ecommerce review mining agent
Transform raw ecommerce reviews into structured product insights and evidence-based action plans.
$5
Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+22 moreClaude CodeCursorCodex CLIMuseDots
OpenClaw
Manus
Grok
Grok Bot+18 agentsecommerce review mining agent
Example session with this skill installed
Analyze these 50 reviews for our ergonomic desk chair. Buyers say the assembly is hard but the lumbar support is great. Some mention the wheels squeak after a month. What should we do next?
- Read your context and instructions
- Compiled the ecommerce review mining
- Generated the document
The evidence confirms high satisfaction with lumbar support but reveals a significant recurring failure in wheel durability and assembly UX. While the comfort provides a strong marketing angle, the mechanical squeaking presents a long-term return risk that outweighs current positive sentiment.
Review Analysis Report
1. Inputs & Assumptions
| Item | Value | Status |
|---|---|---|
| Product | Ergonomic Desk Chair | Verified |
| Dataset Size | 50 Reviews | Verified |
| Primary Positive | Lumbar Support | Verified |
| Primary Negative | Assembly Complexity | Supported |
| Emerging Risk | Wheel Squeak (Post-30 days) | Hypothesis |
2. Analysis
| Dimension | Score | Evidence | Why It Matters |
|---|---|---|---|
| Complaint Frequency | 4/5 | Multiple mentions of assembly and wheels. | High friction in the first 30 days. |
| Complaint Severity | 3/5 | Squeaking starts after one month. | Leads to late-window returns and 1-star reviews. |
| Desired Outcomes | 5/5 | "Great lumbar support" mentioned frequently. | This is the core value proposition to protect. |
| Language Used | N/A | Need more verbatim text for specific phrasing. | Essential for optimizing ad copy. |
3. Key Risks / Gaps
- Mechanical Longevity: We don't know if the squeak is a lubrication issue or a structural bearing failure.
- Assembly Dropout: Complex assembly may be causing "silent returns" before a review is even written.
4. Recommended Next Move
Primary Action: Conduct a tear-down of the wheel assembly from the current batch to check for lubrication consistency.
- Support Action 1: Update the assembly manual with a QR code leading to a 60-second video.
- Support Action 2: Add a "Check Wheel Tension" step to the QA checklist at the factory.
5. Validation Plan
| Question | Evidence Needed | Cheapest Test | Pass Signal |
|---|---|---|---|
| Is the squeak universal? | Stress test 5 units for 100km | Lab cycle test | <10% failure rate |
| Does video help assembly? | Customer support tickets | Send video link to 50 buyers | 20% drop in assembly tickets |
Next steps
- Provide the full text of the "squeaky wheel" reviews to isolate the exact timing of the failure.
- Confirm if the assembly complaints center on specific tools or unclear diagrams.
- Review the factory spec sheet for the caster wheel bearings.
ecommerce-review-mining-agent.pdf
PDF · document
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Ecommerce teams often drown in raw review data, struggling to separate minor gripes from systemic product failures. Manually sorting through hundreds of comments leads to biased conclusions and missed opportunities for product improvements.
What it does
- Identifies recurring customer pain points and severity levels across review datasets.
- Extracts high-intent customer language for use in marketing copy and SEO.
- Maps desired outcomes and purchase triggers to reveal why customers actually buy.
- Quantifies feature requests and trust issues into a structured evidence ledger.
- Generates a validation plan to test hypotheses with minimal capital risk.
Why this beats prompting it yourself
General prompts often produce vague summaries that ignore statistical significance or conflate minor complaints with critical bugs. This skill enforces an evidence-based workflow that separates verified facts from hypotheses, preventing the AI from inventing market data or providing unsupported recommendations.
Use cases
- Analyzing competitor reviews to find gaps in their product offerings.
- Auditing a brand's own product line to prioritize the next engineering sprint.
- Mining customer language for high-converting landing page headlines.
- Identifying "failure modes" that lead to high return rates in specific categories.
Known limitations
Does not integrate directly with marketplace APIs; requires text-based review exports. Analysis is limited to the provided sample and cannot represent market-wide prevalence without representative datasets.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
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