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 learningmatter-mit/AtomisticSkills --skill general-chemical-literaturegit 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-chemical-literature)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/general-chemical-literature"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/general-chemical-literature/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/learningmatter-mit/atomisticskills/general-chemical-literature"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/general-chemical-literature.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 | $0.00025 | $0.00482 |
| Opus 5 | $0.00013 | $0.00241 |
| Sonnet 5 | $0.00005 | $0.00096 |
| Haiku 4.5 | $0.00003 | $0.00048 |
Grade A, and why
general-chemical-literature 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 9d 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
Chemical Literature and Patent Mapping
Goal
To programmatically check if a specific chemical compound exists in recent literature or patent databases. This skill uses PubChem's PUG-REST XRefs endpoint to extract an exhaustive list of associated PubMed IDs and Patent numbers.
This is incredibly useful as an autonomous "novelty check" for generated molecules.
Instructions
1. Extract Literature and Patents by CID
Provide the CID of the target molecule. By default, the script will output the absolute total number of hits but limits the JSON save array to 1000 to prevent memory flooding for ubiquitous molecules (like Aspirin, which has over 100,000 patents). Adjust --limit as needed.
# Env: base-agent
python .agents/skills/general-chemical-literature/scripts/get_xrefs.py \
--cid 2244 \
--limit 50 \
--outdir research/aspirin_literature \
--output xrefs_aspirin.json
Examples
We can pull cross-references for Aspirin (CID: 2244), saving the top 50 identifiers.
# Env: base-agent
python .agents/skills/general-chemical-literature/scripts/get_xrefs.py \
--cid 2244 \
--limit 50 \
--outdir .agents/skills/general-chemical-literature/examples/aspirin \
--output xrefs_aspirin.json
Constraints
- Novelty Assessment Limitation: If 0 PMIDs or Patents are returned, it strongly implies the molecule is highly novel (or purely computational), but it does not guarantee absolute non-existence.
- Link Generation: The script automatically prints actionable links (
pubmed.ncbi.nlm.nih.gov/andpatents.google.com/patent/) for the top 5 results for immediate verification. - Network Limits: Handled internally via standard exponential backoff.
Author: Bowen Deng Contact: GitHub @learningmatter-mit
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.
- 9d ago First seen · 51 lines · 25 tokens per session scan A 758be4363578
general-chemical-literature is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (162 stars, last pushed 6d ago), licensed MIT. It adds 25 tokens to every session and 482 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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