Agent skill · alirezarezvani
meta-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.
What it needs
About 4k tokens when loaded.
What this skill does
Meta-Research — Disciplined Multi-Source 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. meta-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, meta-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 refreshtargets.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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills alirezarezvani/meta-research