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 wentorai/research-plugins --skill chemgraph-agent-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/chemgraph-agent-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/chemgraph-agent-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/chemgraph-agent-guide/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/wentorai/research-plugins/chemgraph-agent-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/chemgraph-agent-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
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 →
- 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.
- medium MCP Rug Pull · line 25 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00016 | $0.00861 |
| Opus 5 | $0.00008 | $0.00430 |
| Sonnet 5 | $0.00003 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
chemgraph-agent-guide 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 6d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ChemGraph Agent Guide
Overview
ChemGraph is an agentic framework from Argonne National Lab that automates molecular simulation workflows using LLMs. Built on LangGraph and ASE (Atomic Simulation Environment), it enables natural language control of computational chemistry tasks — structure generation, geometry optimization, thermochemistry, and more. Supports DFT (NWChem, ORCA), semi-empirical (xTB), and ML potentials (MACE).
Installation
pip install chemgraph
# Or via Docker
docker pull ghcr.io/argonne-lcf/chemgraph:latest
Core Capabilities
Natural Language Chemistry
from chemgraph import ChemGraphAgent
agent = ChemGraphAgent(
llm_provider="anthropic",
calculator="xtb", # fast semi-empirical
)
# Natural language molecular tasks
result = agent.run("Optimize the geometry of caffeine and calculate its vibrational frequencies")
print(result.energy)
print(result.frequencies)
# Thermochemistry
result = agent.run("Calculate the enthalpy of formation of ethanol at 298K")
print(f"ΔHf = {result.enthalpy:.2f} kJ/mol")
Supported Calculators
| Calculator | Type | Speed | Accuracy |
|---|---|---|---|
| xTB (TBLite) | Semi-empirical | Fast | Moderate |
| MACE | ML potential | Fast | Good |
| NWChem | Ab initio DFT | Slow | High |
| ORCA | Ab initio/DFT | Slow | High |
| UMA | Universal ML | Fast | Good |
Workflow Automation
# Multi-step workflow
workflow = agent.create_workflow([
"Generate 3D structure of aspirin from SMILES",
"Optimize geometry with DFT/B3LYP/6-31G*",
"Calculate IR spectrum",
"Identify key functional group vibrations",
])
results = workflow.execute()
# Reaction pathway
pathway = agent.run(
"Find the transition state for the Diels-Alder reaction "
"between butadiene and ethylene"
)
Integration with ASE
from ase.io import read
from chemgraph.calculators import get_calculator
# Use ChemGraph's calculator with ASE directly
atoms = read("molecule.xyz")
calc = get_calculator("xtb")
atoms.calc = calc
energy = atoms.get_potential_energy()
forces = atoms.get_forces()
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.
- 6d ago First seen · 121 lines · 16 tokens per session scan A 901aced2bfb4
chemgraph-agent-guide is a skill published in the GitHub repository wentorai/research-plugins (291 stars, last pushed 2mo ago), licensed MIT. It adds 16 tokens to every session and 861 once invoked, about $0.0001 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.
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