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 dralkh/seerai --skill rowangit clone --depth 1 https://github.com/dralkh/seeraiWrote 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/dralkh/seerai/rowan)<a href="https://agentmods.dev/skills/dralkh/seerai/rowan"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/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/dralkh/seerai/rowan"><img src="https://agentmods.dev/badge/skills/dralkh/seerai/rowan.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00100 | $0.09713 |
| Opus 5.5 | $0.00040 | $0.03885 |
| Sonnet 5.5 | $0.00020 | $0.01943 |
| Haiku 4.5 | $0.00010 | $0.00971 |
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 7d 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 — 1,087 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rowan: Cloud-Native Molecular-Modeling and Drug-Design Workflows
Overview
Rowan is a cloud-native workflow platform for molecular simulation, medicinal chemistry, and structure-based design. Its Python API exposes a unified interface for small-molecule modeling, property prediction, docking, molecular dynamics, and AI structure workflows.
Use Rowan when you want to run medicinal-chemistry or molecular-design workflows programmatically without maintaining local HPC infrastructure, GPU provisioning, or a collection of separate modeling tools. Rowan handles all infrastructure, result management, and computation scaling.
When to use Rowan
Rowan is a good fit for:
- Quantum chemistry, semiempirical methods, or neural network potentials
- Batch property prediction (pKa, descriptors, permeability, solubility)
- Conformer and tautomer ensemble generation
- Docking workflows (single-ligand, analogue series, pose refinement)
- Protein-ligand cofolding and MSA generation
- Multi-step chemistry pipelines (e.g., tautomer search → docking → pose analysis)
- Batch medicinal-chemistry campaigns where you need consistent, scalable infrastructure
Rowan is not the right fit for:
- Simple molecular I/O (use RDKit directly)
- Post-HF ab initio quantum chemistry or relativistic calculations
Access and pricing model
Rowan uses a credit-based usage model. All users, including free-tier users, can create API keys and use the Python API.
Free-tier access
- Access to all Rowan core workflows
- 20 credits per week
- 500 signup credits
Pricing and credit consumption
Credits are consumed according to compute type:
- CPU: 1 credit per minute
- GPU: 3 credits per minute
- H100/H200 GPU: 7 credits per minute
Purchased credits are priced per credit and remain valid for up to one year from purchase.
Typical cost estimates
| Workflow | Typical Runtime | Estimated Credits | Notes |
|---|---|---|---|
| Descriptors | <1 min | 0.5–2 | Lightweight, good for triage |
| pKa (single transition) | 2–5 min | 2–5 | Depends on molecule size |
| MacropKa (pH 0–14) | 5–15 min | 5–15 | Broader sampling, higher cost |
| Conformer search | 3–10 min | 3–10 | Ensemble quality matters |
| Tautomer search | 2–5 min | 2–5 | Heterocyclic systems |
| Docking (single ligand) | 5–20 min | 5–20 | Depends on pocket size, refinement |
| Analogue docking series (10–50 ligands) | 30–120 min | 30–100+ | Shared reference frame |
| MSA generation | 5–30 min | 5–30 | Sequence length dependent |
| Protein-ligand cofolding | 15–60 min | 20–50+ | AI structure prediction, GPU-heavy |
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.
- 7d ago First seen · 1,087 lines · 100 tokens per session scan A 10cfe5332ffb
rowan is a skill published in the GitHub repository dralkh/seerai (84 stars, last pushed 8d ago), licensed MIT. It adds 100 tokens to every session and 9,713 once invoked, about $0.0004 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 2026-09-24.
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