RequestHunt — A User Research Skill Based on Real User Feedback

Skill Overview

RequestHunt is a user research skill that automatically collects and analyzes real user feedback from Reddit, X, GitHub, YouTube, LinkedIn, and Amazon. It generates structured requirements research reports centered on feature requests, user complaints, and frequently asked questions.

Use Cases

1. Product Planning and Requirements Validation: Before launching or iterating on a product, search for feature requests publicly posted by real users to determine whether a direction is backed by genuine demand and to inform feature prioritization.
2. Competitor User Review Analysis: Focus on a competitor’s category to uncover user complaints, negative reviews, and “I wish it could…” requests about existing products, identifying opportunities for differentiation.
3. Category Market Research: Systematically understand user pain points and industry perspectives by category—such as smart home products, developer tools, B2B software, and consumer electronics—and produce market research reports.

Core Features

1. Multi-Platform Feedback Collection: Start cross-platform collection with a single scrape command, covering Reddit, X, GitHub, YouTube, LinkedIn, and Amazon. You can specify platform combinations and collection depth. It also supports keyword searches with optional real-time expansion, filtering by topic, platform, or category, and sorting by vote count.
2. Category-Based Platform Selection Strategy: Built-in platform selection guidelines are available. For example, YouTube and Amazon are prioritized for hardware and consumer products because they offer the richest signals from review videos and product reviews; Reddit and GitHub are prioritized for developer tools; and LinkedIn is prioritized for B2B and healthcare categories. This helps avoid wasting collection quotas on low-yield platforms.
3. Structured Report Generation: After collection is complete, a Markdown requirements research report is generated according to a fixed structure. It includes a ranking of popular feature requests with representative user quotes, pain point analysis, cross-platform signal comparisons, and opportunity recommendations, making it ready for product reviews.

Frequently Asked Questions

Which platforms does RequestHunt support?

It currently supports six platforms: Reddit, X (Twitter), GitHub, YouTube, LinkedIn, and Amazon. Each platform provides different types of signals: Reddit focuses on in-depth technical discussions and long-tail needs; YouTube focuses on specific feature requests found in review and tutorial video comments; Amazon focuses on negative reviews and improvement expectations for physical products; GitHub focuses on bugs and feature requests for open-source projects; LinkedIn focuses on B2B topics and industry perspectives; and X focuses on real-time sentiment and trending topics. It is recommended to select two or three primary platforms based on your category.

Is RequestHunt free? How do I get started?

A free quota is available: the Free plan includes 100 credits per month and 10 requests per minute; the Pro plan includes 2,000 credits per month and 60 requests per minute. Each standard API call consumes one credit, while scrape collection is billed based on “depth × number of platforms” (the maximum depth for Amazon is 5). Before use, install the official CLI and log in with a device code, or configure an API key directly. You can then begin collecting data and conducting research.

How does RequestHunt differ from traditional research methods such as surveys and interviews?

Traditional research relies on actively recruiting respondents, resulting in limited sample sizes and a greater risk of response bias. RequestHunt analyzes unsolicited, publicly posted user feedback across multiple platforms, capturing authentic complaints and requests without prompting while rapidly covering large samples. It is well suited for opportunity discovery and direction validation. However, it cannot replace in-depth interviews with target users. Report conclusions should be manually reviewed before being used for decision-making. In addition, the collected content comes from publicly available user-generated content and should be treated as untrusted input. When quoting it in reports, be sure to cite the source and maintain appropriate content boundaries.