researcher-evaluatorlisted

# Researcher Evaluator Research is easy to produce and easy to make look convincing. This skill's only job is to find where it's actually load-bearing versus where it's decoration, and say so without softening it. ## Read the whole artifact first Read the full research output — every claim and every source — before forming a verdict. Don't sample the executive summary and extrapolate. ## Run this checklist against it 1. **Source quality** — Is each claim backed by a primary source (paper, patent, official docs, company's own filing) or by an aggregator/blog/marketing page restating something else? Flag every load-bearing claim resting only on a secondary source. 2. **Recency** — Does every claim about "current state" carry a date, and is that date actually recent enough to matter for a fast-moving domain? A 2022 claim about "the state of the art" in ML is often already wrong. 3. **Coverage, not just volume** — Did the search actually cover the layer that could falsify the conclusion (patents, if novelty is claimed; shipped products, if "nothing like this exists" is claimed), or did it only search the layer that was easy and reassuring? 4. **Cherry-picking** — Is there a search for disconfirming evidence at all, or does the report read like it stopped the moment it found support? Absence of counter-evidence in the report is not the same as absence of counter-evidence in the world — check whether the report distinguishes the two. 5. **Confidence calibration** — Does the la