skills/43-wentorai-research-plugins/skills/analysis/dataviz/SKILL.md
14 data visualization skills. Trigger: charts, plots, figures, publication-quality graphics. Design: one skill per tool with code templates and academic formatting conventions.
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research dataviz-skillsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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 |
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
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.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.