Unit Tests
thedaviddias/Front-End-Checklist
A skill your agent uses when reviewing CI coverage, automated checks, or test strategy related to Write unit tests.
Reference guide for energy, force, and stress units across MLIPs, DFT codes, and ASE, including conversion factors.
MITAuto-check passed
Install General Property Units
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-property-units --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/general-property-units .claude/skills/general-property-units && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "general-property-units" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-property-units into .claude/skills/general-property-units/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-property-units", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-property-unitsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-property-units --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/general-property-units .agents/skills/general-property-units && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "general-property-units" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-property-units into .agents/skills/general-property-units/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-property-units", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-property-units --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/general-property-units .cursor/skills/general-property-units && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "general-property-units" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-property-units into .cursor/skills/general-property-units/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-property-units", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/learningmatter-mit/AtomisticSkills.git --path skills/general-property-units--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-property-units --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/general-property-units .gemini/skills/general-property-units && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "general-property-units" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-property-units into .gemini/skills/general-property-units/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-property-units", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install learningmatter-mit/AtomisticSkills general-property-unitsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/general-property-units .github/skills/general-property-units && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "general-property-units" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-property-units into .github/skills/general-property-units/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-property-units", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills general-property-units --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/general-property-units .opencode/skills/general-property-units && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "general-property-units" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/general-property-units into .opencode/skills/general-property-units/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "general-property-units", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
general-property-units- GitHub stars
- 175
- Token cost
- ~1.9k tokens
- SKILL.md length
- 756 words
- Files
- 1
- Skills in repo
- 129
- Repo updated
- First seen
- Licence
- MIT
At a glance
Reference guide for energy, force, and stress units across MLIPs, DFT codes, and ASE, including conversion factors.
- SKILL.md covers Goal, Project Standard, MLIP Model Units and DFT Code Units, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
General Property Units is an agent skill from learningmatter-mit/AtomisticSkills. Reference guide for energy, force, and stress units across MLIPs, DFT codes, and ASE, including conversion factors.
Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.
Example prompts
- “/general-property-units”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 7f2d86d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
github.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
General Property Units loads about 1.9k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 756 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from learningmatter-mit/AtomisticSkills at commit 7f2d86d, republished under its MIT licence (© learningmatter-mit). 756 words, ~1,860 tokens.
Download SKILL.mdSave it as
.claude/skills/general-property-units/SKILL.md (or your agent's skills folder).- name
- general-property-units
- description
- Reference guide for energy, force, and stress units across MLIPs, DFT codes, and ASE, including conversion factors.
- metadata.category
- general, machine-learning
Units Reference for Atomistic Simulations
Goal
Provide a single authoritative reference for units of energy, forces, and stress across all MLIPs, DFT codes, and simulation tools used in this project, including the conversions applied internally.
Project Standard
All internal representations follow the ASE (Atomic Simulation Environment) convention:
| Quantity | Standard Unit | Notes |
|---|---|---|
| Energy | eV | Total energy of the system |
| Energy per atom | eV/atom | Used for MAE reporting and training labels |
| Forces | eV/Å | Negative gradient of energy w.r.t. position |
| Stress | eV/ų | Voigt notation, 6-component (xx, yy, zz, yz, xz, xy) |
MLIP Model Units
Prediction (Inference)
The raw torch model and the ASE calculator do not return the same stress units. Most calculators apply a unit conversion on the way out; two do not. Read the stress column for the layer you are actually calling.
Energy is eV and forces are eV/Å everywhere, at both layers. Only stress varies:
| Model (calculator class) | raw model output | ASE calculator output | conversion in the ASE layer |
|---|---|---|---|
MACE (MACECalculator) | eV/ų | eV/ų | none |
UMA / FairChem (FAIRChemCalculator) | eV/ų | eV/ų | none |
CHGNet standalone (CHGNetCalculator) | GPa | eV/ų | × stress_weight, default 1/160.21766208 |
CHGNet / M3GNet / TensorNet via MatGL (PESCalculator) | GPa | GPa unless asked otherwise | none by default — pass stress_unit="eV/A3" |
Measured on one compressed Si cell (xx component), 2026-08-25:
| path | raw | calculator default | calculator eV/ų |
|---|---|---|---|
| MACE-MP small | -0.0762876 | -0.0762876 | — |
UMA uma-s-1p1 (omat) | -0.0821809 | -0.0821810 | — |
| CHGNet standalone 0.4.2 | -13.906347 | -0.0867966 | — |
MatGL TensorNet-PES-MatPES-PBE-2025.2 | -10.780773 | -10.780773 | -0.067288 |
MatGL CHGNet-PES-MatPES-PBE-1M-2026.9 | — | -15.229350 | -0.095054 |
MatGL M3GNet-PES-MatPES-2025.2 | — | -20.293510 | -0.126662 |
Every ratio above is exactly 160.21766208, i.e. GPa per eV/ų.
[!IMPORTANT]
matgl.ext.ase.PESCalculatortakesstress_unit: Literal["eV/A3", "GPa"] = "GPa", so using it as a drop-in ASE calculator gives GPa, not ASE units — it prints a runtime warning saying so. CallingPotential.forwarddirectly also returns GPa. PassPESCalculator(potential=model, stress_unit="eV/A3"), or divide by160.21766208. Mixing this up is a 160x error, not a sign error.
[!NOTE] All of the above are in the ASE sign convention: positive = tensile, compression negative. A compressed cell therefore gives negative diagonal stress at both layers. DFT codes may differ — see VASP below.
Training Input Labels
Training labels in training_data.json are stored in ASE standard units (eV, eV/Å, eV/ų). Conversions to trainer-specific units are handled automatically inside each wrapper:
| Trainer | Energy Input | Force Input | Stress Input | Internal Conversion |
|---|---|---|---|---|
| MACE | eV | eV/Å | eV/ų | None — trains in eV/ų |
| FairChem (UMA) | eV | eV/Å | eV/ų | None — trains in eV/ų |
| MatGL (CHGNet/M3GNet) | eV | eV/Å | GPa (converted) | eV/ų → GPa in _prepare_training_data |
[!IMPORTANT] MatGL is the only trainer that requires stress conversion. The conversion from eV/ų → GPa is performed automatically inside
MATGLWrapper._prepare_training_data(). Users should always provide stress labels in eV/ų.
Show full SKILL.md (335 more words)Show less
Training Output (Saved Metrics)
Each MLIP trainer natively reports MAE in eV. All wrappers apply a ×1000 conversion to save MAE values in meV to training_history.json and plot axes in training_history.png, for human readability and consistent cross-model comparison:
| Trainer | Native Energy MAE | Native Force MAE | Native Stress MAE | Saved Unit |
|---|---|---|---|---|
| MACE | eV/atom | eV/Å | eV/ų | meV (×1000) |
| FairChem (UMA) | eV/atom | eV/Å | eV/ų | meV (×1000) |
| MatGL (CHGNet/M3GNet) | eV/atom | eV/Å | GPa → eV/ų | meV (×1000) |
The training_history.json keys and their units:
| Key | Unit |
|---|---|
energy_mae_train / energy_mae_val | meV/atom |
force_mae_train / force_mae_val | meV/Å |
stress_mae_train / stress_mae_val | meV/ų |
loss_train / loss_val | Dimensionless (weighted combination) |
[!NOTE] For MatGL stress: the trainer computes MAE in GPa internally. The wrapper converts back to eV/ų first, then multiplies by 1000 to get meV/ų, matching the other wrappers.
DFT Code Units
VASP
| Quantity | VASP Internal | VASP OUTCAR | Conversion to ASE Standard |
|---|---|---|---|
| Energy | eV | eV | None needed |
| Forces | eV/Å | eV/Å | None needed |
| Stress | kB (kilo-Bar) | kB (and GPa) | kB × 0.1 = GPa, then GPa × 0.0062415 = eV/ų |
[!NOTE] VASP stores stress internally in kB (kilo-Bar). The
vasprun.xmlparser in pymatgen returns stress in kB. The Atomate2 MCP tool applies the conversionkB → eV/ųautomatically whenconvert_units=True(default).
Sign Convention
VASP reports stress with the opposite sign to the physics and ASE convention:
- VASP: positive = compressive (pressure-like)
- ASE/Physics/MLIPs: positive = tensile
The sign flip is handled during VASP output parsing (e.g. in the atomate2 MCP tool, VASP stress is multiplied by -1 in addition to the unit conversion).
Common Conversion Factors
| From | To | Factor | ASE Code |
|---|---|---|---|
| GPa | eV/ų | 0.00624150913 | ase.units.GPa |
| eV/ų | GPa | 160.21766208 | 1.0 / ase.units.GPa |
| kB | GPa | 0.1 | — |
| kB | eV/ų | 0.000624150913 | 0.1 * ase.units.GPa |
| eV | kJ/mol | 96.4853 | ase.units.kJ / ase.units.mol |
| eV | kcal/mol | 23.0605 | ase.units.kcal / ase.units.mol |
| Å | Bohr | 1.8897259886 | 1.0 / ase.units.Bohr |
Quick Reference: Python Conversions
from ase import units
# Stress conversions
stress_GPa = stress_eV_per_A3 / units.GPa # eV/ų → GPa
stress_eV_per_A3 = stress_GPa * units.GPa # GPa → eV/ų
stress_eV_per_A3 = stress_kB * 0.1 * units.GPa # kB → eV/ų
# Energy conversions
energy_kJ_per_mol = energy_eV * units.kJ / units.mol
energy_kcal_per_mol = energy_eV * units.kcal / units.molConstraints
- Never mix unit systems within a single workflow.
- Always verify stress units when comparing MLIP predictions to DFT references.
- Training data JSON files must use eV/ų for stress — wrapper-internal conversion handles the rest.
- When reporting MAE in papers/docs, specify the unit explicitly (e.g., "Force MAE: 50 meV/Å").
Author: Bowen Deng Contact: GitHub @learningmatter-mit
© learningmatter-mit, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/general-property-units of learningmatter-mit/AtomisticSkills.
Open the folder on GitHubat commit 7f2d86d
Compare with similar skills
General Property Units next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| General Property Units this skilllearningmatter-mit/AtomisticSkills | 175 | — | ~1.9k | Automated safety check: Pass | MIT | |
| Unit Teststhedaviddias/Front-End-Checklist | 74k | — | ~382 | Automated safety check: Pass | MIT | |
| Warp Rust Unit Testswarpdotdev/warp | 65k | 1 repos | ~3.4k | Automated safety check: Pass | AGPL-3.0 | |
| CSS At Propertythedaviddias/Front-End-Checklist | 74k | — | ~602 | Automated safety check: Pass | MIT | |
| Writing Unit TestsTriliumNext/Trilium | 38k | — | ~3.2k | Automated safety check: Pass | AGPL-3.0 | |
| Responsive Unitsthedaviddias/Front-End-Checklist | 74k | — | ~472 | Automated safety check: Pass | MIT |
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Questions about General Property Units
What does General Property Units do?
Reference guide for energy, force, and stress units across MLIPs, DFT codes, and ASE, including conversion factors. General Property Units is an agent skill from learningmatter-mit/AtomisticSkills. Reference guide for energy, force, and stress units across MLIPs, DFT codes, and ASE, including conversion factors.
How do I install General Property Units in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a claude-code`. Or copy the skill folder (skills/general-property-units in learningmatter-mit/AtomisticSkills) into .claude/skills/general-property-units in your project. Claude Code loads it when a task matches its description.
How do I install General Property Units in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a codex`. Or copy the skill folder (skills/general-property-units in learningmatter-mit/AtomisticSkills) into .agents/skills/general-property-units in your project. Codex loads it when a task matches its description.
Can I use General Property Units in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add learningmatter-mit/AtomisticSkills --skill general-property-units -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/general-property-units, .gemini/skills/general-property-units, .github/skills/general-property-units and .opencode/skills/general-property-units in your project.
What does General Property Units need to run?
SKILL.md names no scripts, command-line tools or credentials: General Property Units is instructions for the agent only. Our summary lists: Python 3.
Does General Property Units access the network?
SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.
Is General Property Units safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does General Property Units use?
General Property Units is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does General Property Units use?
About 1.9k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to General Property Units?
Skills that share tags, products or a category with General Property Units: Unit Tests (thedaviddias/Front-End-Checklist, 74k stars), Warp Rust Unit Tests (warpdotdev/warp, 65k stars), CSS At Property (thedaviddias/Front-End-Checklist, 74k stars) and Writing Unit Tests (TriliumNext/Trilium, 38k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains General Property Units?
learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 175 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 6, 2026.
Source: learningmatter-mit/AtomisticSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.