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 rowangit 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/rowan)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/rowan"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/rowan/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/rowan"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/rowan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00115 | $0.03137 |
| Opus 5.5 | $0.00046 | $0.01255 |
| Sonnet 5.5 | $0.00023 | $0.00627 |
| Haiku 4.5 | $0.00012 | $0.00314 |
Grade A, and why
rowan 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- rowan — 92% identical, 12 lines differ
How it starts
The opening of the file, as written. The whole thing — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rowan: Cloud-Based Quantum Chemistry Platform
Overview
Rowan is a cloud-based computational chemistry platform that provides programmatic access to quantum chemistry workflows through a Python API. It enables automation of complex molecular simulations without requiring local computational resources or expertise in multiple quantum chemistry packages.
Key Capabilities:
- Molecular property prediction (pKa, redox potential, solubility, ADMET-Tox)
- Geometry optimization and conformer searching
- Protein-ligand docking with AutoDock Vina
- AI-powered protein cofolding with Chai-1 and Boltz models
- Access to DFT, semiempirical, and neural network potential methods
- Cloud compute with automatic resource allocation
Why Rowan:
- No local compute cluster required
- Unified API for dozens of computational methods
- Results viewable in web interface at labs.rowansci.com
- Automatic resource scaling
Installation and Authentication
Installation
uv pip install rowan-python
Authentication
Generate an API key at labs.rowansci.com/account/api-keys.
Option 1: Direct assignment
import rowan
rowan.api_key = "your_api_key_here"
Option 2: Environment variable (recommended)
export ROWAN_API_KEY="your_api_key_here"
The API key is automatically read from ROWAN_API_KEY on module import.
Verify Setup
import rowan
# Check authentication
user = rowan.whoami()
print(f"Logged in as: {user.username}")
print(f"Credits available: {user.credits}")
Core Workflows
1. pKa Prediction
Calculate the acid dissociation constant for molecules:
import rowan
import stjames
# Create molecule from SMILES
mol = stjames.Molecule.from_smiles("c1ccccc1O") # Phenol
# Submit pKa workflow
workflow = rowan.submit_pka_workflow(
initial_molecule=mol,
name="phenol pKa calculation"
)
# Wait for completion
workflow.wait_for_result()
workflow.fetch_latest(in_place=True)
# Access results
print(f"Strongest acid pKa: {workflow.data['strongest_acid']}") # ~10.17
What ships with it
6 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 Changed · +6 lines a3c97ebf8a2d
- 1mo ago First seen · 426 lines · 115 tokens per session scan A aca7a5236586
rowan is a skill published in the GitHub repository synthetic-sciences/openscience (4,121 stars, last pushed 2d ago), licensed Apache-2.0. It adds 115 tokens to every session and 3,137 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
coding-plan
Enter "plan mode" for a coding task — read the relevant code, optionally ask clarifying questions, design one recommended approach, then write a plain, scannable plans/ - .md file. No source files are edited in this mode. Use this skill whenever the user says "plan mode", "/coding-plan", "make a plan", "draft a plan…
deep-research-plan
Enter "plan mode" for a deep-research or article-writing task — search the web, fetch sources, optionally run experiments in Python/Node, design one recommended outline and research strategy, then write a plain, scannable plans/ - .md file. No article content is drafted in this mode. Use this skill whenever the user…
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.