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14 data visualization skills. Trigger: charts, plots, figures, publication-quality graphics. Design: one skill per tool with code templates and academic formatting conventions.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill dataviz-skillsコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
SOC 職業分類に基づく
SKILL.md を表示中
| name | dataviz-skills |
| description | 14 data visualization skills. Trigger: charts, plots, figures, publication-quality graphics. Design: one skill per tool with code templates and academic formatting conventions. |
Select the skill matching the user's need, then read its SKILL.md.
| Skill | Description |
|---|---|
| algorithm-visualizer-guide | Guide to Algorithm Visualizer for interactive algorithm exploration |
| bokeh-visualization-guide | Guide to Bokeh for interactive browser-based research visualizations |
| chart-image-generator | Generate publication-quality chart images from research data |
| color-accessibility-guide | Colorblind-friendly palettes and accessible visualization design |
| d3-visualization-guide | Guide to D3.js for building custom interactive data visualizations |
| echarts-visualization-guide | Guide to Apache ECharts for interactive research data dashboards |
| geospatial-viz-guide | Create maps, choropleths, and spatial data visualizations for research |
| interactive-viz-guide | Interactive data visualization with Plotly, ECharts, and D3 |
| metabase-analytics-guide | Guide to Metabase for open-source research data analytics and dashboards |
| network-visualization-guide | Visualize networks, graphs, citation maps, and relational data |
| plotly-interactive-guide | Guide to Plotly.py for interactive scientific visualizations in Python |
| publication-figures-guide | Create journal-quality scientific figures with proper styling and accessibility |
| python-dataviz-guide | Publication-quality data visualization with matplotlib, seaborn, and plotly |
| redash-analytics-guide | Guide to Redash for SQL-driven research data dashboards and sharing |