Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LUNARTECH-X/superpowers --skill academic-figure-promptgit clone --depth 1 https://github.com/LUNARTECH-X/superpowersWrote 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/lunartech-x/superpowers/academic-figure-prompt)<a href="https://agentmods.dev/skills/lunartech-x/superpowers/academic-figure-prompt"><img src="https://agentmods.dev/badge/skills/lunartech-x/superpowers/academic-figure-prompt/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/lunartech-x/superpowers/academic-figure-prompt"><img src="https://agentmods.dev/badge/skills/lunartech-x/superpowers/academic-figure-prompt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00186 | $0.05421 |
| Opus 5 | $0.00093 | $0.02710 |
| Sonnet 5 | $0.00037 | $0.01084 |
| Haiku 4.5 | $0.00019 | $0.00542 |
Grade A, and why
Academic Figure Prompt 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 11d 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.
This is a copy
100% identical to Academic Figure Prompt — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Figure Prompt — 学术论文配图提示词生成器
为学术论文生成极其详细的英文提示词，供 AI 图片生成工具（NanoBanana / Gemini / Midjourney / DALL-E）生成顶会级别的专业学术配图。
核心理念
生成的提示词必须做到三点：信息密度极高、视觉风格精确、内容完整无遗漏。
宁可提示词过长过详细，也绝不能简化省略。学术配图的价值在于精准传达复杂信息，而非美观简洁。
工作流程
Step 1: 理解论文内容
在生成提示词之前，必须先充分理解论文内容：
- 阅读用户提供的论文/章节源文件（LaTeX、Word、PDF 等）
- 提取每个章节的核心概念、方法、模型架构、数据流
- 识别所有需要配图的位置及其内容需求
- 理解论文中的数学符号、变量含义、维度信息
Step 2: 分析参考图（如有）
如果用户提供了参考图，必须详细分析：
| 分析维度 | 提取内容 |
|---|---|
| 配色方案 | 主色、辅色、强调色的精确色值（如 #3A8F85） |
| 布局结构 | 流向（左→右 / 上→下）、分区方式、层次关系 |
| 模块样式 | 框的形状、边框粗细、填充色、圆角大小 |
| 标注方式 | 标题栏样式、公式标注、维度标注、箭头类型 |
| 信息密度 | 每个模块内的子细节数量、嵌入缩略图的使用方式 |
| 特殊元素 | 反馈环路、虚线框、跳接箭头、图例位置 |
Step 2.5: 配色方案选择（必须在生成提示词前完成）
在生成任何提示词之前，必须先向用户展示配色选项并等待确认。
展示以下内容：
请选择配色方案（输入编号或自定义）：
| # | 方案名 | 风格定位 | 主色 | 辅色 | 点缀色 |
|---|---|---|---|---|---|
| A | Okabe-Ito 学术标准 | Nature / Science / CVPR 推荐，色盲友好 | Steel Blue #0072B2 |
Warm Orange #E69F00 |
Bluish Green #009E73 |
| B | Blue 单色系 | 克制、模块详解图适用 | Navy #0072B2 |
Medium Blue #4A90D9 |
Light Blue #A0C4E8 |
| C | Teal + Amber | 现代感强，ICLR / NeurIPS 风 | Deep Teal #00897B |
Amber #FFB300 |
Soft Grey #ECEFF1 |
| D | Navy + Coral | 沉稳大气，IEEE 期刊风 | Deep Navy #1A3A5C |
Coral #E05A47 |
Warm Sand #F5ECD7 |
| E | Slate + Violet | 优雅冷调，医学 / 生物信息学风 | Slate Blue #3F51B5 |
Muted Violet #7E57C2 |
Pale Lavender #EDE7F6 |
| F | Forest + Gold | 厚重学术感，自然科学期刊风 | Forest Green #2E7D32 |
Gold #C49A00 |
Cream #F9F6EE |
| G | Minimal Grey | 极简灰度 + 单一强调色，arXiv 技术报告风 | Charcoal #263238 |
Steel #546E7A |
单一强调（用户指定） |
| H | 自定义 | 由用户提供色值或从下方工具选取 | — | — | — |
如需自定义配色，推荐以下工具：
- Coolors — 随机生成 + 锁定调整，导出色板：https://coolors.co
- ColorHunt — 精选高质量色板，支持标签筛选：https://colorhunt.co
- Adobe Color — 色轮 + 互补/类比/三分配色生成：https://color.adobe.com/create
- ColorBrewer — 专为学术数据可视化设计，支持色盲安全验证：https://colorbrewer2.org
- Viz Palette — 专为数据可视化配色，实时模拟色盲效果：https://projects.susielu.com/viz-palette
- Paletton — 色相环驱动配色方案设计器：https://paletton.com
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.
- 11d ago First seen · 391 lines · 186 tokens per session scan A 5f7f71a77fa4
Academic Figure Prompt is a skill published in the GitHub repository LUNARTECH-X/superpowers (16 stars, last pushed 3mo ago), licensed MIT. It adds 186 tokens to every session and 5,421 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to Academic Figure Prompt, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
html-ppt-zhangzara-monochrome
A grant proposal on CRISPR base-editing for sickle-cell disease — the hypothesis, the approach, the milestones, and the risk. Built as a decision-grade academic research deck for grant review committee.
html-ppt-zhangzara-pin-and-paper
A field-biology capstone on urban pollinator decline — the survey design, the data, the contribution, and the caveats. Built as a decision-grade coursework defense deck for faculty reviewers.
paper-illustration
A workflow for generating academic illustrations, such as architecture diagrams and method visuals, with image generation and repeated review. Claude plans and checks the figure during the process.
paper-illustration-image2
Generate publication-quality academic illustrations through a local Codex app-server bridge that uses Codex native image generation. This is a separate experimental alternative to paper-illustration, intended for Claude Code users who want a GPT-image-style renderer without modifying the original skill.
paper2video
Turn a research paper, a paper2assets package, or an existing PPT deck into a narrated MP4 video by fully delegating slide authoring to the installed ppt-master skill and fully delegating rendering, subtitles, timeline assembly, and strict media QA to the installed pptx2video skill and its public CLI. Resolves one…
figure-composer
Compose one publication-grade multi-panel figure. Start from a one-line claim plus immutable data Artifact Version references, or inspect an existing figure and draft its outline directly. Plan a 12-column panel outline, delegate one worker per panel, compose and inspect the result, then run at most three adversarial…