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GitHub リポジトリ

ai4s-skills

ai4s-skills には ai4s-research から収集した 7 個の skills があり、リポジトリ単位の職業カバレッジとサイト内 skill 詳細ページを表示します。

収集済み skills
7
Stars
144
更新
2026-07-06
Forks
10
職業カバレッジ
5 件の職業カテゴリ · 100% 分類済み
リポジトリエクスプローラー

このリポジトリの skills

mindmap-render
ソフトウェア開発者

Generate beautiful, high-resolution mindmaps from Markdown unordered lists. Outputs interactive HTML, HD PNG, and PDF with colorful branch themes.

2026-07-06
integrity-auditor
その他の高等教育教員

Use when the user wants a paper audited for integrity issues — image misuse, numerical anomalies, logical gaps — and needs a reviewable evidence report. Works on external papers (PDF / DOI / arXiv) and on outputs from a local paper-writer run. Single-stage skill.

2026-07-03
experiment-suite
データサイエンティスト

Use when the user has a research question and needs a complete experiment package — design document, runnable code, results (measured or simulated with honest provenance), publication-grade figures, structured report. Single-stage, no Python runtime.

2026-07-01
ai4s-agent
ソフトウェア開発者

Use when the user wants an end-to-end AI4S research pipeline — broad direction or specific topic in, full research package out (exploration + literature survey + experiment + paper). Meta-skill that chains the four downstream skills in order. Pure markdown, no Python runtime.

2026-06-28
literature-survey
その他の高等教育教員

Use when the user wants a comprehensive literature survey on a specific research topic. Outputs a complete PDF survey (6–20 pages, 60+ real citations, 100+ recommended) with LaTeX source, taxonomy figures, and a classified literature table. Single-stage, no Python runtime.

2026-06-28
paper-writer
テクニカルライター

Use when the user wants a complete, publication-grade research paper on a specific topic — produces 200+ real citations, 4–8 publication-grade figures, and 7 sections of substantive prose compiled to PDF in one pass. No skeleton stage.

2026-06-28
research-explorer
市場調査アナリスト・マーケティングスペシャリスト

Use when the user has a vague research direction and wants to explore feasible specific topics. Outputs a structured analysis with candidate topics, innovation/feasibility scoring, and a pre-survey of 20–30 representative works. Single-stage, no Python runtime.

2026-06-28