Easel is an open-source content workspace and AI agent for managing social media from trend discovery through planning, content creation, publishing, and performance analysis. It is intended for social media creators and maintains separate account profiles, content projects, platform adaptations, and publishing records. The catalogue skills support these content and publishing workflows.
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 ZJU-REAL/Easel --skill paper-explainergit clone --depth 1 https://github.com/ZJU-REAL/EaselWrote 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/zju-real/easel/paper-explainer)<a href="https://agentmods.dev/skills/zju-real/easel/paper-explainer"><img src="https://agentmods.dev/badge/skills/zju-real/easel/paper-explainer/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/zju-real/easel/paper-explainer"><img src="https://agentmods.dev/badge/skills/zju-real/easel/paper-explainer.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 | $0.00114 | $0.02854 |
| Opus 5.5 | $0.00046 | $0.01142 |
| Sonnet 5.5 | $0.00023 | $0.00571 |
| Haiku 4.5 | $0.00011 | $0.00285 |
Grade A, and why
paper-explainer 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 8d 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
科研论文解读（论文 → 视频 / 图文）
把一篇论文讲成普通人/同行都爱看的视频号视频或图文。核心中间产物是一份 结构化 asset library（一次解析+提炼，视频与图文两条产线共用，不重复调 LLM）。 确定性 IO（拉论文/解析 PDF/骨架）走
scripts/paper_ingest.py；提炼与分镜脚本由你 LLM 完成——这是本 SKILL 的核心价值。
视频转图文（反向）见 video-to-article；纯格式转换见 doc-convert； 只做图表见 chart-visualization / infographic；发视频号见 skill-channels-upload。
输入
| 字段 | 必填 | 说明 |
|---|---|---|
| 论文 | 是 | arxiv id（2401.12345）/ arxiv 链接 / 本地 PDF 路径（没给就问） |
| 目标形态 | 否 | 视频（默认，视频号/B站）/ 图文（知乎/公众号）/ 两者都要 |
| 视频画幅 | 视频时必填 | 用户或上游任务未明确横版/竖版（或 16:9/9:16/具体分辨率）时，进入视频制作前必须追问并等确认；不得按平台、Profile 或默认值静默推断，已明确则不重复问 |
| 受众深度 | 否 | 大众科普（默认）/ 同行向（更专业） |
| 时长 | 否 | 视频默认 2–4 分钟（视频号中视频） |
产物结构（outputs/论文简称/）
article.md 图文版（知乎/公众号）
final.mp4 成片
assets/ paper.pdf / parsed/ / asset-library.json / script.md
slide-plan.json 结构化分页（页面唯一输入，口播与屏幕文字分离）
slides/ 稳定渲染的逐页 PNG + HTML + audit report
slides-contact-sheet.jpg 整套视觉复核图
脚本（相对项目根）：paper_ingest.py（解析）+ render_slides.py（分页校验/渲染/审计）。
执行步骤
1. 取原文 + 解析
- 环境自检：
python skills/builtin/paper-explainer/scripts/paper_ingest.py check（看 pdfplumber / MinerU token / 代理；缺 pdfplumber 则pip install pdfplumber）。 - 拉论文：
paper_ingest.py fetch --paper <id/url/本地pdf> -o outputs/论文简称/assets/paper.pdf。 - 解析：
paper_ingest.py parse -i outputs/论文简称/assets/paper.pdf -o outputs/论文简称/assets/parsed/（有MINERU_API_TOKEN走 MinerU 含公式/图表结构化，否则 pdfplumber 纯文本 + 尽力抽图）。
2. 结构化提炼（你来做，核心）
- 生成骨架：
paper_ingest.py skeleton -o outputs/论文简称/assets/asset-library.json。 - 读
outputs/论文简称/assets/parsed/content.*，按references/paper-distill-schema.md填满outputs/论文简称/assets/asset-library.json：one_liner（一句话讲清干了啥）、problem/prior_gap、contributions（≤3 条）、method（含通俗类比 analogy）、key_figures（挑 2–4 张关键图，每张写plain大白话解释）、results（含关键数字）、limitations、takeaway、terms（术语通俗表）。 通俗化方法见references/explain-methodology.md（公式/图表→大白话、类比法、避免堆术语）。 - 忠于原文：不夸大、不编造结论；拿不准的地方标注，别臆测（学术内容错了会被同行抓）。
What ships with it
10 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.
- EASEL-META.md 2.1 KB
- references/explain-methodology.md 3.2 KB
- references/paper-distill-schema.md 2.7 KB
- references/platform-adapt.md 2.3 KB
- references/slide-design.md 12 KB
- references/slide-plan.example.json 2.9 KB
- references/slide-plan.style-transfer.example.json 1.8 KB
- references/video-storyboard.md 3.1 KB
- scripts/paper_ingest.py 19 KB runs code
- scripts/render_slides.py 49 KB runs code
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.
- 8d ago Changed 8232b67909cb
- 1mo ago First seen · 102 lines · 114 tokens per session scan A 5ccf7747707e
paper-explainer is a skill published in the GitHub repository ZJU-REAL/Easel (3,918 stars, last pushed yesterday), licensed Apache-2.0. It adds 114 tokens to every session and 2,854 once invoked, about $0.0005 per session on Opus 5.5. 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.
Other skills, from other repositories
interactive-explainer
Use when someone wants an educational explainer with a host and characters — history or science shorts with dialogue, not voiceover-only B-roll.
hidden-talents
Interview you in depth to surface overlooked or suppressed talents and write your Personal Talent Manual.
socratic
Diagnose a confusion Socratically, one question at a time, until the question actually worth answering surfaces — then answer that one.
two-layer-explain
Explain a concept twice — for a newcomer and for an expert — then bridge the two layers, flag the likely misunderstandings, and set three check questions.
deconstruct
Reverse-engineer why a finished piece of work succeeds and extract what transfers to your own — rules, a checklist, and a first exercise.
project-scaffolder
Use when a learning plan requires runnable chapter files, a multi-lesson coding course, a project-based route, or a workspace the learner will open in an editor.