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 skills add aksika/abtars --skill nlmgit clone --depth 1 https://github.com/aksika/abtarsWrote 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/aksika/abtars/nlm)<a href="https://agentmods.dev/skills/aksika/abtars/nlm"><img src="https://agentmods.dev/badge/skills/aksika/abtars/nlm/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/aksika/abtars/nlm"><img src="https://agentmods.dev/badge/skills/aksika/abtars/nlm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00016 | $0.00262 |
| Opus 5.5 | $0.00006 | $0.00105 |
| Sonnet 5.5 | $0.00003 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
nlm 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
Knowledge Base Query (NotebookLM)
Query curated reference documents — research papers, technical docs, guides.
nlm notebook query <NOTEBOOK_ID> "your question here" --json
Other commands:
nlm notebook list --json # list notebooks
nlm notebook create <name> --json # create notebook
nlm source list <NOTEBOOK_ID> --json # list sources
Troubleshooting
If nlm returns 400 Bad Request or auth errors, the session has expired. Fix:
nlm login
This opens a browser for Google re-authentication. Happens periodically when Google rotates session tokens.
Returns JSON: answer, sources_used.
When to use
- Questions about uploaded reference material (docs, research, guides)
- Technical documentation lookups local memory wouldn't have
- Local memory search returned nothing and question is about curated knowledge
When NOT to use
- Answer already in conversation context
- Personal memories / past conversations (use memory-search)
- Real-time / live information (knowledge base is static)
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 · 41 lines · 16 tokens per session scan A 7c41c75df064
nlm is a skill published in the GitHub repository aksika/abtars (10 stars, last pushed today), licensed Apache-2.0. It adds 16 tokens to every session and 262 once invoked, about $0.0001 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.
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