Automates the discovery and collection of multi-source research data into structured, evidence-based datasets.
Deep Research Explorer (dr-explore) is a specialized skill designed for the initial phase of high-integrity information gathering. It traverses global news, social media, academic papers, and official documentation to build a robust evidence library. By standardizing the collection process into reproducible 'runs,' it ensures every claim is backed by a URL, timestamp, and citation. This skill is particularly effective for professionals in AI systems, marketing, or investment research who require high-integrity primary data organized for immediate synthesis or implementation.
Key Features
01Reproducible research runs with structured directory and YAML configuration
02Automated evidence reliability scoring based on source authority
03Multi-source data harvesting across Web, SNS, Docs, and academic papers
04Domain-specific focus modes for AI systems, marketing, and investment research
05Security-first data handling that neutralizes external prompt injection attempts
060 GitHub stars
Use Cases
01Performing investment due diligence by aggregating catalysts, fundamentals, and risk factors
02Gathering technical implementation patterns and documentation for AI architectures
03Conducting deep-dive competitive analysis and market trend mapping
