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 agentmods add skills/mrsgsa/math-modeling-skill-dify/pdf-batchnpx skills add MrSGSA/math-modeling-skill-dify --skill pdf-batchgit clone --depth 1 https://github.com/MrSGSA/math-modeling-skill-difyWrote 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/mrsgsa/math-modeling-skill-dify/pdf-batch)<a href="https://agentmods.dev/skills/mrsgsa/math-modeling-skill-dify/pdf-batch"><img src="https://agentmods.dev/badge/skills/mrsgsa/math-modeling-skill-dify/pdf-batch.svg" alt="Measured on agentmods" 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 | $0.00082 | $0.00740 |
| Opus 5 | $0.00041 | $0.00370 |
| Sonnet 5 | $0.00016 | $0.00148 |
| Haiku 4.5 | $0.00008 | $0.00074 |
Grade A, and why
pdf-batch 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 4d 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
文档批量转 Markdown
调用 scripts/batch_convert.py 和 MinerU。保持输入文档只读，将结果写入 PROJECT_ROOT。旧式 .doc 先转换为 .docx。
输入目录、Markdown 输出目录和多模态包输出目录必须彼此分离，既不能相同也不能互相嵌套；否则递归发现可能把上次产物再次当作输入，工具会拒绝运行。
环境与运行
MinerU 在 Windows 上要求 Python 3.10 至 3.12。本机默认 Python 可以用于启动批处理，但 MinerU 应使用 uv 建立的独立 Python 3.12 工具环境：
uv tool install --python 3.12 "mineru[all]"
安装体积较大，未经用户同意不要自动安装或下载模型。在 PROJECT_ROOT 中运行：
python <SKILL_ROOT>\tools\pdf-batch\scripts\batch_convert.py --input knowledge_inbox\pdf --output knowledge_ready
默认使用支持纯 CPU 的 pipeline 后端。可用 --backend auto 切换默认后端，--dry-run 只检查计划，--force 强制重新转换。
推荐单次高精度转换同时生成文本库与图片库成品：
python <SKILL_ROOT>\tools\pdf-batch\scripts\batch_convert.py --input <文档目录> --output <多模态包目录> --markdown-output <Markdown目录> --backend auto --effort high --image-analysis --dify-multimodal
此模式对每份文档只调用一次 MinerU，同时发布：
- 纯 Markdown：保留公式和图表分析文字、移除无法上传的本地图片链接，用于现有文本知识库。
.dify-mm.zip：包含manifest.json、document.md和唯一命名的原图，用于配套 Dify 插件和统一多模态图片库。该包只索引含图片的块，正文仍由文本库负责。
只有两份成品都存在且不早于源文档时才跳过。中间 JSON、布局结果和临时目录在单篇完成后自动清理。
只需要一个 Markdown 时仍可使用 --md-only。
输出与检查
- 图片名包含源文档哈希、图序号和图片哈希；插件再按清单路径与 SHA-256 校验，避免跨论文串图。
- 同名且同扩展名的源文档会在输出名中加入内容哈希，避免覆盖。
- batch_report.json 记录源文件 SHA-256、命令、状态、耗时和输出。
- dify_upload_list.txt 列出成功生成的 Markdown，供用户手动上传。
- conversion.log 保存每篇文档的 MinerU 输出。
重复运行时跳过未变化且两份成品均已存在的文件。文本 Markdown 上传普通知识库；.dify-mm.zip 只上传统一多模态图片库。
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.
- 4d ago First seen · 46 lines · 82 tokens per session scan A e923c87ec376
pdf-batch is a skill published in the GitHub repository MrSGSA/math-modeling-skill-dify (4 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 740 once invoked, about $0.0004 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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