Borrowing it
Nothing to install: this file belongs to rippleshe/sciplot-academic. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/rippleshe/sciplot-academic/master/.claude/skills/sciplot/SKILL.mdgit clone --depth 1 https://github.com/rippleshe/sciplot-academicWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rippleshe/sciplot-academic/sciplot)<a href="https://agentmods.dev/skills/rippleshe/sciplot-academic/sciplot"><img src="https://agentmods.dev/badge/skills/rippleshe/sciplot-academic/sciplot/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rippleshe/sciplot-academic/sciplot"><img src="https://agentmods.dev/badge/skills/rippleshe/sciplot-academic/sciplot.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.02894 |
| Opus 5 | $0.00061 | $0.01447 |
| Sonnet 5 | $0.00024 | $0.00579 |
| Haiku 4.5 | $0.00012 | $0.00289 |
Grade A, and why
sciplot scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SciPlot Academic — publication figure workflow
这个 Skill 的目标不是“调用一个画图函数”，而是把数据变成语义正确、视觉克制、可复核、可投稿的科研图。
1. 先判断，不要急着画
先回答四个问题：
- 图要证明什么？ 趋势、差异、分布、相关、构成、流向、模型诊断还是机制流程？
- 数据是什么结构？ 连续/类别/时间/矩阵/网络/层级/多阶段流；是否有重复测量、误差或显著性？
- 使用场景是什么？
nature/ieee/thesis/presentation，中文还是英文？ - 读者第一眼应该看到什么？ 主结论必须比装饰、网格、背景、次要系列更抢眼。
不要为了“高级”选择复杂图型。普通折线、散点、箱线、森林图如果更准确，就优先用它们。
2. 图型选择
常用入口：
| 研究问题 | 首选 |
|---|---|
| 单/多组趋势 | sp.plot() / sp.plot_multi() |
| 两连续变量关系 | sp.plot_scatter()；高密度用 sp.plot_hexbin() |
| 分类数值比较 | sp.plot_bar() / sp.plot_grouped_bar() |
| 前后/两条件变化 | sp.plot_dumbbell() |
| 分布与原始点 | sp.plot_raincloud() / sp.plot_beeswarm() |
| 经典分布摘要 | sp.plot_box() / sp.plot_violin() |
| 矩阵/相关性 | sp.plot_heatmap() / sp.plot_bubble_heatmap() |
| 模型比较与不确定性 | sp.plot_errorbar() / sp.plot_forest() / sp.plot_taylor() |
| 组成占比 | sp.plot_treemap() / sp.plot_donut() / sp.plot_waffle() |
| 排名变化 | sp.plot_bump() / sp.plot_circular_barplot() |
| 多阶段流动 | sp.plot_sankey() / sp.plot_alluvial() |
| 层级构成 | sp.plot_sunburst() |
| 网络结构 | sp.plot_network() / sp.plot_network_communities() |
| 多组分 | sp.plot_ternary() |
| 多维性能 | sp.plot_radar()，但维度少且尺度一致时才用 |
| 多面板论文图 | sp.figure_panels() + 各绘图 API |
完整函数表和参数细节按需读取 references/full-api.md；常见组合范式读取 references/recipes.md；配色与视觉语义读取 references/color-style.md。不要把整个参考文件无差别读入上下文，只读当前任务需要的部分。
如果参考文档与实际安装版本冲突，以运行时为准：
import inspect
import sciplot as sp
print(sp.__version__)
print(inspect.signature(sp.plot_scatter))
3. 默认工作流
Step A — 建立样式
import sciplot as sp
sp.setup_style("nature", palette="ocean", lang="en")
常用场景：
- 英文期刊：
nature或ieee，优先 PDF/SVG。 - 中文论文/学位论文：
thesis,lang="zh"。 - PPT/答辩：
presentation，PNG 300 dpi 足够。 - 基础出版风格由 SciPlot 默认层兜底：四边向内主/次刻度、完整轴脊、无框图例、稳定线宽和保存边距；不要在业务代码里重复写这些“补妆”设置。
- 不要因为用户没指定，就默认深色、渐变背景、玻璃拟态、阴影卡片。
Step B — 画主图
高层 plot_* 默认返回 PlotResult，可以继续拿 fig / ax 做最后 10% 的定制：
result = sp.plot_scatter(x, y, xlabel="Dose (mg)", ylabel="Response")
fig, ax = result
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 199 lines · 122 tokens per session scan A 3f6b10e5e4a3
sciplot is a skill published in the GitHub repository rippleshe/sciplot-academic (22 stars, last pushed 16d ago), licensed MIT. It adds 122 tokens to every session and 2,894 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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