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-rowangit 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-rowan)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-rowan"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-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/alterlab-ieu/alterlab-academic-skills/alterlab-rowan"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-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 | $0.00121 | $0.03441 |
| Opus 5 | $0.00060 | $0.01721 |
| Sonnet 5 | $0.00024 | $0.00688 |
| Haiku 4.5 | $0.00012 | $0.00344 |
Grade A, and why
alterlab-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 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 — 368 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
Requires Python >= 3.12. This skill targets rowan-python 3.x (the current major version; v2 had a different result API).
uv pip install "rowan-python>=3.0"
Installing rowan-python also pulls in stjames (molecule/result models) and rdkit.
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}")
The Result Pattern (read this first)
Every submit_*_workflow returns a Workflow. Do NOT read workflow.data[...] by hand and do NOT call the deprecated wait_for_result(). The v3 idiom is a single call:
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.
- 11d ago First seen · 368 lines · 121 tokens per session scan A 3c09b9d52913
alterlab-rowan is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 6d ago), licensed MIT. It adds 121 tokens to every session and 3,441 once invoked, about $0.0006 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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