notebooklm-research

This skill creates a clinical baseline Table 1 from XLSX or CSV data using design-aware rules: baseline p-values are hidden for RCTs by default, while observational studies emphasize SMD and multiple-testing warnings. It produces self-contained HTML plus Word and LaTeX files, masks identifier columns, and excludes survival, regression, and mixed-effects analyses.

The skill searches 20,972 Arabic-English records from the Quran, Sahih al-Bukhari, and Sahih Muslim through Supabase and pgvector. It supports exact lookup, topical search, cross-referencing, and checking whether a report appears in the two Sahih collections, while presenting returned text with citations instead of reconstructing it from memory. It does not issue fatwas and requires a Supabase deployment plus embedding setup.

Six-Layer Product Research guides Claude Code through a structured problem-definition process before solution work begins. It examines customer language, ecosystem perspectives, behavioral frictions, failed status-quo attempts, new technology enablers, and the changes a team can realistically influence. Separate findings are then synthesized into a multi-perspective definition that helps product teams test assumptions and understand why earlier approaches failed.

paper-review.skill guides Claude through a four-stage review of an ML or AI paper: map recent work, test the claimed novelty, write the review, and verify the remaining references. It pushes weaknesses to cite the paper's own evidence and keeps genuine author questions separate from revision requests. It is useful for conference review, pre-submission red teaming, and rebuttal preparation, but is less suited to pure theory, non-English papers, or very narrow fields.