9 skills published by richard-kim-79 across 1 repository. Together they weigh 5 988 tokens — that is what loading all of them at once would cost you in context.
9 skills 5 988 tokens total
> Generates rigorous devil's advocate critiques from methodological, theoretical, and practical perspectives. Use when the user asks to challenge their own work, find weaknesses, stress-test assumptions, anticipate reviewer objections, or argue against their research claims. Run BEFORE writing a draft for stronger papers.
> Writes a complete IMRaD-structured academic paper draft (Abstract, Introduction, Related Work, Methods, Results, Discussion, Conclusion, References) from the user's notes, documents, and wiki content. Use when the user asks to write a paper, create a manuscript, generate a draft, or produce academic writing. Do NOT use for short summaries — only for full paper-length output.
> Generates complete, runnable visualization code for research figures. Produces matplotlib/seaborn Python scripts for quantitative data and Mermaid diagrams for conceptual relationships, workflows, and taxonomies. Use when the user asks for charts, graphs, visualizations, figures, or diagrams. Produces code only — does not render images directly.
> Generates falsifiable, testable research hypotheses from notes, documents, and research content. Use when the user asks to brainstorm hypotheses, generate research questions, identify testable predictions, or discover patterns across their notes. Do NOT use for general Q&A — only when structured hypothesis output is needed.
> Simulates a full academic journal peer review with EIC decision and 3 independent reviewers (Methodology Expert, Domain Specialist, Devil's Advocate). Scores 1–10, provides major/minor Use when the user asks for a peer review, journal-quality feedback, readiness assessment, or to simulate reviewer responses before submission.
> Detects statistical errors, logical fallacies, and methodological issues in research content. Checks for p-hacking, correlation/causation confusion, underpowered samples, multiple comparisons problems, overgeneralization, and other common fallacies. Use when the user asks to validate statistics, audit quantitative claims, check methodology, or find logical errors. Returns minimal output on purely theoretical content — most useful after empirical data is present.
> Synthesizes research notes and documents to surface cross-cutting themes, unexpected connections, and high-level insights not visible in individual pieces. Use when the user asks for an overview, thematic analysis, synthesis across notes, or to find patterns in their research collection.
> Designs detailed experimental protocols to validate research hypotheses. Each protocol includes independent variable, dependent variable, controls, sample size with power analysis, timeline, and expected outcome. Use when the user asks to design experiments, plan a study, propose validation methods, or test a specific hypothesis. Works best after hypothesis generation.
> Organizes research notes into a structured wiki knowledge base with wikilinks ([[slug]] syntax), cross-references, and an auto-generated index. Use when the user asks to build a wiki, organize notes into a knowledge base, create an index, or sync notes into a structured format.