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 molecular-optimizationgit 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/molecular-optimization)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/molecular-optimization"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/molecular-optimization/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/synthetic-sciences/openscience/molecular-optimization"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/molecular-optimization.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 | $0.00034 | $0.01389 |
| Opus 5.5 | $0.00014 | $0.00556 |
| Sonnet 5.5 | $0.00007 | $0.00278 |
| Haiku 4.5 | $0.00003 | $0.00139 |
Grade A, and why
molecular-optimization 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 1mo 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular Optimization
Overview
Lead optimization is the bottleneck of drug discovery — modifying a hit compound to improve potency, selectivity, and ADMET properties without breaking what already works. LLMs frequently generate invalid SMILES or propose modifications that don't appear in the actual structure.
This skill implements an iterative optimization protocol based on three peer-reviewed approaches:
- MT-Mol (Kim et al., 2025): Multi-agent tool-based reasoning with verification — SOTA on 17/23 PMO benchmark tasks
- DrugR (Liu et al., 2026): Explicit liability reasoning before generation — 18× improvement over blind generation
- MultiMol (Yu et al., 2025): Generate-then-rank with scaffold preservation — 82.3% multi-objective success rate
The core loop: Analyze → Identify Liabilities → Generate Candidates → Verify → Evaluate & Rank → Iterate.
When to Use This Skill
- Lead optimization: Improve ADMET properties of a hit while preserving potency
- Scaffold hopping: Find new scaffolds that maintain key pharmacophoric features
- Property-driven design: Generate analogs targeting specific property improvements (lower LogP, reduce hERG, improve solubility)
- Multi-objective optimization: Balance multiple properties simultaneously
Do NOT use this skill for:
- De novo design from scratch (use
denovo-designinstead) - Simple property prediction without optimization (use
admet-prediction) - Docking or binding affinity estimation (use
molecular-docking,binding-affinity)
Related Skills
- admet-prediction: Compute ADMET properties (this skill uses it internally)
- admet-reasoning: Interpretable ADMET analysis with mechanistic explanations
- smiles-validation: Strict SMILES parsing and structural verification
- rdkit: Core molecular operations
- medchem: Medicinal chemistry filters and transformations
Installation
Required dependencies
pip install rdkit-pypi numpy pandas
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.
- 1mo ago First seen · 161 lines · 34 tokens per session scan A 28af9bc27fb4
molecular-optimization is a skill published in the GitHub repository synthetic-sciences/openscience (4,160 stars, last pushed today), licensed Apache-2.0. It adds 34 tokens to every session and 1,389 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
drug-lead-analysis
Analyze drug candidate molecules for drug-likeness, ADMET properties, and safety profiles. Use this skill when: (1) Evaluating a molecule's potential as a drug candidate, (2) Checking drug-likeness scores (QED, Lipinski), (3) Predicting blood-brain barrier penetration, (4) Assessing side effects and ADMET properties…
songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
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.