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 beita6969/ScienceClaw --skill chembl-databasegit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/chembl-database)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/chembl-database"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/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 5.1 | $0.00045 | $0.02472 |
| Opus 5 | $0.00023 | $0.01236 |
| Sonnet 5 | $0.00009 | $0.00494 |
| 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 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.
This is a copy
97% identical to chembl-database — 3 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 — 388 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.
- 9d ago First seen · 388 lines · 45 tokens per session scan A 5f8a749e3857
chembl-database is a skill published in the GitHub repository beita6969/ScienceClaw (896 stars, last pushed 3mo ago), licensed MIT. It adds 45 tokens to every session and 2,472 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to chembl-database, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use…
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
biomcp
Search and retrieve biomedical data - genes, variants, clinical trials, diagnostic tests, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. Use for gene function, variant pathogenicity, trials, diagnostics, drug safety, pathway context, disease workups…
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.