What problem does it solve? Turning open questions — market entry, technology selection, competitor teardowns, literature reviews — into trustworthy, cited research artifacts is slow and error-prone. This Skill structures the entire research lifecycle so every claim carries a source, freshness is tracked, and downstream planning documents consume the results without reprocessing. ## 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 verification. - Six shipped research type packs: market, domain, technical, competitive, user-voice, and academic literature, each with prioritized dimensions, source craft, freshness bars, and two-source claim classes, plus a select shape for choose-between decisions. - Deterministic tooling: a Python helper script handles citation cross-checks, claim ledger tallies, staleness computation, run-folder slugs, and safe HTML source-table escaping. - Use Case: Ask it to research whether to enter a new market; it holds a plan gate, fans out researcher subagents behind a research firewall, verifies load-bearing claims, and delivers research.md with an executive summary, source appendix, and staleness map. ## Quick Start Ask the assistant to run deep recon on a topic, for example: research the competitive landscape for my product idea and produce a cited decision-ready report.