Haojae

Scipilot Figure Skill — Development skill for Claude Code

Development community

SciPilot Skills family - Publication-grade scientific figure copilot for Claude Code.

How to install Scipilot Figure Skill

This entry records only its repository, not the path inside it, so there is no exact command to give. Open Haojae/scipilot-figure-skill and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Scipilot Figure Skill does

SciPilot Skills family - Publication-grade scientific figure copilot for Claude Code.

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README

scipilot-figure-skill

SciPilot Skills family. Scientific data **visualization advisor** — thinks first, plots second. SciPilot Skills 家族成员 — 科研数据**可视化顾问**，先思考后绘制。

[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Python: 3.9+](https://img.shields.io/badge/Python-3.9%2B-3776AB.svg)](#dependencies--依赖) [![Status: v2.1.0](https://img.shields.io/badge/Status-v2.1.0-success.svg)](#) [![Advisor Mode](https://img.shields.io/badge/Mode-Advisor%2BPlotter-c41e3a.svg)](#为什么这不只是个画图工具) [![Stack](https://img.shields.io/badge/Stack-matplotlib%20%7C%20seaborn%20%7C%20plotly-orange.svg)](#) [![Claude Code Skill](https://img.shields.io/badge/Claude%20Code-Skill-orange.svg)](https://claude.com/claude-code)

A [Claude Code](https://claude.com/claude-code) / [Codex](https://github.com/openai/codex) / Cursor Skill that does **two things in order**: (1) profiles your data and recommends the right chart for the argument you want to make, (2) renders it at publication grade for Nature / Science / IEEE / Elsevier / PNAS / Chinese journals. Built on **matplotlib + seaborn + SciencePlots** (static) and **plotly** (interactive), with **CJK font auto-configuration** so Chinese text never renders as boxes.

[中文文档](#中文文档) | [English](#english)

中文文档

概览

科研工作者最大的画图痛点往往不是"不会用 matplotlib"，而是"手上一堆数据，不知道该用什么图把结论讲清楚"。`scipilot-figure-skill` 是 SciPilot Skills 家族的第二个成员，专做这件事——

**它的首要能力是【思考与判断】，其次才是【绘制】。**

为什么这不只是个画图工具

普通画图工具           scipilot-figure-skill
─────────────         ─────────────────────
你说 "画柱状图"  →   先 EDA：每列类型、样本量、分布、异常值、相关性
直接 plt.bar()        再问："你想论证什么？组间差异？关系？趋势？"
                     按数据特征 + 论证目标查决策框架推荐图型
                     n=5 想画均值柱时主动拦截 → 改用 stripplot
                     维度 > 12 时建议拆图，不硬塞
                     最后才进入绘制 → 期刊规范 → 自检 → 导出

核心工作流（8 步）

0. 理解任务   ── 这张图要论证什么？数据在哪？
   ↓
1. 剖析数据   ── profile_data.py：列类型/样本量/分布/异常/相关
   ↓
2. 选图       ── chart_selection.md：按数据形态+论证目标决策
   ↓ (主动拦截 → viz_pitfalls.md)