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 silverstein/claude-scientific-skills-desktop --skill pubchem-databasegit clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktopWrote 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/silverstein/claude-scientific-skills-desktop/pubchem-database)<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/pubchem-database"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/pubchem-database/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/silverstein/claude-scientific-skills-desktop/pubchem-database"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/pubchem-database.svg" alt="Reviewed on agentmods" width="80" 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 | $0.04049 |
| Opus 5 | $0.00024 | $0.02024 |
| Sonnet 5 | $0.00010 | $0.00810 |
| Haiku 4.5 | $0.00005 | $0.00405 |
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 7d 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) This is a copy
95% identical to pubchem-database — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 569 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.
- 7d ago First seen · 569 lines · 49 tokens per session scan A 5ae16d7d3fe5
pubchem-database is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 4,049 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 95% identical to pubchem-database, differing in 7 lines, and is treated as a copy.
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