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 agentmods add skills/synthetic-sciences/openscience/chembl-databasenpx skills add synthetic-sciences/openscience --skill chembl-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/chembl-database)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/chembl-database"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/chembl-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 | $0.00045 | $0.02474 |
| Opus 5 | $0.00023 | $0.01237 |
| Sonnet 5 | $0.00009 | $0.00495 |
| Haiku 4.5 | $0.00005 | $0.00247 |
Grade A, and why
chembl-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 yesterday.
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.
Copies of this mod
8 near-identical copies found in the catalogue:
- chembl-database — 97% identical, 3 lines differ
- chembl-database — 97% identical, 3 lines differ
- chembl-database — 97% identical, 3 lines differ
- chembl-database — 97% identical, 4 lines differ
- chembl-database — 95% identical, 7 lines differ
- chembl-database — 86% identical, 6 lines differ
- chembl-database — 86% identical, 6 lines differ
- chembl-database — 86% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ChEMBL Database
Overview
ChEMBL is a manually curated database of bioactive molecules maintained by the European Bioinformatics Institute (EBI), containing over 2 million compounds, 19 million bioactivity measurements, 13,000+ drug targets, and data on approved drugs and clinical candidates. Access and query this data programmatically using the ChEMBL Python client for drug discovery and medicinal chemistry research.
When to Use This Skill
This skill should be used when:
- Compound searches: Finding molecules by name, structure, or properties
- Target information: Retrieving data about proteins, enzymes, or biological targets
- Bioactivity data: Querying IC50, Ki, EC50, or other activity measurements
- Drug information: Looking up approved drugs, mechanisms, or indications
- Structure searches: Performing similarity or substructure searches
- Cheminformatics: Analyzing molecular properties and drug-likeness
- Target-ligand relationships: Exploring compound-target interactions
- Drug discovery: Identifying inhibitors, agonists, or bioactive molecules
Installation and Setup
Python Client
The ChEMBL Python client is required for programmatic access:
uv pip install chembl_webresource_client
Basic Usage Pattern
from chembl_webresource_client.new_client import new_client
# Access different endpoints
molecule = new_client.molecule
target = new_client.target
activity = new_client.activity
drug = new_client.drug
Core Capabilities
1. Molecule Queries
Retrieve by ChEMBL ID:
molecule = new_client.molecule
aspirin = molecule.get('CHEMBL25')
Search by name:
results = molecule.filter(pref_name__icontains='aspirin')
Filter by properties:
# Find small molecules (MW <= 500) with favorable LogP
results = molecule.filter(
molecule_properties__mw_freebase__lte=500,
molecule_properties__alogp__lte=5
)
2. Target Queries
Retrieve target information:
target = new_client.target
egfr = target.get('CHEMBL203')
What ships with it
2 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.
- yesterday First seen · 389 lines · 45 tokens per session scan A ece1ce2874e9
chembl-database is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 2,474 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 2026-09-03.
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