← 返回 Skills 市场
157
总下载
1
收藏
0
当前安装
2
版本数
在 OpenClaw 中安装
/install notebooklm-pdf-cleaner
功能描述
Create a presentation-ready copy of a NotebookLM-exported slide-deck PDF by masking the small visible NotebookLM footer badge at the bottom-right of each pag...
安全使用建议
This skill appears to do exactly what it claims: overlay a white rectangle over the bottom-right badge and write a new '*-clean.pdf' without exfiltrating data. Things to consider before installing or running: 1) Dependencies: the script requires pypdf and reportlab but the skill metadata doesn't declare them — install via a trusted source (e.g., pip install pypdf reportlab) or run in a virtualenv. 2) Backup: always run it on a copy of your PDF (the script defaults to writing a new file, but double-check). 3) Flags: --strip-metadata and --strip-annots will remove provenance/comments; use them only if you intend to remove that information. 4) Visual correctness: the script overlays a white rectangle — on non-white slide backgrounds the mask may be visible; test and adjust --mask-* values as needed. 5) No network: the code contains no network calls or credential usage. 6) Review code: if you have concerns, inspect the included Python file before running to confirm there are no modifications. Overall the skill is coherent and narrow in scope; installing/running it in a contained environment with the declared Python packages is a reasonable next step.
能力评估
Purpose & Capability
The script implements exactly what the description says (creates a masked '*-clean.pdf' copy by overlaying a white rectangle). However, the package metadata/requirements do not declare the Python dependencies used in the script (pypdf and reportlab). No unrelated credentials, binaries, or system paths are requested.
Instruction Scope
SKILL.md confines the work to NotebookLM slide-deck PDFs and instructs running the included script. The script enforces PDF-only input, refuses to overwrite the source file, and only strips metadata/annotations when explicit flags are used. It does not read unrelated files or environment variables, nor does it send data to external endpoints.
Install Mechanism
There is no install spec (instruction-only), which is low risk. But the script depends on third-party Python libraries (pypdf, reportlab) that are not declared in the skill metadata; the environment must already have them or the user must install them via pip. No downloads or archive extraction are performed by the skill itself.
Credentials
The skill requests no environment variables, credentials, or config paths. This is proportionate for its stated purpose.
Persistence & Privilege
The skill is not always-enabled and does not modify system or other-skill configuration. It runs as an on-demand script and writes only the specified output file.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install notebooklm-pdf-cleaner - 安装完成后，直接呼叫该 Skill 的名称或使用
/notebooklm-pdf-cleaner触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.0.1
Narrowed scope to NotebookLM slide-deck footer-badge masking, replaced machine-specific paths with portable skill paths, and added safer defaults: no metadata/annotation stripping unless explicitly requested, no source overwrite, and no output overwrite without --force.
v1.0.0
Initial release: clean NotebookLM-exported slide deck PDFs by masking the visible bottom-right footer mark, stripping lightweight PDF extras, and writing a cleaned copy without touching the original.
元数据
常见问题
NotebookLM PDF Cleaner 是什么？
Create a presentation-ready copy of a NotebookLM-exported slide-deck PDF by masking the small visible NotebookLM footer badge at the bottom-right of each pag... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 157 次。
如何安装 NotebookLM PDF Cleaner？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install notebooklm-pdf-cleaner」即可一键安装，无需额外配置。
NotebookLM PDF Cleaner 是免费的吗？
是的，NotebookLM PDF Cleaner 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
NotebookLM PDF Cleaner 支持哪些平台？
NotebookLM PDF Cleaner 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 NotebookLM PDF Cleaner？
由 fudanjx（@fudanjx）开发并维护，当前版本 v1.0.1。
推荐 Skills