laohan-notebooklm

laohan-notebooklm is a skill for Claude Code, Codex from hanzhcn/laohan-skills. It costs 73 tokens per session (916 once invoked), scanned A, original, MIT.

A workflow that turns a Chinese spoken script into slide images through NotebookLM, Google’s AI notebook service, and saves them for use in video editing. It also converts the resulting PDF into images.

In plain words
What is it for?
Making rough presentation slides from a script, exporting each slide as an image, and preparing those images for use in 剪映 (Jianying).
Why use it?
It removes the manual work of creating draft slides and preparing them as visual backgrounds for a video editor. It is intended for Chinese-language output and depends on access to NotebookLM.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Making rough presentation slides from a script, exporting each slide as an image, and preparing those images for use in 剪映 (Jianying).

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hanzhcn/laohan-skills/laohan-notebooklm
Install

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.

Any agent
npx skills add hanzhcn/laohan-skills --skill laohan-notebooklm
Clone the repo
git clone --depth 1 https://github.com/hanzhcn/laohan-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for laohan-notebooklm

README.md
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Your own site
<a href="https://agentmods.dev/skills/hanzhcn/laohan-skills/laohan-notebooklm"><img src="https://agentmods.dev/badge/skills/hanzhcn/laohan-skills/laohan-notebooklm/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.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/hanzhcn/laohan-skills/laohan-notebooklm"><img src="https://agentmods.dev/badge/skills/hanzhcn/laohan-skills/laohan-notebooklm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 73 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 916 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
SkillSpector: 1 finding, up to low

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • low MCP Rug Pull · line 109
    pip install without ==version installs the latest release, which could include malicious changes.
    Fix: Pin the version: pip install package==1.2.3
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00073 $0.00916
Opus 5 $0.00036 $0.00458
Sonnet 5 $0.00015 $0.00183
Haiku 4.5 $0.00007 $0.00092

Measured 10d ago against content hash 1e00ea8f22f2, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

laohan-notebooklm 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 10d 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.

laohan-notebooklm/SKILL.md · 111 lines

What it actually says

口播稿幻灯片生成

把口播稿一键生成幻灯片图片,输出到 script.md 同级目录的 slides/ 子目录,剪映直接用。

核心理念

不加提示词、不加布局要求——NotebookLM 自由发挥效果最好。输出是半成品图片,布局和排版在剪映里调整,不在这里浪费时间。

不适用场景

  • 想要精确控制每页内容和排版 → NotebookLM 不支持,用 python-pptx 手动生成 PPT
  • 生成英文幻灯片 → 改 --language en-US,但本 skill 默认中文场景
  • 没有 nlm CLI 或网络无法访问 NotebookLM → 先装依赖、解决网络问题

使用

/laohan-notebooklm <script.md路径>

流程

每步可能 SSL 超时,加重试(最多3次,间隔3秒)。

0. 准备

  • 从 script.md 路径确定输出目录:script.md 所在目录下的 slides/ 子目录
  • SCRIPT_DIR=$(dirname "$SCRIPT")OUTPUT="$SCRIPT_DIR/slides"
  • mkdir -p "$OUTPUT"
  • cp "$SCRIPT" "$OUTPUT/script.md"
  • 确认网络可访问 NotebookLM(REGION_NOT_SUPPORTED 时需切换网络环境)

1. 创建笔记本

nlm notebook create "<标题>PPT"

记录返回的 NOTEBOOK_ID。

2. 上传素材

nlm source add NOTEBOOK_ID --file "$OUTPUT/script.md"

只上传口播稿原文,不加任何提示词或引导注释。

3. 触发生成

echo "y" | nlm slides create NOTEBOOK_ID --language zh-CN --length default

--language zh-CN 是关键,漏了生成英文版。

4. 轮询+下载+转图片(后台脚本)

必须用一个后台脚本一次性完成轮询、下载、转图片。用 Bash 工具的 run_in_background: true 执行:

NB="NOTEBOOK_ID"
TITLE="<标题>"
OUTPUT="<script.md所在目录>/slides"

for i in $(seq 1 20); do
  st=$(nlm studio status "$NB" 2>&1 | grep -o '"status": "[^"]*"' | head -1)
  echo "[$i] $st"
  echo "$st" | grep -q "completed" && break
  sleep 30
done

for i in 1 2 3; do
  nlm download slide-deck "$NB" -o "${OUTPUT}/${TITLE}.pdf" 2>&1 && break
  sleep 5
done

pdftoppm -png -r 200 "${OUTPUT}/${TITLE}.pdf" "${OUTPUT}/slide"
echo "Done: $(ls "${OUTPUT}"/slide-*.png | wc -l | tr -d ' ') slides"

后台完成后读取输出确认图片数量,告知用户。

输出

<script.md所在目录>/slides/
├── script.md
├── <标题>.pdf
├── slide-01.png
├── slide-02.png
└── ...

关键原则

  • 不加提示词:只上传口播稿原文,NotebookLM 自由发挥效果最好
  • 不加布局要求:布局在剪映里调整
  • 输出到 script.md 同级的 slides/ 目录,与其他素材统一管理

前置依赖

  • nlm CLI v0.5.25(pip install notebooklm-mcp-cli
  • poppler(brew install poppler
Changes

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.

  1. 10d ago First seen · 111 lines · 73 tokens per session scan A 1e00ea8f22f2

Subscribe to this mod's changes

laohan-notebooklm is a skill published in the GitHub repository hanzhcn/laohan-skills (11 stars, last pushed 14d ago), licensed MIT. It adds 73 tokens to every session and 916 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 2026-08-30.

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