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 LeonChaoX/qinyan-academic-skills --skill rowangit clone --depth 1 https://github.com/LeonChaoX/qinyan-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/leonchaox/qinyan-academic-skills/rowan)<a href="https://agentmods.dev/skills/leonchaox/qinyan-academic-skills/rowan"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/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/leonchaox/qinyan-academic-skills/rowan"><img src="https://agentmods.dev/badge/skills/leonchaox/qinyan-academic-skills/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 | $0.03227 |
| Opus 5.5 | $0.00046 | $0.01291 |
| Sonnet 5.5 | $0.00023 | $0.00645 |
| Haiku 4.5 | $0.00012 | $0.00323 |
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 — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 428 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 First seen · 428 lines · 115 tokens per session scan A 9690cf9dbe3c
rowan is a skill published in the GitHub repository LeonChaoX/qinyan-academic-skills (979 stars, last pushed 3mo ago), licensed MIT. It adds 115 tokens to every session and 3,227 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
code-execution
Execute scientific Python code for computation, data analysis, simulation, and verification. Use when: (1) running statistical analyses, (2) numerical computation, (3) data processing pipelines, (4) verifying calculations, (5) running simulations. NOT for: literature search (use literature-search), writing papers (use…
model-assessment
Use when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.
model-scaffold
Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
radiomics-ml
Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.
imaging-data
Use when preparing a medical-imaging dataset (DICOM/NIfTI) for modelling. Profiles spacing, orientation, intensity, label integrity, foreground fraction and target volume, gates them against the plan, then plans and audits preprocessing and augmentation for leakage.
model-selection
Use when choosing the model for a medical-imaging study. Picks a paper-grounded architecture family (CNN/ViT, U-Net/nnU-Net, detection, SAM/foundation, GNN), then vets the concrete repo or checkpoint for licence, version pin, weight provenance and benchmark overlap.