Science Skills is a collection of add-ons that give AI agents structured instructions, scripts, and references for scientific research, including genomics, structural biology, cheminformatics, and literature search. Researchers use it to guide agents through specialized scientific tasks with information from databases and tools such as AlphaGenome, AFDB, and UniProt. The catalogue entries are individual skills from this collection.
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/google-deepmind/science-skills/pubchem_databasenpx skills add google-deepmind/science-skills --skill pubchem_databasegit clone --depth 1 https://github.com/google-deepmind/science-skillsWrote 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/google-deepmind/science-skills/pubchem_database)<a href="https://agentmods.dev/skills/google-deepmind/science-skills/pubchem_database"><img src="https://agentmods.dev/badge/skills/google-deepmind/science-skills/pubchem_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 5.1 | $0.00049 | $0.01403 |
| Opus 5 | $0.00024 | $0.00701 |
| Sonnet 5 | $0.00010 | $0.00281 |
| Haiku 4.5 | $0.00005 | $0.00140 |
Grade A, and why
pubchem-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 6d 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PubChem Database
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.- User Notification: If .licenses/pubchem_database_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://pubchem.ncbi.nlm.nih.gov/docs/citation-guidelines and https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest, then (2) create the file recording the notification text and timestamp.
Quick Start
PubChem queries are executed via a robust Python wrapper script to respect terms-of-service and handle complex JSON parsing. This script allows safe multi-agent use of APIs.
Example: Resolve a chemical name to its Compound ID (CID)
uv run scripts/pubchem_api.py resolve --name "aspirin" --output result.json
Core Rules
- Use the Wrapper: ALWAYS execute the provided helper scripts to query the database rather than accessing the database directly. The scripts automatically enforce the required rate limit gracefully.
- Read the generated JSON output file, and process it with jq or code.
- Verify Facts: ALWAYS verify information retrieved from memory with a database query if the user asks for a specific fact that can be checked in PubChem. Do not rely solely on internal knowledge.
- Notification: If this skill is used, ensure this is mentioned in the output.
Core Capabilities
1. Compound Resolution (Name or InChI to Identifiers) Convert chemical/trade names or InChI strings into PubChem CIDs, SMILES, and InChIKeys.
uv run scripts/pubchem_api.py resolve --name "ibuprofen" --output result.json
# OR
uv run scripts/pubchem_api.py resolve --inchi "InChI=1S/C3/c1-3-2/i1+1" --output result.json
2. Physical & Chemical Property Retrieval Fetch computed properties (e.g., MolecularWeight, XLogP, TPSA).
uv run scripts/pubchem_api.py properties --cid 2244 --output result.json
What ships with it
4 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.
- 6d ago First seen · 165 lines · 49 tokens per session scan A dfb7876a2da5
pubchem-database is a skill published in the GitHub repository google-deepmind/science-skills (2,844 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 1,403 once invoked, about $0.0002 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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