academic-research

To automate a systematic literature review, this Skill executes a six-phase workflow covering Discovery, Deep Reading, Citation Chain Analysis, Database Verification, Synthesis, and Reporting across Semantic Scholar, OpenAlex, PubMed, and arXiv to ensure reproducible evidence synthesis.

Citation chain analysis in academic research traces reference trails backward and forward across databases like CrossRef and OpenAlex to verify citation integrity, map foundational literature, and enforce zero-hallucination during knowledge synthesis.

Yes, the academic research workflow applies to both STEM and social sciences by applying domain-specific checks and cross-database fallback chains across Europeana and SSRN/IDEAS to ensure comprehensive evidence synthesis across different research domains.

Preventing AI hallucinations in a literature review requires enforcing a zero-hallucination protocol through cross-database verification, citation tracing, and ScholarEval integration to evaluate outputs and ensure all synthesized references are verified against actual database records.

Yes, literature review automation persists research knowledge across sessions by integrating a Reflexion Cycle to capture lessons learned and storing project memory in .tad/project-knowledge, enabling cross-session knowledge reuse for ongoing academic research planning.

The best way to start a research planning task is to provide a research query and optional constraints, which triggers the six-phase protocol to automatically handle discovery, deep reading, and database verification for your systematic review.