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/InternScience/ChemClawnpx agentmods add skills/internscience/chemclaw/literature-parsingWrote 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/internscience/chemclaw/literature-parsing)<a href="https://agentmods.dev/skills/internscience/chemclaw/literature-parsing"><img src="https://agentmods.dev/badge/skills/internscience/chemclaw/literature-parsing/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/internscience/chemclaw/literature-parsing"><img src="https://agentmods.dev/badge/skills/internscience/chemclaw/literature-parsing.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 | $0.02767 |
| Opus 5 | $0.00019 | $0.01384 |
| Sonnet 5 | $0.00008 | $0.00553 |
| Haiku 4.5 | $0.00004 | $0.00277 |
Grade A, and why
literature-parsing 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.
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 — 348 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Literature Parsing Skill
将 PDF 文献完整转换为 Markdown 文件，并自动提取所有图表和图片。使用 MinerU (opendatalab) 进行工业级高质量解析。
触发条件
- 用户提供 PDF 文件并要求转换为 Markdown
- 提到"PDF 转 Markdown"、"文献解析"
- 说"提取 PDF 中的图片"、"extract figures from PDF"
- 需要批量处理多个 PDF 文件
功能特性
- ✅ PDF → Markdown 转换 - 保留标题、段落、列表、公式等结构
- ✅ 智能空格处理 - 自动修复中英文之间的空格
- ✅ 图表提取 - 提取 PDF 中嵌入的真实图片
- ✅ 表格识别 - 检测并转换表格为 Markdown 格式
- ✅ 公式保留 - LaTeX 格式数学公式
- ✅ 元数据提取 - 提取标题、作者等信息
- ✅ 批量处理 - 支持同时处理多个 PDF
- ✅ 结构化输出 - 生成组织良好的 Markdown 文档
核心技术
| 组件 | 用途 |
|---|---|
| MinerU | 工业级 PDF 解析引擎 (opendatalab) |
| Pipeline Backend | 文本提取、布局分析 |
| OCR | 扫描版 PDF 文字识别 |
输出结构
转换后的目录结构：
output_folder/
├── document.md # 主 Markdown 文件
├── images/ # 提取的图片
│ ├── xxx.jpg
│ ├── yyy.jpg
│ └── ...
└── document_metadata.json # 文档元数据
使用方法
对话框中使用
将这个 PDF 转换为 Markdown
解析这篇文献，提取所有图片
PDF to Markdown with figures
批量转换这些 PDF 文件
命令行使用
# 基本转换（自动模式）
python3 scripts/literature_parsing.py -i paper.pdf -o ./output
# 文本 PDF（速度快）
python3 scripts/literature_parsing.py -i paper.pdf -o ./output -m txt
# 扫描版 PDF（使用 OCR）
python3 scripts/literature_parsing.py -i paper.pdf -o ./output -m ocr
# 指定语言（提高 OCR 准确率）
python3 scripts/literature_parsing.py -i paper.pdf -o ./output -l en
# 使用 GPU 加速
python3 scripts/literature_parsing.py -i paper.pdf -o ./output -d cuda
直接使用 MinerU
# 基本用法
mineru -p paper.pdf -o ./output
# 文本模式（无图片，速度快）
mineru -p paper.pdf -o ./output -m txt
# OCR 模式（扫描版）
mineru -p paper.pdf -o ./output -m ocr
# 指定语言
mineru -p paper.pdf -o ./output -l ch
# 指定设备
mineru -p paper.pdf -o ./output --device cpu
参数说明
| 参数 | 简写 | 说明 | 默认值 |
|---|---|---|---|
--input |
-i |
输入 PDF 文件路径 | - |
--output |
-o |
输出目录 | ~/.openclaw/media/literature-parsing |
--method |
-m |
解析方法：auto/txt/ocr | auto |
--lang |
-l |
OCR 语言：ch/en 等 | ch |
--device |
-d |
设备：cpu/cuda | cpu |
--quiet |
-q |
安静模式（输出 JSON） | false |
What ships with it
3 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.
- 12d ago First seen · 348 lines · 38 tokens per session scan A 5ce6502193f0
literature-parsing is a skill published in the GitHub repository InternScience/ChemClaw (52 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 2,767 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 2026-08-30.
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