Individual skill
deep-research
Use when a wrong answer is expensive: strategy research, comparing several products or markets, validating a hypothesis with outside data. Fans out searches and sub-agents, triangulates each claim across 3+ sources and saves every source with quotes. Quick facts: answer directly.
research/deep-research/skills/deep-research/SKILL.md
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# 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)
