What problem does it solve? Deciding whether a published systematic review or meta-analysis can be trusted requires a structured, defensible method rather than gut feel. This Skill applies AMSTAR 2 (Shea et al., BMJ 2017) to answer the 16 checklist items from the review's report, flag flaws in the seven critical domains, and derive the overall confidence rating — High, Moderate, Low, or Critically low — without ever summing items into a score. ## Core Features & Use Cases - Full 16-item appraisal: Answers every item with Yes / Partial Yes / No (or "No meta-analysis conducted" for items 11, 12, 15), quoting the evidence from the report, with Partial Yes restricted to items 2, 4, 7, 8, 9. - Mechanical Box 2 rating: Classifies critical flaws versus non-critical weaknesses against pre-specified critical domains (default items 2, 4, 7, 9, 11, 13, 15) and derives the confidence rating with an explicit rule trace. - Deterministic companion tool: scripts/amstar2.py (Python standard library only) validates answers, counts flaws, applies the Partial Yes convention, and prints the rating with warnings — removing counting slips while leaving judgement to the appraiser. - Use Case: Before citing a meta-analysis of remote-work productivity in a board memo, run the appraisal to discover unlisted excluded studies and uninvestigated publication bias, yielding a Critically low rating that tells you to verify the primary trials first. ## Quick Start Ask the AI to appraise the attached systematic review with AMSTAR 2 and report the overall confidence rating with per-item evidence.