Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ZJU-REAL/Easelnpx agentmods add skills/zju-real/easel/xhs-note-creatorWrote 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/xhs-note-creator)<a href="https://agentmods.dev/skills/zju-real/easel/xhs-note-creator"><img src="https://agentmods.dev/badge/skills/zju-real/easel/xhs-note-creator/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/xhs-note-creator"><img src="https://agentmods.dev/badge/skills/zju-real/easel/xhs-note-creator.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 | $0.00125 | $0.02495 |
| Opus 5 | $0.00063 | $0.01247 |
| Sonnet 5 | $0.00025 | $0.00499 |
| Haiku 4.5 | $0.00013 | $0.00249 |
Grade A, and why
xhs-note-creator 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 6d 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
小红书笔记创作
把一个主题 + 可选素材，生成图文卡片组或短视频分镜脚本，按规范输出到 outputs/。
核心理念
小红书读者看的不是长文，是卡片组或短视频。长文原稿只是中间产物。
- 图文帖：3-9 张 3:4 竖版卡片（cover + content × N + ending），每张 ≤80 字
- 视频帖：15-90 秒竖屏视频分镜 + 封面卡
爆款 5 大原则
- 真实素材优先：截图、对比图 > 纯 AI 生图
- 聚焦核心卖点：用公式判断优先级（稀缺性 × 实用性 × 可感知）
- 高级感视觉（走 card-design，别做糙卡）：卡片视觉统一走 card-design 设计系统（选风格锁 spec、字大字细、填满画幅、禁大 emoji 廉价感）。文案要口语化、真实感，但视觉不能糙——想要「素人 / 手账」质感就选 card-design 的手账贴纸 / 奶油温柔风格（仍是高质量 HTML 渲染，不是糙 t2i 大 emoji）。
- 痛点导向："能解决什么问题" > 堆砌功能列表
- 快速迭代：V1(60 分) → 用户反馈 → V2(70 分)
详细方法论：references/xiaohongshu-viral-methodology.md
工作流
按顺序执行，不要跳步。
Step 0 — Intake（必问，一次问完）
- 主题 / 目标读者 / 核心观点
- 输出形态：图文 or 视频？（默认图文）
- 素材：有无现成文字/图片/视频？
- 风格：视觉风格走 card-design 风格库（9 种：瑞士极简/杂志编辑/新中式墨韵/奶油温柔/多巴胺 Y2K/高奢黑金/手账贴纸/极客终端/植物清新）——在 Step 5A 出卡时由 card-xiaohongshu 引导选定，这里先不锁死。
- 卡片数量 / 视频时长：图文默认 5-7 张；视频默认 30-60 秒
Step 0.5 — 卖点/亮点分析（推广/分享/测评类必做）
- 列出所有功能/特性/亮点
- 让用户对每个点打分：
- 稀缺性（1-5）：别人有吗？
- 实用性（1-5）：解决多大问题？
- 可感知（1-5）：用户能直接看到吗？
- 得分 = 稀缺性 × 实用性 × 可感知
- 选 Top 1-2 作为核心卖点
Step 1 — 素材清点（有素材才做）
如果用户提供了素材，先用脚本生成清单：
python3 skills/openclaw/xhs-note-creator/scripts/analyze_material.py <path>... \
--out <work-dir>/reference/materials.json \
--frames-dir <work-dir>/reference/frames
素材价值排序：对比图 > 功能演示 > 数据图表 > 品牌素材
Step 2 — 采集外部参考（观点类/资讯类必做）
按 references/reference-search.md 执行，核心数据 ≥2 个来源交叉验证。
Step 3 — 写长文原稿（2000-4000 字）
从 H2 开始（不写 H1），写完先给用户确认再继续。
Step 4 — 去 AI 化（强制）
去 AI 感规则统一走 text-polisher 权威源，不在本 SKILL 维护副本：
- 中文规则（含小红书素人感/闺蜜语气/emoji 节奏特化）→
../text-polisher/references/zh-ai-markers.md - 通用填充短语 →
../text-polisher/references/phrases-to-remove.md - 公式化结构 →
../text-polisher/references/structures-to-avoid.md
按其五层原则完整扫描重写，质量评分满分 50。
Step 5 — 分发：图文 or 视频
5A. 图文帖：拆成 3-9 张卡片
- cover（第 1 张）+ content（中间）+ ending（最后 1 张）
- 每张 ≤80 字
- 一张卡只讲一个论点
- 全套配色/字体/风格保持一致（由 card-design 选中的那一套贯穿整组）
What ships with it
13 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 554 B
- references/image-sourcing.md 3.8 KB
- references/material-intake.md 5.2 KB
- references/meta-schema.md 4.6 KB
- references/output-spec.md 2.3 KB
- references/reference-search.md 2.9 KB
- references/xiaohongshu-viral-methodology.md 7.3 KB
- scripts/analyze_material.py 4.9 KB runs code
- scripts/collage_3x4.py 3.4 KB runs code
- scripts/crop_watermark.py 1.6 KB runs code
- scripts/normalize_slug.py 2.1 KB runs code
- scripts/text_on_image.py 5.0 KB runs code
- scripts/validate_meta.py 5.8 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.
- 6d ago First seen · 172 lines · 125 tokens per session scan A 1372ff820d27
xhs-note-creator is a skill published in the GitHub repository ZJU-REAL/Easel (494 stars, last pushed 2d ago), licensed Apache-2.0. It adds 125 tokens to every session and 2,495 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 2026-09-03.
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