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 malue-ai/dazee-small --skill content-reformattergit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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/malue-ai/dazee-small/content-reformatter)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/content-reformatter"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/content-reformatter/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/malue-ai/dazee-small/content-reformatter"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/content-reformatter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00038 | $0.00774 |
| Opus 5 | $0.00019 | $0.00387 |
| Sonnet 5 | $0.00008 | $0.00155 |
| Haiku 4.5 | $0.00004 | $0.00077 |
Grade A, and why
content-reformatter 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 9d 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.
What it actually says
多平台格式转换
将同一篇内容适配到不同发布平台，自动调整长度、语气、格式和配图要求。
使用场景
- 用户说「把这篇文章改成小红书版本」「帮我发到朋友圈的版本」
- 用户写了一篇长文，需要同时发到多个平台
- 用户需要把邮件内容转为公众号文章
支持的平台
微信公众号
特点：
- 标题：≤ 64 字，吸引点击
- 正文：800-3000 字，段落短（3-5 行）
- 格式：加粗标题、引用框、分隔线
- 语气：半正式，有温度
- 配图：建议每 300 字配 1 张图
- 封面：建议 2.35:1 横图
小红书
特点：
- 标题：≤ 20 字，带 emoji
- 正文：300-800 字，口语化
- 格式：分点列表，emoji 标记
- 语气：亲切活泼，像朋友聊天
- 标签：5-10 个 #话题标签
- 配图：正方形或 3:4 竖图
知乎
特点：
- 标题：提问式或观点式
- 正文：1000-5000 字，逻辑清晰
- 格式：大标题 + 小标题 + 引用 + 代码块
- 语气：理性专业，有论据
- 引用：标注信息来源
微博 / Twitter
特点：
- 长度：≤ 140 字（微博 ≤ 2000 字但建议短）
- 格式：纯文本 + 话题标签
- 语气：简洁有力，一句话说完核心观点
- 话题：1-3 个 #话题#
邮件
特点：
- 主题行：清晰说明目的
- 正文：简洁，重点在前 3 行
- 格式：称谓 + 正文 + 落款
- 语气：正式/半正式
- 附件：提醒用户附件内容
朋友圈
特点：
- 长度：≤ 6 行（超过会折叠）
- 格式：纯文本，可用 emoji
- 语气：自然随意
- 配图：1/3/6/9 张
执行方式
直接使用 LLM 能力进行格式转换，无需外部工具。
输出格式
## 微信公众号版本
**标题**：XXXXX
**正文**：
（格式化后的内容）
---
## 小红书版本
**标题**：XXXXX
**正文**：
（格式化后的内容）
**标签**：#话题1 #话题2 ...
输出规范
- 默认转换为用户指定的单一平台
- 用户说「全平台」时，一次生成 3+ 个版本
- 每个版本标注字数和关键差异
- 保留原文核心信息，不编造新内容
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.
- 9d ago First seen · 122 lines · 38 tokens per session scan A e8c4f1123ec9
content-reformatter is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 774 once invoked, about $0.0002 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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