Paper Explainer
AI论文解读工具。面向产品经理、运营等非技术背景但对AI感兴趣的读者,将晦涩的学术论文转化为图文并茂的通俗研报:讲清楚”这个技术解决什么问题、有多厉害、对业务意味着什么”,配以极简科技风插图,并输出莫兰迪风格的 HTML 单文件。
Install
npx skills add PompeiiChan/skills-ExplainPaper --skill paper-explainergit clone --depth 1 https://github.com/PompeiiChan/skills-ExplainPaper.git /tmp/skills-ExplainPaper
mkdir -p ~/.claude/skills
cp -r /tmp/skills-ExplainPaper/. ~/.claude/skills/paper-explainerPersonal skills live in ~/.claude/skills. Use .claude/skills in a project folder to share a skill with your team.
From the SKILL.md
# Paper Explainer Skill
这是一个专门用于解读 AI/CS 领域技术论文的技能。
**目标读者**:产品经理、运营、业务负责人——对 AI 感兴趣,但没有算法/工程背景。
**核心目标**:去掉学术腔和工程术语,用”讲故事”的方式说清楚三件事:
1. 这个技术在解决什么痛点?
2. 它怎么做到的?(用类比,不用公式)
3. 对我们的产品/业务意味着什么机会?
## 核心能力
1. **故事化叙事**:不复述摘要,而是从”旧方案有什么死穴”开始讲,讲到”新突破怎么绕过去的”。
2. **视觉化解构**:对最难懂的概念画”极简对比图”或”流程演进图”,让不懂技术的人看一眼就明白。
3. **沉浸式研报**:生成莫兰迪配色的 HTML 单文件,带关键数字卡片、结论高亮块,提供优质的阅读体验。
## 依赖说明
- **图像生成**:本技能将直接生成 **SVG 矢量图代码**,无需外部 API 调用。这能确保生成的图表精准、清晰且风格统一。
- **联网搜索**:需要调用 WebSearch 或 WebFetch 工具来处理 URL 输入。
## 工作流程 (Workflow)
当用户提供一篇论文(PDF、文本或 **URL链接**)并要求解读时,**在开始之前先读取 `prompts/narrative.md` 和 `prompts/visual.md`**,然后按以下步骤执行:
### Step 0: 内容获取
- **本地文件**:直接读取。
- **URL**:用 `WebFetch` 抓取;内容截断时用 `WebSearch` 找 arXiv 纯文本页重新抓取;过滤导航/广告,只保留正文。
### Step 1: 理解论文,构建叙事脉络
读完全文后,在内存中整理三个问题的答案:
1. 旧方案的死穴是什么?(对应 Phase 1)
2. 新方案的核心思路是什么?最适合用什么类比解释?(对应 Phase 2)
3. 对 PM 而言最重要的结论和机会点是什么?(对应 Phase 4)
同时标记 **2-3 个”视觉锚点”**:最抽象、用文字说不清楚、但画一张图就能秒懂的概念。优先考虑 Before/After 对比图。
### Step 2: 按叙事框架撰写正文
严格按照 `prompts/narrative.md` 的五个 Phase 撰写,写到视觉锚点时直接内联生成 SVG(参考 `prompts/visual.md` 的规范)。
**SVG 生成要点**:
- 先选图表类型(对比图/流程图/模块图),再动笔
- SVG 直接嵌入文档,不保存为外部文件
- 每张图只讲一个概念,不超过 5 个元素
### Step 3: 生成 HTML 研报
按照 `prompts/html_style.md` 的规范将正文转为完整 HTML:
1. Phase 0 之后必须插入 `stat-cards`,展示 1-3 个最重要的对比数字
2. Phase 4 的”选型建议”必须用 `insight-box` 呈现
3. 代价/风险用 `warning-box` 标出
4. SVG 直接内联到 HTML 中,不使用 `<img>` 外链
5. 保存为 `./output/{paper_keyword}_report.html`
## 文件管理规范 (File Management)
- **输出根目录**:`./output/` (相对于当前项目工作目录)Details
- Skill name
paper-explainer- Repository
- PompeiiChan/skills-ExplainPaper
- License
- MIT
- Repo updated
- 22 June 2026
FAQ
What does the Paper Explainer skill do?
AI论文解读工具。面向产品经理、运营等非技术背景但对AI感兴趣的读者,将晦涩的学术论文转化为图文并茂的通俗研报:讲清楚”这个技术解决什么问题、有多厉害、对业务意味着什么”,配以极简科技风插图,并输出莫兰迪风格的 HTML 单文件。
How do I install the Paper Explainer skill?
skills CLI (Claude Code, Cursor, Codex and more): npx skills add PompeiiChan/skills-ExplainPaper --skill paper-explainer. Skills go in ~/.claude/skills (personal) or .claude/skills (project), and Claude loads them automatically when a task matches the description.
Is Paper Explainer popular?
It lives in PompeiiChan/skills-ExplainPaper, which has 47 GitHub stars and 5 forks.
Related skills
Research Craft
50nik1t7n/research-craft-skill
Use when planning, reviewing, or improving research work: choosing research problems, designing ML/AI experiments, reading papers, building experiment loops, analyzing outputs, writing research notes, or turning vague curiosity into a disciplined research plan. Emphasizes avoiding absorbed problems, upgrading information inputs beyond shared reading lists, and reasoning backward from outcomes the researcher genuinely wants to exist.
Genesis Architect
48maioio/genesis-architect
Use when starting a new project. Genesis finds real production failures from similar GitHub repos, then builds a working MVP immediately - not just documents. Two modes: fast-mvp (5 min research cap, builds right after) and deep-research (full analysis). Success = user can run the project. Triggers on: "genesis init [vision]", "I want to build X", "scaffold", "new project", "set up project", "start building", "create a tool", "make a CLI", "bootstrap", "בנה פרויקט", "צור פרויקט", "התחל פרויקט".
Labyrinth Exploration
47nasqret/labyrinth-exploration
Map and advance open-ended research programmes in mathematics, theoretical science and algorithms. Use for pushing bounds, attacking open conjectures, mapping what is still unknown, or independently refereeing and integrating research from agents or external models. Maintain proof tiers, provenance, frontier data and a dashboard. Not for a single self-contained proof, a one-off literature question or ordinary code review.
Paperbanana
59PlutoLei/paperbanana-skill
Use when user needs academic diagrams, methodology figures, statistical plots, or presentation slides from text descriptions or data files. Also use for evaluating generated figures against references.
Synthetic Market Research
43BayramAnnakov/synthetic-market-research
Fast, cheap market research using LLM-generated synthetic survey responses with Semantic Similarity Rating (SSR). Runs purchase intent, concept tests, and pricing research in minutes instead of weeks, at $0 per respondent. Based on PyMC Labs' validated methodology (90% correlation with real humans across 57 surveys). Triggers: synthetic research, market research, survey, purchase intent, concept test, consumer research, SSR, synthetic survey, product validation, pricing research, Likert scale.
Math Skill
54Wholiver/Math.Skill
A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling
Browse the most popular Claude skills, all research & science skills, or write your own with the skills generator.