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 style-transfergit 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/style-transfer)<a href="https://agentmods.dev/skills/zju-real/easel/style-transfer"><img src="https://agentmods.dev/badge/skills/zju-real/easel/style-transfer/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/style-transfer"><img src="https://agentmods.dev/badge/skills/zju-real/easel/style-transfer.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 | $0.00167 | $0.01468 |
| Opus 5.5 | $0.00067 | $0.00587 |
| Sonnet 5.5 | $0.00033 | $0.00294 |
| Haiku 4.5 | $0.00017 | $0.00147 |
Grade A, and why
style-transfer 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 1mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
文案风格迁移
把文案从一种风格改写成另一种风格，保留核心语义，只改表达方式。
输入
| 字段 | 必填 | 说明 |
|---|---|---|
text |
是 | 待改写的原文 |
target_style |
是 | 目标风格（见预设风格列表，或自由描述） |
style_reference |
否 | 一段目标风格的示例文本，用于学习风格特征 |
preserve_keywords |
否 | 必须保留的关键词/术语列表 |
intensity |
否 | 迁移强度：light（微调语气）/ medium（默认）/ full（彻底改写） |
预设风格
详见
references/style-rules.md，含完整执行清单。
| 风格 key | 名称 | 核心规则 | 句式特征 | 禁止事项 |
|---|---|---|---|---|
humorous |
搞笑 | 每 3-4 句至少一个梗点/夸张/自嘲 | 短句为主，反转节奏 | 不用"哈哈哈"、不解释梗 |
colloquial |
口语 | 去掉所有书面连词，用"嗯""就是""反正" | ≤15字/句，多用问句和感叹 | 不用"综上""此外""因此" |
literary |
文艺 | 意象化表达，用比喻代替直述 | 长短交替，有留白 | 不堆砌形容词 |
sharp |
犀利 | 直接下判断，不留余地 | 短促有力，多用句号 | 不用"可能""也许""个人觉得" |
professional |
专业 | 术语准确，逻辑链完整 | 并列/递进结构清晰 | 不用口语化表达 |
healing |
治愈 | 温暖共情，"你"开头的句子多 | 中等句长，节奏舒缓 | 不用反讽/批判 |
sarcastic |
毒舌 | 一针见血指出问题，不留面子 | 极短句+反问 | 不人身攻击 |
social_media |
社交媒体感 | emoji+口语+互动句式 | 分行多，每行≤20字 | 不写长段落 |
custom |
自定义 | 需提供 style_reference |
— | — |
输出
- 改写后的文案（纯文本）
- 风格变更摘要（改了哪些维度：用词、句式、语气、修辞、节奏）
- 写入
outputs/目录，文件名含原风格和目标风格标识
执行步骤
Step 1 — 风格分析
分析原文的风格特征，识别以下维度：
- 用词层：正式/口语、抽象/具体、术语密度
- 句式层：长短句比例、主被动、复句/简单句
- 语气层：客观/主观、冷峻/热情、距离感
- 修辞层：比喻、排比、反问、夸张等手法的使用频率
- 节奏层：段落长度、停顿感、信息密度
Step 2 — 目标风格建模
如果提供了 style_reference：
- 用同样维度分析参考文本，提取风格特征向量
- 识别参考文本的标志性手法（如特定句式、口头禅、节奏模式）
如果使用预设风格：
- 加载预设风格的特征定义
Step 3 — 迁移改写
按迁移强度执行改写：
- 逐段处理，保证语义完整性
- 按优先级调整：语气 → 用词 → 句式 → 修辞 → 节奏
- 保留
preserve_keywords中指定的关键词不变 - 保持原文的信息完整度（不增不减核心信息）
Step 4 — 一致性校验
- 检查全文风格是否统一（避免前后风格不一致）
- 检查核心信息是否保留完整
- 检查
preserve_keywords是否全部保留 - 如果迁移后文意偏移超过阈值，回退该段重写
Step 5 — 输出变更摘要
列出改写涉及的风格维度变化，让用户了解改了什么：
风格迁移: 正式 → 口语化
- 用词: 书面语替换为日常用词 (12处)
- 句式: 长复句拆为短句 (8处)
- 语气: 增加语气词和互动感 (6处)
- 修辞: 移除排比，增加口语化比喻 (3处)
Profile 感知
- 有 Profile：从
style.md读取账号的惯用风格偏好，作为目标风格的微调参考；从identity.md读取品牌调性，确保改写后仍符合品牌边界 - 无 Profile：直接按用户指定的目标风格改写，不做品牌约束
自研溯源与参考项目见同目录
EASEL-META.md。
What ships with it
2 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.
- 1mo ago First seen · 100 lines · 167 tokens per session scan A 0fae73efa972
style-transfer is a skill published in the GitHub repository ZJU-REAL/Easel (4,052 stars, last pushed 2d ago), licensed Apache-2.0. It adds 167 tokens to every session and 1,468 once invoked, about $0.0007 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 2026-09-03.
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digital-real-estate
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ai-newsletter
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p-video-2-pro-reference
Use when someone wants one cinematic clip that locks a subject from references — a product still, character, clip, or voice — using image or video references plus optional audio kept as the soundtrack or used as guidance. Not for text-only generation, 1080p, or drafts.
p-video
Use when someone wants a simple short clip from text or images — quick B-roll, drafts, or start/end frame animation. Not when the brief needs cinematic generation, highest quality, tight lip-sync, or imported audio at 1080p.