What types of data formats can I upload for analysis?

This skill accepts raw text, CSV exports, and JSON logs from sources like Shopify, Zendesk, or Amazon. Simply upload your qualitative data, and the skill will automatically organize it into a structured priority matrix.

How does this differ from just asking a standard AI to summarize my reviews?

Unlike basic prompts that can hallucinate trends, this skill uses a specialized synthesis framework that maps every insight to specific evidence in your source data. This ensures your product roadmap is based on actual customer quotes rather than AI assumptions.

What is included with my purchase of this skill?

You will receive the 'Customer Feedback Analyzer' skill logic, a guide on preparing your data for the best results, and a template for the priority matrix output. You also get access to all future version updates and refinements.

Which AI agents or platforms are compatible with this skill?

This skill is designed to be framework-agnostic and works with any AI agent capable of processing structured text data. It is optimized for agents that handle research, project management, and data synthesis tasks.

Are there any types of feedback this skill might struggle to process?

While the skill is highly adept at identifying recurring themes and sentiment, it may struggle with extremely niche technical jargon or subtle sarcasm. It relies entirely on the text you provide and cannot independently verify the identity of the reviewers.

Customer Feedback Analyzer

Example session with this skill installed

Analyze these 3 reviews:

"Love the shoes but the sole fell off after a week." 2. "Fast shipping, great color." 3. "The checkout page kept crashing on mobile, took 3 tries."

Read your context and instructions
Compiled the customer feedback analyzer

I have analyzed the 3 feedback items provided. The data includes mentions of product durability, delivery speed, and technical stability.

Customer Feedback Analysis

1. Executive Summary

The feedback indicates a split between high initial satisfaction (delivery, aesthetics) and significant friction in the post-purchase and checkout phases. Critical issues include a product defect (sole detachment) and a high-friction mobile checkout experience.

2. Recurring Themes

Theme	What customers are saying	Evidence	Mentions	Confidence
Product Quality	Soles are detaching prematurely.	"sole fell off after a week"	1	High
Logistics	Delivery is meeting expectations.	"Fast shipping"	1	High
UX/Technical	Mobile checkout is unstable.	"checkout page kept crashing on mobile"	1	High

3. Top Complaints and Unmet Needs

Explicit Complaint: The mobile checkout process is unreliable, requiring multiple attempts.
Explicit Complaint: The shoe construction lacks durability for short-term use.

Inferred Need: Customers need a stable mobile shopping environment and a clear quality guarantee/return path for defective goods.

4. Improvement Priority Matrix

Priority	Issue	Recommended Action	Impact	Effort
High	Checkout Crashing	Debug mobile session handling on the checkout page.	Conversion	Medium
High	Sole Detachment	Investigate batch quality with the manufacturer.	Retention	High
Low	Shipping Speed	Maintain current logistics workflow.	Reputation	Low

Next steps

Review mobile error logs for the checkout page to identify the crash trigger.
Contact the customer from Review #1 to initiate a replacement and document the defect.
Verify if other customers have reported similar durability issues in a larger dataset.

Connects securely to your tools. The creator never sees your data.

About this skill

Customer sentiment analysis often relies on gut feeling or cherry-picked quotes, leading to misaligned product roadmaps. This skill transforms raw text data into an evidence-based priority matrix to guide business decisions.

What it does

Theme extraction groups feedback into recurring categories like product quality, delivery, and pricing.
Sentiment classification labels text as positive, negative, or neutral based on context.
Evidence mapping pairs every finding with representative quotes from the source data.
Priority ranking assesses issues based on frequency, severity, and business impact.
Action planning generates concrete next steps with suggested metrics for success.

How it works

Data intake accepts raw reviews, survey results, or support logs provided by the user.
Pattern recognition identifies recurring complaints and unmet customer needs.
Synthesis generates a structured report covering sentiment, themes, and strengths.
Prioritization produces a matrix ranking improvements from High to Low effort and impact.

Frameworks & tools

This skill is framework-agnostic and processes plain text, CSV exports, or JSON feedback logs. It works best with qualitative data from platforms like Shopify, Zendesk, Typeform, or Amazon.

Why this beats prompting it yourself

Generic prompts often hallucinate statistics or miss subtle sarcasm in customer reviews. This skill enforces strict evidence-based grounding, ensuring every recommendation is tied to a specific piece of text rather than an LLM's best guess.

Use cases

App store audits to identify technical bugs mentioned in recent reviews.
Product launch reviews to gauge initial market reaction and feature requests.
Churn analysis to find recurring reasons why customers are canceling services.
E-commerce optimization to identify friction points in the shipping or return flow.

Known limitations

This skill cannot independently access external review platforms or verify customer identities. It relies entirely on the data provided and may struggle with highly technical jargon or deep sarcasm.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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Customer Feedback Analyzer