Deep Research — Disciplined Meta-Research

Turn "research this topic" into an auditable, reusable investigation instead of a one-shot wall of text. The output is a folder you can return to in a month: every claim traces to a specific source file, the plan documents why each choice was made, and a refresh protocol lets you update it later without re-running everything.

This is the heavy, methodical end of research. It is not a fast overview — it is the workflow you reach for when getting the answer wrong costs more than the tokens spent getting it right.

How it differs from a quick research router

A router-style research skill (keyword-classify → delegate → short sequential search → markdown brief) is optimal when you need an answer fast and the decision risk is low. deep-research is the opposite trade: it pays for rigor. Use it when the answer feeds a strategy, an irreversible decision, a published artifact, or a hypothesis you need to actually test — situations where a shallow fallback would be a liability.

Concretely, deep-research adds what a fast overview does not: falsifiable hypotheses up front, parallel sub-agent fan-out across many channels, triangulation with explicit source-type diversity, a mandatory adversarial pass, per-source files with verbatim quotes, and a refresh_targets.md for delta-updates later.

The pipeline (9 phases)

Depth scales with the task — shallow runs the core phases inline; medium/deep add capability discovery, verification, and refresh targets.