.
- Pattern Identification: Assess the frequency and severity of each cluster, distinguishing between a loud anecdote and a statistically significant repeat pattern.
- Root Cause Mapping: Categorize issues by ownership—determining if the fix belongs to product development, marketing messaging, or shipping operations.
- Actionable Synthesis: Generate a final report that ranks patterns by importance and recommends specific next steps for the team.
Review Analysis Setup
To deploy this skill, ensure your environment is configured for the Openclaw Skills framework and point the agent to your feedback data source.
# Install the skill dependencies
npm install @openclaw/review-analysis
# Configure the input source
openclaw setup --skill review-analysis --source ./data/reviews.csv
Review Analysis Data Schema & Taxonomy
The skill organizes findings into a structured taxonomy for easy integration into project management tools:
| Attribute | Description | Values |
|---|---|---|
| Pattern Cluster | The primary category of feedback | Product, Shipping, Support, Value |
| Root Cause | The origin of the issue | Messaging, Quality, Expectation |
| Severity | The urgency of the pattern | High, Medium, Low |
| Evidence | Supporting raw text | Representative user quotes |
| Action Owner | The department responsible | Product, Marketing, Ops, CX |
Review Analysis Advanced Features
- Pattern Hierarchy Ranking: Automatically prioritizes issues based on potential business impact rather than just raw volume.
- Signal-to-Noise Filtering: Sophisticated logic to filter out non-actionable complaints and focus on systemic improvements.
- Multi-Departmental Recommendations: Specifically assigns tasks to marketing, support, or product teams based on detected root causes.
- Strength Amplification: Identifies positive customer delight drivers that can be instantly recycled into FAQ updates or sales copy.
SKILL.md
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