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収集済み skills
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収集済み skill 19 件中 19 件を表示しています。

職業分類
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説明

You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or planning anything new, in software or out of it (a talk, a business, a renovation).

原文の言語: 英語

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職業分類
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説明

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

原文の言語: 英語

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職業分類
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説明

Use when executing implementation plans with independent tasks in the current session

原文の言語: 英語

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職業分類
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説明

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes

原文の言語: 英語

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職業分類
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説明

Use when creating new skills, editing existing skills, or verifying skills work before deployment

原文の言語: 英語

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職業分類
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説明

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,…

原文の言語: 英語

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職業分類
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説明

Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify…

原文の言語: 英語

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職業分類
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説明

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…

原文の言語: 英語

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職業分類
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説明

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…

原文の言語: 英語

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職業分類
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説明

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.…

原文の言語: 英語

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説明

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.…

原文の言語: 英語

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職業分類
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説明

Structured manuscript/grant review with checklist-based evaluation. Use when writing formal peer reviews with specific criteria methodology assessment, statistical validity, reporting standards compliance (CONSORT/STROBE), and constructive feedback. Best for…

原文の言語: 英語

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職業分類
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説明

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.

原文の言語: 英語

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説明

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…

原文の言語: 英語

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説明

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…

原文の言語: 英語

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説明

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…

原文の言語: 英語

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説明

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…

原文の言語: 英語

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職業分類
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説明

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…

原文の言語: 英語

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職業分類
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説明

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…

原文の言語: 英語

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収集済み skill 19 件中 19 件を表示しています。