What problem does it solve?
Researchers and AI agents often produce outputs that may contain citation errors, logical fallacies, or incomplete reporting. This Skill provides an automated self-review process to identify and correct such issues, improving the validity and reliability of scientific communications.
Core Features & Use Cases
- Citation Verification: Cross-checks DOIs and PMIDs against authoritative sources to detect fabricated or incorrect citations.
- Logical Consistency Analysis: Detects fallacies such as circular reasoning, false dichotomies, and contradictions within research texts.
- Evidence Grading: Assesses whether claims align with the strength of supporting evidence based on predefined hierarchies.
- Statistical Rigor Checks: Validates proper interpretation of p-values, confidence intervals, and sample sizes.
- Completeness Checks: Ensures all required reporting sections are present per guidelines like PRISMA or CONSORT.
Quick Start
Input your research output into the AI assistant and ask it to review for citation accuracy, logical coherence, evidence strength, statistical correctness, and report completeness before sharing it publicly or submitting it for peer review.
