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.
git clone --depth 1 https://github.com/ArieGoldkin/claude-forgeWrote 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/commands/ariegoldkin/claude-forge/notebooklm)<a href="https://agentmods.dev/commands/ariegoldkin/claude-forge/notebooklm"><img src="https://agentmods.dev/badge/commands/ariegoldkin/claude-forge/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.
<a href="https://agentmods.dev/commands/ariegoldkin/claude-forge/notebooklm"><img src="https://agentmods.dev/badge/commands/ariegoldkin/claude-forge/notebooklm.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 | $0.00072 | $0.00255 |
| Opus 5.5 | $0.00029 | $0.00102 |
| Sonnet 5.5 | $0.00014 | $0.00051 |
| Haiku 4.5 | $0.00007 | $0.00026 |
Grade A, and why
notebooklm scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
notebooklm
Setup Verification
Before invoking the skill, verify the CLI is available:
notebooklm --version
If command not found:
- Install:
uv tool install "notebooklm-py[browser]" --python 3.12 - Add to PATH if needed: add
export PATH="$HOME/.local/bin:$PATH"to~/.zshrc - Install browser for OAuth:
uv tool run --from playwright playwright install chromium - Authenticate:
notebooklm login(opens browser for Google OAuth) - Verify:
notebooklm auth check --test
If installed but auth expired, re-run notebooklm login.
Skill
Invoking skill: atk:notebooklm
Follow the instructions in the notebooklm skill exactly.
$ARGUMENTS
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.
- 1mo ago First seen · 32 lines · 72 tokens per session scan A 384add280309
notebooklm is a command published in the GitHub repository ArieGoldkin/claude-forge (7 stars, last pushed 2mo ago), licensed MIT. It adds 72 tokens to every session and 255 once invoked, about $0.0003 per session on Opus 5.5. 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.
Other commands, from other repositories
octo-docs
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aqe-report
Generate comprehensive quality engineering reports with metrics, trends, and actionable insights.
council-extract
Extract a completed Perplexity Model Council response from the current browser tab, converting it to structured markdown with model attribution, and save to disk.
pdf-to-markdown-docling
Convert a local PDF to markdown via Docling's standard pipeline (layout + table-structure recognition — higher fidelity than MarkItDown on complex tables/layouts, 10x slower). Requires the opt-in Docling install.
pdf-to-markdown
Convert a local PDF to markdown via the bundled MarkItDown Python CLI (fast, lightweight — plain text extraction, no table-structure recognition). For complex tables/layouts prefer /obsidian-router:pdf-to-markdown-docling.
put
Upload file to reMarkable (converts markdown to PDF).