OpenByline: Skill for Claude Code

.claude/skills/article-to-xhs-cards/SKILL.md

article-to-xhs-cards is a skill for Claude Code from bailutingyu/OpenByline. It costs 124 tokens per session (3,524 once invoked), scanned A, original, MIT.

A workflow guide for turning a finished article into six to nine vertical image cards for Xiaohongshu, a Chinese social platform also known as Little Red Book, or Xiaolvshu.

In plain words
What is it for?
Use it to plan card-by-card content, choose among three visual styles, create 3:4 cards, or render text-focused cards in HTML when exact wording matters.
Why use it?
It provides a structured way to split long writing into visual posts while keeping the chosen style, layout, and wording consistent.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: reads .claude/ paths; mentions CLAUDE.md; names the AskUserQuestion tool.

This is bailutingyu/OpenByline's own configuration. It tells Claude Code how to work on OpenByline itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything OpenByline configures →

Reuse

Borrowing it

Nothing to install: this file belongs to bailutingyu/OpenByline. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/bailutingyu/OpenByline/main/.claude/skills/article-to-xhs-cards/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/bailutingyu/OpenByline

Made for: Claude Code.

Wrote 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.

agentmods badge for article-to-xhs-cards

README.md
[![agentmods](https://agentmods.dev/badge/skills/bailutingyu/openbyline/article-to-xhs-cards/github.svg)](https://agentmods.dev/skills/bailutingyu/openbyline/article-to-xhs-cards)
Your own site
<a href="https://agentmods.dev/skills/bailutingyu/openbyline/article-to-xhs-cards"><img src="https://agentmods.dev/badge/skills/bailutingyu/openbyline/article-to-xhs-cards/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.

agentmods 80×15 button for article-to-xhs-cards

Your own site · 80×15
<a href="https://agentmods.dev/skills/bailutingyu/openbyline/article-to-xhs-cards"><img src="https://agentmods.dev/badge/skills/bailutingyu/openbyline/article-to-xhs-cards.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,524 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00124 $0.03524
Opus 5 $0.00062 $0.01762
Sonnet 5 $0.00025 $0.00705
Haiku 4.5 $0.00012 $0.00352

Measured 12d ago against content hash 2bfbcdd8ad8d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

article-to-xhs-cards 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 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (render_cards.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.claude/skills/article-to-xhs-cards/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

文章转小红书 / 小绿书图文卡片(风格选择器版)

把一篇文章变成一叠竖图发小红书 / 小绿书。三种画风、3:4 竖版、你自己的 IP 形象。

信条:一图一个认知锚点主角承担画面核心动作,不是站旁边装饰为这篇内容重新发明一个怪诞但成立的物理隐喻,不套模板、不做 PPT。

〇、启动先选风格(每次必做 ← 本技能的入口动作)

不要默认画风。 进入任务先把下面 3 种风格的"一句话气质 + 适用场景"列给用户,让用户挑一种(或指定某几张用 A、某几张用 C)。挑定后再进流程,并加载对应 styles/ 文件。

风格 气质 最适合 细则
A 暖彩动漫 暖·有人味·故事感 封面首图、情绪共鸣、产品故事 styles/warm-anime.md
B 留白混血 高级·克制·留白(含彩色档 / 黑剪影档) 职场共鸣、观点隐喻、正文配图 styles/whitespace.md
C 彩蛋长卷人生线 一条线串真实物件节点(竖版蜿蜒) 个人经历、项目复盘、产品演化、成长路径 styles/long-scroll.md

选 B 时再追问一句用彩色档还是黑剪影档(剪影档≈高级冷感,详见该 style 文件)。 不想烧生图额度 / 要文字 100% 精确 → 走文末模式 B(HTML 文字卡)

一、规格(固定)

  • 3:4 竖版 1080×1440 px(A / B 出图、模式 B 渲染都按这个)。长卷 C 单图 3:4,节点多时出竖版长图 1080×N。
  • ⚠️ 3:4 是硬要求每次委派 Codex 生图,prompt 里必须反复强调"3:4 竖版 / 1080×1440"(订阅版 image_gen 不强调易给方图);别要更高分辨率(易失败)。
  • 第 1 张 = 封面(小红书首图)。

二、方法论底座(三风格共用,按需读取)

  • references/scene-patterns.md:6 类场景(拉扯/涌出/审查/重命名/筛选/加码)+ 原创隐喻三步 + 五步提炼 + 短标签规范 + shot-list 格式。
  • references/master-lock.md:母版锁定——先锁锚点、抽不变量、写 ≥3 变异点;防元素清单化 / 母版复刻化;出图拦截规则。
  • references/fact-anchoring.md:事实锚定——个人经历/品牌/数字只用作者给的,接本项目"私货清单",反幻觉。
  • references/qa-gates.md:两级质量门(CRITICAL/STANDARD)+ 逐张 QA + 落盘核验 + 生图稳定性经验。
  • references/ip-character.md:IP 形象行为 DNA——形变弹性 / 禁止项 / 合格 vs 不合格动作(三风格共用)。
  • references/prompt-scaffold.md:标准模式生图 prompt 骨架 + 负面约束块 + 三段局部编辑 prompt(长卷骨架在 styles/long-scroll.md §六)。
  • assets/masters/:作者自建视觉母版库(出图质量标尺)。首跑通常为空——正常,直接用 6 类场景抽象锚点,出过合格图再回填。

三、数量与切分

  • 6-9 张:封面 1 + 内容若干 + 收尾 1。按"认知锚点"切,不要每段都配图。
  • 认知锚点 = 核心判断 / 起因痛点 / 关键产品动作 / 前后对比 / 核心金句隐喻 / 边界判断 / 行动建议。
  • 长卷 C 例外:整组就是一条人生线(5-8 节点),按 styles/long-scroll.md 切节点。

四、标准流程

  1. 选风格(见〇)。加载对应 styles/*.md + 方法论底座。
  2. 读料:Read workspace/内容输出/<主题slug>/final.md(标题/金句/正文;若该主题只有 final-formatted.md / final-candidate.md 则读现有定稿版)+ 根目录 workspace/voice-profile.md(文案守【绝对不要】、不"算账"字眼)。确认参考图 workspace/个人知识库/个人IP图/个人IP动漫图.png 在。用户没给主题 slug 就先问。
  3. 事实锚定:按 references/fact-anchoring.md 过一遍——涉及亲历/品牌/数字的,缺口走私货清单或降级,别编。
  4. 写 shot-list(落 workspace/内容输出/<主题slug>/xiaohongshu-v2/shot-list.md):每张先填母版锁定六字段master-lock.md §三),再填用途/主题/共鸣点/物理动作/主物件/主角动作/短标签(scene-patterns.md §六 格式);出图 prompt 按 references/prompt-scaffold.md(标准模式)或 styles/long-scroll.md §六(长卷)。 4.5 shot-list 交主编确认(最便宜的纠偏点):把切分 + 每张隐喻 / 物理动作 / 短标签交人类主编过一遍,认可方向后再出样张——对齐 shot-list 先行与本项目"初稿要人类确认"信条。
  5. 先出 2-3 张样张(封面 + 最核心隐喻 + 一张产品/对比页)委派 Agentsubagent_type: codex:codex-rescue),规范见〇五。
  6. 给人类主编验收样张(形象像不像、隐喻成不成立、中文有无错字、是否 3:4、风格对不对)。认可后再批量出剩余。
  7. QA:逐张过 references/qa-gates.md,不合格局部编辑或重生成。
  8. 配文产出caption.md(每张配的小红书正文段落)+ titles.md(小红书标题候选,可调 headline-craft skill)。供发布时贴正文与标题。
  9. 生图 prompt 先存后生(🔴 作者硬规矩,2026-06-11;时机在步骤 5 出样张之前)调用生图引擎之前,先把每张完整生图 prompt 写进 xiaohongshu-v2/shot-list.md 末尾「生图 prompt 存档」区块落盘(公共的通用前缀/后缀/负面块提取一次,每张只列差异化的画面+标题+批注),落完才允许出图;执行中临场改了 prompt,出图后把实际版本同步回档。这样换引擎/失败重试零成本、出错字直接改那条重出,prompt 绝不允许只活在 Bash 历史或委派指令里。

Read the full file on GitHub · 109 lines

Changes

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.

  1. 12d ago First seen · 109 lines · 124 tokens per session scan A 2bfbcdd8ad8d

Subscribe to this mod's changes

article-to-xhs-cards is a skill published in the GitHub repository bailutingyu/OpenByline (2 stars, last pushed 2mo ago), licensed MIT. It adds 124 tokens to every session and 3,524 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 2026-08-31.

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