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 AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-medchemgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/alterlab-medchem)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-medchem"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-medchem/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/alterlab-ieu/alterlab-academic-skills/alterlab-medchem"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-medchem.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00086 | $0.03708 |
| Opus 5 | $0.00043 | $0.01854 |
| Sonnet 5 | $0.00017 | $0.00742 |
| Haiku 4.5 | $0.00009 | $0.00371 |
Grade A, and why
alterlab-medchem 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 11d 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 — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Medchem
Overview
Medchem (datamol-io/medchem) is a Python library for molecular filtering and prioritization in drug-discovery workflows: medicinal-chemistry rules, structural alerts (ChEMBL/NIBR/PAINS), chemical-group detection, complexity metrics, and a query DSL. Rules and filters are context-specific guidelines, not hard truth — combine with domain expertise.
Verified against medchem==2.0.5 (RDKit 2026.3.x, Python 3.12). API names below are checked against this version; earlier docs/blog posts described a different surface.
When to Use This Skill
This skill should be used when:
- Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries
- Filtering molecules by structural alerts or PAINS patterns
- Prioritizing compounds for lead optimization
- Assessing compound quality and medicinal chemistry properties
- Detecting reactive or problematic functional groups
- Calculating molecular complexity metrics
Installation
uv pip install medchem # PyPI; pulls rdkit + datamol
Two features need extra native deps that PyPI cannot provide:
- Lilly demerits (
lilly_demerit_filter) shells out to compiled binaries — install via conda:mamba install -c conda-forge lilly-medchem-rules. Without them, the call raisesImportError. - The ChemAxon rule (
rule_of_chemaxon_druglikeness) needs a licensed ChemAxon install.
Everything else (RuleFilters, CommonAlerts, NIBR, complexity, groups, query) works from the PyPI wheel alone.
Core Capabilities
Conventions that hold across medchem. Filters take
mols(a sequence of SMILES strings or RDKit mols), default ton_jobs=-1(all cores), and acceptprogress=True. Themedchem.structural/medchem.rulesfilter classes return a pandas DataFrame (one row per input mol); themedchem.functional.*helpers return a NumPy boolean array whereTrue= the molecule passes / is kept. Get the canonical rule and alert names frommc.rules.RuleFilters.list_available_rules()andmc.structural.CommonAlertsFilters.list_default_available_alerts()rather than guessing.
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.
- 11d ago First seen · 275 lines · 86 tokens per session scan A 735b7040c759
alterlab-medchem is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 86 tokens to every session and 3,708 once invoked, about $0.0004 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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