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Found 6 Skills
Organize research — manage references, notes, and collaboration.
A qualitative research assistant tool based on Braun & Clarke's Reflexive Thematic Analysis framework. Supports two input modes: (1) Provide raw interview text directly → The skill completes initial TA coding for each document, then proceeds to theme identification after summarization; (2) Provide existing initial coding pool → Directly enter the process of clustering, review, and naming suggestions. Outputs a structured candidate theme table, clearly marking codes with ambiguous boundaries and naming suggestions to be decided by researchers. This skill is triggered when users mention terms such as "thematic analysis", "theme coding", "help me cluster codes", "extract themes from codes", "Braun Clarke", "candidate themes", "how to categorize these codes into themes", "help me check the theme structure", "conduct thematic analysis on interviews". Note the difference from grounded-coding: grounded-coding focuses on category construction and theoretical relationships for procedural grounded theory; thematic-analysis focuses on semantic theme identification following the Braun & Clarke approach, outputting theme structures rather than theoretical propositions.
Use when analyzing research datasets, cleaning tabular data, selecting statistical tests, producing result tables, creating publication figures, or moving notebook logic into reproducible code.
Search for academic literature, empirical evidence, and scholarly research using the Dimensions database. Use when seeking research papers to support product decisions, find empirical studies, conduct literature reviews, explore funding patterns, validate hypotheses with academic sources, or discover research trends. Supports publications, grants, patents, clinical trials, and researcher profiles. Triggers on requests for "academic evidence", "empirical research", "find studies", "literature search", or "research to support decisions".
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
Find and evaluate research datasets for any scientific question. Teaches how to reason about data needs, search across public repositories, evaluate dataset fitness, and identify access requirements. Use whenever users ask to find data, search for datasets, identify cohort studies, or need data for analysis. Also use when users ask about a specific survey or cohort (NHANES, HRS, UK Biobank, TCGA, etc.), when they want to know what data exists for a research question, or when they need to compare available data sources. If the user mentions "where can I get data" or "is there a dataset for X", this is the right skill.