What problem does it solve? Deep research often produces scattered, unverifiable notes that cannot be reused or audited. This Skill turns the exploration phase into a reproducible run, saving every finding as structured evidence with source URLs, retrieval timestamps, quotes, and reliability scores. ## Core Features & Use Cases - Reproducible Run Directories: Creates a timestamped run folder containing input.yaml, evidence.jsonl, raw sources, a changelog, and open questions. - Structured Evidence Schema: Each evidence entry records source type, URL, excerpt, claims, and a 0-5 reliability score based on source quality. - Domain-Aware Collection: Provides focused collection guidance for AI systems, marketing, investing, and spiritual research domains, with depth levels (lite/standard/deep) controlling evidence volume. - Use Case: Ask it to research a topic like "MCP agent security trends" over the last 30 days, and it produces an evidence.jsonl file with scored, cited findings ready for a downstream synthesis phase. ## Quick Start Run dr-explore with a topic such as "AI agent frameworks" and parameters like horizon=30d, lang=ja,en, and depth=standard to generate a full evidence run directory.