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/molecular-ragnpx skills add synthetic-sciences/openscience --skill molecular-raggit 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-rag)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/molecular-rag"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/molecular-rag.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 | $0.00040 | $0.00658 |
| Opus 5 | $0.00020 | $0.00329 |
| Sonnet 5 | $0.00008 | $0.00132 |
| Haiku 4.5 | $0.00004 | $0.00066 |
Grade A, and why
molecular-rag 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 2d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular RAG (Retrieval-Augmented Generation)
Overview
LLMs hallucinate molecular properties. This skill grounds predictions by retrieving structurally similar compounds with experimentally measured properties from ChEMBL and ZINC. When the agent says "this compound should have good hERG safety," it can now check what happened with similar compounds in real assays.
Based on:
- MolRAG (Xian et al., 2025, ACL): RAG for molecular property prediction — retrieves similar compounds to ground LLM predictions
When to Use This Skill
- Before property prediction: Retrieve analogs with known properties for context
- Lead optimization: Find what modifications worked for similar scaffolds
- Novelty assessment: Check if your generated molecule is truly novel or already known
- SAR grounding: Ground structure-activity reasoning in experimental data
Do NOT use this skill for:
- Bulk database queries (use
chembl-databaseorpubchem-databasedirectly) - De novo generation (use
denovo-design)
Related Skills
- chembl-database: Direct ChEMBL API access
- pubchem-database: PubChem compound lookup
- zinc-database: ZINC compound search
- admet-reasoning: Interpret properties of retrieved analogs
Installation
pip install rdkit-pypi requests pandas
Core Workflows
1. Find Similar Compounds with Known Properties
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--similarity-threshold 0.6 \
--max-results 20 \
--output analogs.json
2. Target-Specific Analog Search
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--target CHEMBL25 \
--output target_analogs.json
3. SAR Context for Optimization
python scripts/retrieve_analogs.py \
--smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \
--include-activities \
--output sar_context.json
Script Reference
| Script | Purpose | Key Outputs |
|---|---|---|
retrieve_analogs.py |
Find similar compounds with experimental data | JSON with analogs, similarities, bioactivities |
What ships with it
1 file 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.
- 2d ago First seen · 80 lines · 40 tokens per session scan A c31376e3a2a1
molecular-rag is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 40 tokens to every session and 658 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.
Other skills, from other repositories
rag-retrieval
Retrieval-Augmented Generation patterns for grounded LLM responses. Use when building RAG pipelines, embedding documents, implementing hybrid search, contextual retrieval, HyDE, agentic RAG, multimodal RAG, query decomposition, reranking, or pgvector search.
Vector Databases
Guides retrieval-store design, indexing, and query behavior for embedding-backed systems without confusing storage with application truth.
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.
unified-llm-api
Call model APIs through @prismshadow/agenthub — streaming text generation, image generation, speech synthesis, embeddings and the supported-model registry with one client.
admet_genetic
ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and write candidate triage reports.
chem-msms-predict
Predict LC-MS/MS (MS2, tandem mass spectra) from SMILES via ICEBERG, a two-stage deep neural network. Outputs predicted m/z vs intensity spectrum, fragment ion SMILES, and a spectrum plot.