synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 synthetic-sciences/openscience --skill pubchem-databasegit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/pubchem-database)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/pubchem-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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 | $0.04065 |
| Opus 5 | $0.00024 | $0.02032 |
| Sonnet 5 | $0.00010 | $0.00813 |
| Haiku 4.5 | $0.00005 | $0.00407 |
Grade A, and why
pubchem-database scanned grade A with 1 finding 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 3d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(url) Copies of this mod
6 near-identical copies found in the catalogue:
- pubchem-database — 97% identical, 3 lines differ
- pubchem-database — 97% identical, 3 lines differ
- pubchem-database — 95% identical, 7 lines differ
- pubchem-database — 86% identical, 5 lines differ
- pubchem-database — 86% identical, 6 lines differ
- pubchem-database — 86% identical, 5 lines differ
How it starts
The opening of the file, as written. The whole thing — 574 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PubChem Database
Overview
PubChem is the world's largest freely available chemical database with 110M+ compounds and 270M+ bioactivities. Query chemical structures by name, CID, or SMILES, retrieve molecular properties, perform similarity and substructure searches, access bioactivity data using PUG-REST API and PubChemPy.
When to Use This Skill
This skill should be used when:
- Searching for chemical compounds by name, structure (SMILES/InChI), or molecular formula
- Retrieving molecular properties (MW, LogP, TPSA, hydrogen bonding descriptors)
- Performing similarity searches to find structurally related compounds
- Conducting substructure searches for specific chemical motifs
- Accessing bioactivity data from screening assays
- Converting between chemical identifier formats (CID, SMILES, InChI)
- Batch processing multiple compounds for drug-likeness screening or property analysis
Core Capabilities
1. Chemical Structure Search
Search for compounds using multiple identifier types:
By Chemical Name:
import pubchempy as pcp
compounds = pcp.get_compounds('aspirin', 'name')
compound = compounds[0]
By CID (Compound ID):
compound = pcp.Compound.from_cid(2244) # Aspirin
By SMILES:
compound = pcp.get_compounds('CC(=O)OC1=CC=CC=C1C(=O)O', 'smiles')[0]
By InChI:
compound = pcp.get_compounds('InChI=1S/C9H8O4/...', 'inchi')[0]
By Molecular Formula:
compounds = pcp.get_compounds('C9H8O4', 'formula')
# Returns all compounds matching this formula
2. Property Retrieval
Retrieve molecular properties for compounds using either high-level or low-level approaches:
Using PubChemPy (Recommended):
import pubchempy as pcp
# Get compound object with all properties
compound = pcp.get_compounds('caffeine', 'name')[0]
# Access individual properties
molecular_formula = compound.molecular_formula
molecular_weight = compound.molecular_weight
iupac_name = compound.iupac_name
smiles = compound.canonical_smiles
inchi = compound.inchi
xlogp = compound.xlogp # Partition coefficient
tpsa = compound.tpsa # Topological polar surface area
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.
- 3d ago First seen · 574 lines · 49 tokens per session scan A e98b0576565f
pubchem-database is a skill published in the GitHub repository synthetic-sciences/openscience (3,501 stars, last pushed today), licensed Apache-2.0. It adds 49 tokens to every session and 4,065 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
songsee
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arxiv
Search arXiv papers by keyword, author, category, or ID.
research-paper-writing
Write ML papers for NeurIPS/ICML/ICLR: design→submit.
paper-revision-author
Revise independently drafted paper sections into one coherent LaTeX body before the abstract is written.
paper-plot-stub
Plot a results CSV (x, ybaseline, yours) as a two-line matplotlib chart and write a PDF. Demo-only.
google-workspace-setup
One-time setup for gws: install, OAuth, scopes, auto-approve.