Skip to main content
Manusで任意のスキルを実行
ワンクリックで
CRAG666
GitHub クリエイタープロフィール

CRAG666

1 件の GitHub リポジトリにある 20 件の収集済み skills をリポジトリ単位で表示します。

収集済み skills
20
リポジトリ
1
更新
2026-07-15
リポジトリマップ

skills がある場所

収集済み skill 数が多いリポジトリを、このクリエイターカタログ内の比率と職業範囲とともに表示します。

リポジトリエクスプローラー

リポジトリと代表的な skills

scientific-writing-en
編集者

Use whenever the user writes, drafts, revises, edits, polishes, or translates scientific or academic prose in ENGLISH - Q1 research articles, theses, dissertations, abstracts, introductions, methods, results, discussions, conclusions, acknowledgments, literature reviews, grant proposals, conference papers, or any IMRaD section. Provides 5000+ pre-cooked, idiomatic, native-academic phrases plus paragraph- and sentence-level guidance for every standard section of a scientific document, avoiding robotic / LLM-style prose. Triggers: "write the introduction", "draft the abstract", "help me with the discussion", "polish this scientific paragraph", "translate this paper to English", "this sounds like AI, rewrite it", "write the methods section", "I need phrases for...", "academic English", "make this sound more academic", "make this Q1-ready".

2026-07-15
citation-management
ソフトウェア開発者

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 citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.

2026-06-20
latex-posters
グラフィックデザイナー

Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication.

2026-06-20
aeon
データサイエンティスト

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.

2026-06-14
literature-review
その他の生物科学者

Conduct comprehensive, systematic literature reviews across multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). Use for systematic reviews, meta-analyses, scoping reviews, research synthesis, and broad literature searches, including topic overviews, surveys, and state-of-the-art summaries that gather and organize the major work on a subject by theme or method. Produces professionally formatted markdown and PDF documents with verified citations in multiple styles (APA, Nature, Vancouver, etc.).

2026-06-14
markitdown
ソフトウェア開発者

Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.

2026-06-14
matplotlib
ソフトウェア開発者

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

2026-06-14
ml-science-discipline
その他の生物科学者

Enforces rigorous scientific methodology for machine learning experiments intended to support publication-grade claims (Q1 journals, conference papers, regulated decisions). Use this skill when designing an ML pipeline, splitting datasets, evaluating performance, selecting features, tuning hyperparameters, comparing models, quantifying uncertainty, validating externally, or preparing results for a paper. Consult it for any task where experimental validity, reproducibility, or publication standards (TRIPOD+AI, CLAIM, STARD-AI, CONSORT-AI) are in scope. Routine "train a model" or "compute accuracy" requests do NOT automatically trigger this skill unless results will be reported, compared, or acted on.

2026-06-14
このリポジトリの収集済み skills 20 件中、上位 8 件を表示しています。
1 件中 1 件のリポジトリを表示
すべてのリポジトリを表示しました