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/Azzygoatcoder/agent-useful-skillsnpx agentmods add skills/azzygoatcoder/agent-useful-skills/office-toolsWrote 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/azzygoatcoder/agent-useful-skills/office-tools)<a href="https://agentmods.dev/skills/azzygoatcoder/agent-useful-skills/office-tools"><img src="https://agentmods.dev/badge/skills/azzygoatcoder/agent-useful-skills/office-tools/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/azzygoatcoder/agent-useful-skills/office-tools"><img src="https://agentmods.dev/badge/skills/azzygoatcoder/agent-useful-skills/office-tools.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 | $0.00118 | $0.02431 |
| Opus 5.5 | $0.00047 | $0.00972 |
| Sonnet 5.5 | $0.00024 | $0.00486 |
| Haiku 4.5 | $0.00012 | $0.00243 |
Grade A, and why
office-tools scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Office Tools — Office/PDF 文件处理
科研骨架的 Office 模块。Excel 数据处理 + markdown↔Office 转换 + PDF/Office 提图给 vision 读。
场景判定
| 场景 | 触发 | 命令 |
|---|---|---|
| Excel 数据处理 | 实验数据/表格分析 | read / stats |
| markdown 表 → Excel | 阅读报告/防撞车矩阵转 xlsx 分析 | md2xlsx |
| Excel → markdown | xlsx 回写笔记 | xlsx2md |
| CSV → Excel | 实验数据导入 | csv2xlsx |
| PDF/Office 提图 | 论文配图、报告插图 | source 优先（arXiv 源码包作者原图）→ extract 回退 → vision.py |
| markdown → Word | 课程作业/报告转 docx | md2docx（pandoc） |
| markdown → PPT | 课程汇报/讲稿转 pptx | md2pptx（pandoc） |
写作：markdown → Word/PPT（pandoc）
office-tools md2docx 笔记.md 报告.docx [--toc] [--reference-doc bin/templates/reference.docx]
office-tools md2pptx 讲稿.md 汇报.pptx [--slide-level 2] [--reference-doc 模板.pptx]
- LLM 写 markdown（天然输出）→ pandoc 转 Office，薄封装在 office_tools（pandoc 全部能力自然继承：公式/引用/双栏/备注）
- LaTeX 公式($..$) → Word/PPT 原生 OMML 方程（可编辑，实测 docx+pptx 都出）；这是选 pandoc 而非其他路线的关键
- 中文字体：docx 用
--reference-doc bin/templates/reference.docx（已预设宋体正文/黑体标题/1.5倍距/首行缩进2字符） - pptx 结构：
#=分节标题页、##=一页，输出原生文本框（可编辑，非图片）；公式同样 OMML - 重新生成默认模板：
pandoc -o reference.docx --print-default-data-file reference.docx→style-reference-docx；模板可在 WPS/Word 手动微调后保存即生效
关键能力：看图
宿主模型自带原生视觉（DeepSeek V4.1+）——直接读图即可，不需要代理：
office_tools extract pdf 论文.pdf --outdir 图/
→ read_image 图/p7_img957.png # 原生读图（首选）
→ vision.py 图/p7_img957.png "这是什么图？" # 跨模型第二意见（可选，见下）
- PDF：默认提取嵌入图；
--pages 1,3渲染整页为 PNG - docx/pptx：提取内嵌图片
- 默认直接用原生读图：更快、无外部依赖、能同时看多张
vision.py现在的定位是「跨模型第二意见」（Qwen3-VL-32B，SiliconFlow），不是"代眼"： 当需要与作者模型不同的独立判断时才用（例如判"这张图值不值得留"、"渲染有没有错"）。 同模型自评会继承同一套盲点，独立模型才有信息量——这正是本仓库"验证环"的要点。 批量分类场景仍推荐classify（两阶段过滤后只对幸存图调用，省 token）
拿论文原图：源码包优先（存在 LaTeX 源码版就直接拿）
多数 arXiv 论文有 LaTeX 源码包——需要论文原图时首选它，作者亲手画的原图无碎片/无渲染损耗、矢量保持矢量：
python papers/arxiv_fetch.py source <id> --outdir 笔记/assets/
# e-print 下载 → 解压 → \includegraphics 反查作者原图 → PDF 转 PNG
extract 只在无源码包时作回退（区域渲染；extract_image 原始字节会拿复合图碎片，已弃用）。
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 Changed · +7 lines · +49 tokens per session 729b8dd051dd
- 1mo ago First seen · 105 lines · 69 tokens per session scan A 1d949cbbe7d4
office-tools is a skill published in the GitHub repository Azzygoatcoder/agent-useful-skills (9 stars, last pushed 1mo ago), licensed MIT. It adds 118 tokens to every session and 2,431 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 2026-08-31.
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