mcpbeat Sign in

Xtb Agent Skill

Prepare and explain xTB semiempirical quantum-chemistry workflows for single-point energy, forces, charges, dipole, geometry optimization, and molecular dynamics. Use when the user asks for xTB calculations directly, or wants to use xTB through Python/ASE/dpdata bridges while keeping xTB as the primary method rather than as an ASE-only backend.

2k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
118
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/jinzhezenggroup/computational-chemistry-agent-skills --skill xtb

What comes with it

2 974 bytes besides the instruction
references/commands-and-workflow.md

The instruction itself

13 sections, as written by the author

xTB

Use this skill as the top-level xTB orchestration layer.

Scope

This skill should:

  • identify whether the user wants direct xTB usage or a Python bridge workflow
  • classify the task as static, optimization, or MD-style usage
  • keep xTB as the primary method in the user-facing framing
  • provide runnable examples for direct Python/ASE integration when appropriate
  • document how to connect xTB to dpdata driver/minimizer flows

This skill should not:

  • force the user into ASE if they asked for xTB itself
  • hide xTB-specific scientific choices behind generic backend wording
  • submit jobs directly; use dpdisp-submit if execution/submission is requested

Supported usage patterns

1. Direct xTB-oriented tasks

Use this skill when the user asks for:

  • xTB single-point energy
  • xTB forces / charges / dipole
  • xTB geometry optimization
  • xTB molecular dynamics
  • xTB method choice such as GFN0-xTB, GFN1-xTB, or GFN2-xTB
  • xTB solvent settings

2. xTB through the ASE bridge

If the user wants Python scripting, ASE integration, or ASE workflows, use:

from xtb.ase.calculator import XTB

Treat ASE as an integration layer, not the primary identity of the method.

3. xTB through dpdata

If the user wants labeled data or geometry minimization through dpdata, bridge via the ASE driver/minimizer while still presenting xTB as the force/energy method.

Practical installation notes

For one-off Python scripts, prefer uv run instead of uvx because this is a Python package used inside a Python script, not a standalone CLI tool.

Recommended pattern for the ASE bridge:

uv run --no-project --with ase --with xtb --with typing_extensions python your_script.py

Notes:

  • The PyPI package name is xtb.
  • The upstream docs/project are often referred to as xtb-python.
  • If ModuleNotFoundError: typing_extensions appears, add --with typing_extensions explicitly.

Method selection guidance

  • GFN2-xTB: default choice for most molecular single-point and force evaluations
  • GFN1-xTB: use when there is a user or literature reason
  • GFN0-xTB: use when the workflow specifically needs xTB-level stress through the ASE bridge

Detailed runnable examples

For copy-paste-ready command and script patterns, see:

  • references/commands-and-workflow.md

Use that reference when the user specifically wants a minimal runnable example for:

  • single-point energy / forces
  • geometry optimization
  • dpdata driver labeling
  • dpdata minimization

ASE bridge example

from ase.build import molecule
from xtb.ase.calculator import XTB

atoms = molecule("H2O")
atoms.calc = XTB(method="GFN2-xTB")
print(atoms.get_potential_energy())
print(atoms.get_forces())
print(atoms.get_charges())

Common calculator arguments:

  • method
  • accuracy
  • electronic_temperature
  • max_iterations
  • solvent
  • cache_api

Property support through the ASE bridge includes:

  • energy / free_energy
  • forces
  • dipole
  • charges
  • stress for GFN0-xTB only

dpdata driver bridge

If the user wants dpdata labeling:

from dpdata.system import System
from xtb.ase.calculator import XTB

sys = System("input.xyz", fmt="xyz")
ls = sys.predict(driver="ase", calculator=XTB(method="GFN2-xTB"))

This connects naturally to tools/dpdata-driver.

dpdata minimizer bridge

If the user wants dpdata geometry minimization:

from dpdata.driver import Driver
from dpdata.system import System
from xtb.ase.calculator import XTB

sys = System("input.xyz", fmt="xyz")
ase_driver = Driver.get_driver("ase")(calculator=XTB(method="GFN2-xTB"))
ls = sys.minimize(minimizer="ase", driver=ase_driver, fmax=0.05, max_steps=200)

This connects naturally to tools/dpdata-minimizer.

Output expectations

Provide:

  • the selected xTB usage mode
  • runnable command or script pattern
  • method/solvent/accuracy assumptions
  • unresolved scientific choices
  • handoff to dpdisp-submit if execution/submission is requested

Other skills for the same job

different authors, same section of the catalogue
MCP Builder
by anthropics
vendor ×13

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

30k tokens scripts
Changelog Generator
by frostant
×9

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

774 tokens
Finishing A Development Branch
by ZhanlinCui
×7

Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup

1k tokens
MCP Builder
by JayZeeDesign
×7

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

37k tokens scripts
Vercel React Native Skills
by vercel-labs
vendor ×6

React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.

39k tokens
Vercel React Best Practices
by ratacat
×5

React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.

34k tokens
Next Best Practices
by vercel-labs
vendor ×4

Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling

20k tokens
Using Git Worktrees
by ZhanlinCui
×4

Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification

1k tokens

How to use it

Copy the folder

Take jinzhezenggroup/xtb from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.