What problem does it solve? Turning open-ended research questions into trustworthy, decision-ready artifacts is slow and error-prone: claims lack sources, reports go stale, and raw research output gets reprocessed repeatedly. This Skill structures the entire research lifecycle so every claim is cited, verified, and tracked for staleness. ## Core Features & Use Cases - Three research modes: Draft a deep-research prompt for external tools (ChatGPT, Gemini, Perplexity), Process a finished report into a distilled cited summary, or Run native research through parallel web fan-out with subagents. - Typed research packs: Shipped packs for market, domain, technical, competitive, user-voice, and academic literature research, each with prioritized dimensions, source craft, freshness bars, and two-source verification classes. - Verification and lifecycle: Claims ledger with verified/disputed/unverified status, optional red-team passes, citation cross-checking, and Refresh/Deepen workflows that re-verify only stale claims. - Use Case: Before choosing between two vendor platforms, run a select-shape competitive research run that produces a weighted decision matrix with cited pricing, a named runner-up, and a staleness map telling you when to re-check. ## Quick Start Ask the assistant to run deep recon on your research question, for example: "Run market research on the European e-bike subscription market to decide whether we should enter it."
