28건(spacering-net의 저장소) · 인기순
Show results as interactive UI inside Codeg's chat (cards, tables, charts, tabs, accordions, badges, progress bars, simple forms) by writing a json-render spec code block in your reply. Use when the user invokes this skill or asks to see something as a card, table, chart, dashboard or other visual layout in Codeg.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Search 10 academic literature APIs for papers, preprints, citations, and open-access full text, and return results with reproducible provenance. Covers PubMed, PMC (full text), bioRxiv, medRxiv, arXiv, OpenAlex, Crossref, Semantic Scholar, CORE, Unpaywall. Use when searching for papers, citations, DOI/PMID/arXiv lookups, abstracts, full text, open-access PDFs, preprints, citation graphs, author publications, or any scholarly literature query. Triggers on mentions of any supported database or requests like "find papers on X", "look up this DOI", "who cites this paper", or "get me the PDF".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Sample-size and statistical power calculations for planning studies. Use whenever someone asks "how many subjects/samples/replicates do I need", wants an a priori power analysis, a minimum detectable effect (MDE), a power curve, or needs to justify a sample size for a grant, IRB protocol, or pre-registration. Covers closed-form power for t-tests, ANOVA, proportions, correlations, chi-square, and regression, plus simulation-based (Monte Carlo) power for designs with no formula — logistic/Poisson regression, mixed models, cluster-randomized trials, survival, and interactions. Use this skill even when the request only mentions an effect size, alpha, or "80% power" without saying "power analysis" explicitly. For laying out the study (randomization, blocking, factorial/DOE, crossover, sequential designs) use experimental-design; for analyzing data already collected and reporting it use statistical-analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when receiving code review feedback, before implementing suggestions, especially if feedback seems unclear or technically questionable - requires technical rigor and verification, not performative agreement or blind implementation
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when creating new skills, editing existing skills, or verifying skills work before deployment
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Evaluate scientific claims and evidence quality. Use for assessing experimental design validity, identifying biases and confounders, applying evidence grading frameworks (GRADE, Cochrane Risk of Bias), or teaching critical analysis. Best for understanding evidence quality, identifying flaws. For formal peer review writing use peer-review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-analysis.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when implementing any feature or bugfix, before writing implementation code
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you have a written implementation plan to execute in a separate session with review checkpoints
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you have a spec or requirements for a multi-step task, before touching code
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when executing implementation plans with independent tasks in the current session
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.