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/chachamaru127/codex-harness/notebooklmnpx skills add Chachamaru127/codex-harness --skill notebooklmgit clone --depth 1 https://github.com/Chachamaru127/codex-harnessWrote 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/chachamaru127/codex-harness/notebooklm)<a href="https://agentmods.dev/skills/chachamaru127/codex-harness/notebooklm"><img src="https://agentmods.dev/badge/skills/chachamaru127/codex-harness/notebooklm.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 | $0.00059 | $0.01091 |
| Opus 5 | $0.00030 | $0.00545 |
| Sonnet 5 | $0.00012 | $0.00218 |
| Haiku 4.5 | $0.00006 | $0.00109 |
Grade A, and why
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 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
NotebookLM Skill
ドキュメント生成を担当するスキル群です。
機能詳細
| 機能 | 詳細 |
|---|---|
| NotebookLM YAML | See references/notebooklm-yaml.md |
| スライド YAML | See references/notebooklm-slides.md |
実行手順
- ユーザーのリクエストを分類
- 上記の「機能詳細」から適切な参照ファイルを読む
- その内容に従って生成
🔧 PDF ページ範囲読み取り（Claude Code 2.1.38+）
大型 PDF を効率的に扱うための機能です。
ページ範囲指定で読み取り
// ページ範囲指定で読み取り
Read({ file_path: "docs/spec.pdf", pages: "1-10" })
// 目次だけ確認
Read({ file_path: "docs/manual.pdf", pages: "1-3" })
// 特定のセクションのみ
Read({ file_path: "docs/api-reference.pdf", pages: "25-45" })
ユースケース別の推奨アプローチ
| ケース | 推奨読み取り方法 | 理由 |
|---|---|---|
| 100ページ超のPDF | 目次(1-3) → 関連章のみ | トークン消費を最小化 |
| 仕様書レビュー | セクション単位で範囲指定 | 必要な部分のみ精読 |
| APIドキュメント | エンドポイント一覧(目次)から開始 | 全体構造を把握してから詳細へ |
| 学術論文 | Abstract + 結論 → 本文 | 要点を先に把握 |
| 技術マニュアル | 目次 + トラブルシューティング章 | 実用的な部分を優先 |
NotebookLM YAML 生成時の活用例
大型PDF（300ページの技術仕様書）からYAMLを生成する場合：
1. **目次を読む**（1-5ページ）
Read({ file_path: "spec.pdf", pages: "1-5" })
→ 章立てを把握
2. **各章の冒頭を読む**（各章の最初の2ページ）
Read({ file_path: "spec.pdf", pages: "10-11" }) // 第1章
Read({ file_path: "spec.pdf", pages: "45-46" }) // 第2章
→ 各章の概要を把握
3. **重要セクションを精読**
Read({ file_path: "spec.pdf", pages: "78-95" }) // APIリファレンス
→ 詳細な内容を抽出
この方法で、300ページすべてを読むことなく効率的にYAMLを生成できます。
ベストプラクティス
| 原則 | 説明 |
|---|---|
| 段階的読み込み | 目次 → 概要 → 詳細の順に読む |
| 関連ページのみ | タスクに必要なページだけ指定 |
| トークン節約 | 全ページ読み込みは最終手段 |
| 構造理解優先 | 目次で全体像を把握してから詳細へ |
従来の方法との比較
| 方法 | トークン消費 | 処理時間 | 精度 |
|---|---|---|---|
| 全ページ読み込み（300ページ） | ~150,000 | 長い | 高 |
| ページ範囲指定（必要な30ページ） | ~15,000 | 短い | 高 |
→ 90%のトークン削減と処理時間短縮が可能
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.
- 4d ago First seen · 94 lines · 59 tokens per session scan A 53646af1fcd6
notebookLM is a skill published in the GitHub repository Chachamaru127/codex-harness (2 stars, last pushed 6mo ago), licensed MIT. It adds 59 tokens to every session and 1,091 once invoked, about $0.0003 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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