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/learningmatter-mit/atomisticskills/general-query-literature-databasenpx skills add learningmatter-mit/AtomisticSkills --skill general-query-literature-databasegit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/general-query-literature-database)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/general-query-literature-database"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/general-query-literature-database.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 | $0.00018 | $0.00183 |
| Opus 5 | $0.00009 | $0.00092 |
| Sonnet 5 | $0.00004 | $0.00037 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
general-query-literature-database 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
Query Literature Databases
The in-house literature database consists of markdown (.md) files stored in the .agents/workflows/ directory. Each file contains a workflow guide for a specific research topic, summarized from academic papers.
To efficiently find the most relevant workflow guide without reading every file in full, you must rely on the short descriptions located in each file's YAML header.
Execution Instructions:
Scan Database: Because the database is currently small, use the following command to quickly extract all descriptions:
grep -rn "^description:" .agents/workflows/
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 · 23 lines · 18 tokens per session scan A f7ac99721c34
general-query-literature-database is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (158 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 183 once invoked, about $0.0001 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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