Code Of Conduct
sickn33/agentic-awesome-skills
Build a human-reviewed conduct register after context-first intake.
Agent skill
Mat Lattice Thermal Conductivity
Calculate lattice thermal conductivity of materials with MLIPs.
MITAuto-check passed
Install Mat Lattice Thermal Conductivity
$ npx skills add learningmatter-mit/AtomisticSkills --skill mat-lattice-thermal-conductivity -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-lattice-thermal-conductivity --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/mat-lattice-thermal-conductivity .claude/skills/mat-lattice-thermal-conductivity && 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 "mat-lattice-thermal-conductivity" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-lattice-thermal-conductivity into .claude/skills/mat-lattice-thermal-conductivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-lattice-thermal-conductivity", 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/mat-lattice-thermal-conductivityType 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 mat-lattice-thermal-conductivity -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-lattice-thermal-conductivity --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/mat-lattice-thermal-conductivity .agents/skills/mat-lattice-thermal-conductivity && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mat-lattice-thermal-conductivity" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-lattice-thermal-conductivity into .agents/skills/mat-lattice-thermal-conductivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-lattice-thermal-conductivity", 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 mat-lattice-thermal-conductivity -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-lattice-thermal-conductivity --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/mat-lattice-thermal-conductivity .cursor/skills/mat-lattice-thermal-conductivity && 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 "mat-lattice-thermal-conductivity" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-lattice-thermal-conductivity into .cursor/skills/mat-lattice-thermal-conductivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-lattice-thermal-conductivity", 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/mat-lattice-thermal-conductivity--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 mat-lattice-thermal-conductivity -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills mat-lattice-thermal-conductivity --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/mat-lattice-thermal-conductivity .gemini/skills/mat-lattice-thermal-conductivity && 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 "mat-lattice-thermal-conductivity" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-lattice-thermal-conductivity into .gemini/skills/mat-lattice-thermal-conductivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-lattice-thermal-conductivity", 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 mat-lattice-thermal-conductivityInstalls 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 mat-lattice-thermal-conductivity -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/mat-lattice-thermal-conductivity .github/skills/mat-lattice-thermal-conductivity && 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 "mat-lattice-thermal-conductivity" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-lattice-thermal-conductivity into .github/skills/mat-lattice-thermal-conductivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-lattice-thermal-conductivity", 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 mat-lattice-thermal-conductivity -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 mat-lattice-thermal-conductivity --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/mat-lattice-thermal-conductivity .opencode/skills/mat-lattice-thermal-conductivity && 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 "mat-lattice-thermal-conductivity" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/mat-lattice-thermal-conductivity into .opencode/skills/mat-lattice-thermal-conductivity/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mat-lattice-thermal-conductivity", 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
mat-lattice-thermal-conductivity- GitHub stars
- 176
- Token cost
- ~969 tokens
- SKILL.md length
- 405 words
- Files
- 5 (incl. scripts)
- Skills in repo
- 129
- Repo updated
- First seen
- Licence
- MIT
At a glance
Calculate lattice thermal conductivity of materials with MLIPs.
- Works in 5 steps: Prerequisites → Choosing a Foundation Potential → Calculation Workflow → …
- SKILL.md covers 1. Prerequisites, 2. Choosing a Foundation…, 3. Calculation Workflow and 4. Output Files, plus 1 more section
- Runs Python scripts from its folder
What it does
Mat Lattice Thermal Conductivity is an agent skill from learningmatter-mit/AtomisticSkills. Calculate lattice thermal conductivity of materials with MLIPs.
Its SKILL.md is about 970 tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts (for example `examples/Si-mace/README.md`, `examples/Si-mace/thermal-conductivity/lattice_thermal_conductivity_results.json` and `examples/Si-mace/thermal-conductivity/phonon3.yaml`).
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
- “/mat-lattice-thermal-conductivity”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 6257444. 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
Ships 1 file in scripts/ (Python), which the agent can run.
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
Mat Lattice Thermal Conductivity loads about 969 tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 405 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); the scripts in this folder are not scanned.
SKILL.md
The full file from learningmatter-mit/AtomisticSkills at commit 6257444, republished under its MIT licence (© learningmatter-mit). 405 words, ~969 tokens.
Download SKILL.mdSave it as
.claude/skills/mat-lattice-thermal-conductivity/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- mat-lattice-thermal-conductivity
- description
- Calculate lattice thermal conductivity of materials with MLIPs.
- metadata.category
- materials
- metadata.venv
- mlip
Lattice Thermal Conductivity Calculation Skill
This skill provides tools for calculating lattice thermal conductivity of materials using anharmonic lattice dynamics with Machine Learning Interatomic Potentials (MLIPs).
[!WARNING] Lattice thermal conductivity only considers phonon-phonon interactions. It can be considered that lattice thermal conductivity accurately models the thermal conductivity of non-metallic materials. For metallic materials, electron-phonon interactions also need to be considered to accurately calculate thermal conductivity, which is beyond the scope of this skill.
1. Prerequisites
- The appropriate MLIP wrapper must be available (
MACEWrapper,MatGLWrapper, orFAIRCHEMWrapper). matcalc,phonopy, andphono3pyare included in themlipandfairchemenvironments.
Required Patch for phono3py ≥ 3.x
phono3py 3.x renamed ConductivityRTA.kappa_TOT_RTA to .kappa. Apply the following one-line fix in matcalc/src/matcalc/_phonon3.py:
-kappa = np.asarray(phonon3.thermal_conductivity.kappa_TOT_RTA)
+kappa = np.asarray(phonon3.thermal_conductivity.kappa)2. Choosing a Foundation Potential
Phonon and thermal conductivity calculations are highly sensitive to the quality of the potential energy surface (PES).
[!IMPORTANT]
- Use OMAT or MatPES trained models: These models (e.g.,
MACE-OMAT-0-small,TensorNet-MatPES-r2SCAN) are specifically optimized for forces and vibrational stability.- Avoid MPtrj-trained models: Models trained primarily on the
MPtrjdataset (e.g.,CHGNet-MPtrj) suffer from the "softening" problem, where the calculated phonon frequencies are significantly lower than DFT values.
Refer to the foundation-potentials skill for more details.
3. Calculation Workflow
Step One: Verify given material is an insulator / semiconductor
First of all, using the mat-electronic-structure skill to calculate the band gap of the given material or retrieve the band gap from Materials Project. If the band gap does not exist, the material is a metal, and this skill cannot give a meaningful prediction on thermal conductivity. Otherwise, the material is an insulator, and we can proceed to next step.
Show full SKILL.md (139 more words)Show less
Step Two: Calculate phonon properties
Before calculating thermal conductivity (which is related to higher order force constants), we need to calculate phonon properties which is related to second order force constants. Use the mat-phonon skill to calculate phonon properties.
Check the phonon_results.json file and phonon band structure to see if the phonon properties are reasonable, especially for imaginary frequencies in phonon band. If there are imaginary frequencies, the structure is not stable. In this case, redo the structure optimization first, and if it does not work, try different models/methods. Only after validating the phonon properties, proceed to calculate thermal conductivity.
Step Three: Calculate lattice thermal conductivity
${CLAUDE_SKILL_DIR}/../../venv/run mlip python ${CLAUDE_SKILL_DIR}/scripts/calculate_thermal_conductivity.py \
--structure Si.cif \
--model_type mace \
--model_name MACE-OMAT-0-small \
--output_dir si_mace_thermal_conductivitySee examples/README.md for detailed usage scenarios.
4. Output Files
lattice_thermal_conductivity_results.json: Summary.phonon3.yaml: Third order force constants and supercell data.
5. Examples
See examples/ for detailed usage scenarios.
Author: Bohan Li Contact: GitHub @bkhli
© 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
SKILL.md and 4 other files (scripts) in skills/mat-lattice-thermal-conductivity of learningmatter-mit/AtomisticSkills.
- SKILL.md
- examples/Si-mace/README.md
- examples/Si-mace/thermal-conductivity/lattice_thermal_conductivity_results.json
- examples/Si-mace/thermal-conductivity/phonon3.yaml
- scripts/calculate_thermal_conductivity.py
Open the folder on GitHubat commit 6257444
Compare with similar skills
Mat Lattice Thermal Conductivity 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 |
|---|---|---|---|---|---|---|
| Mat Lattice Thermal Conductivity this skilllearningmatter-mit/AtomisticSkills | 176 | — | ~969 | Automated safety check: Pass | MIT | |
| Code Of Conductsickn33/agentic-awesome-skills | 47k | 1 repos | ~6.7k | Automated safety check: Pass | MIT | |
| Investor Materialsaffaan-m/ECC | 276k | 3 repos | ~268 | Automated safety check: Pass | MIT | |
| Material Designsickn33/agentic-awesome-skills | 47k | 1 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Materialbergside/awesome-design-skills | 3.1k | 1 repos | ~919 | Automated safety check: Pass | MIT | |
| Eol Resistor Calculatorsickn33/agentic-awesome-skills | 47k | 1 repos | ~3.5k | Automated safety check: Pass | MIT |
Similar skills
Investor Materials
affaan-m/ECC
创建和更新宣传文稿、一页简介、投资者备忘录、加速器申请、财务模型和融资材料。当用户需要面向投资者的文件、预测、资金用途表、里程碑计划或必须在多个融资资产中保持内部一致性的材料时使用。
Material Design
sickn33/agentic-awesome-skills
Web and App implementation guide for Material Design. An agent skill from sickn33/agentic-awesome-skills.
Material
bergside/awesome-design-skills
Google's Material Design with layered surfaces, dynamic theming, built-in motion, and responsive cross-platform patterns.
Eol Resistor Calculator
sickn33/agentic-awesome-skills
Calculates and validates end-of-line (EOL, SEOL, DEOL, TEOL) resistor loops for intrusion alarm panels (Honeywell, DSC, Paradox, Bosch) with wire gauge drop and state tables.
Material Density Volume Calculation
InternScience/scp
Calculate material density and volume from mass and geometric dimensions for materials mechanics analysis.
More from learningmatter-mit/AtomisticSkills
All 129 skills in this repoDrug Binding Site Definition
learningmatter-mit/AtomisticSkills
Define a docking search box (center coordinates + box dimensions in Angstroms) from a co-crystal ligand, binding-site residues, or a saved JSON specification.
Drug Complex System Builder
learningmatter-mit/AtomisticSkills
Build a solvated, charge-neutralized protein-ligand complex for OpenMM molecular dynamics simulation.
Drug Pocket Detection
learningmatter-mit/AtomisticSkills
Identify and rank ligandable pockets on a protein structure or model using geometry (fpocket) or an ML predictor (P2Rank).
Chem Bond Dissociation
learningmatter-mit/AtomisticSkills
Calculate homolytic and heterolytic bond dissociation energies (BDEs) for all single bonds in a molecule using MLIPs with RDKit fragmentation.
Chem Conformer Search
learningmatter-mit/AtomisticSkills
Generate molecular conformers with RDKit ETKDG, relax with MLIPs, and rank by energy with Boltzmann weighting.
Chem DB Mof
learningmatter-mit/AtomisticSkills
Query multiple MOF databases (QMOF via MPContribs; ARC-MOF DB7/Majumdar et al.
Questions about Mat Lattice Thermal Conductivity
What does Mat Lattice Thermal Conductivity do?
Calculate lattice thermal conductivity of materials with MLIPs. Mat Lattice Thermal Conductivity is an agent skill from learningmatter-mit/AtomisticSkills. Calculate lattice thermal conductivity of materials with MLIPs.
How do I install Mat Lattice Thermal Conductivity in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-lattice-thermal-conductivity -a claude-code`. Or copy the skill folder (skills/mat-lattice-thermal-conductivity in learningmatter-mit/AtomisticSkills) into .claude/skills/mat-lattice-thermal-conductivity in your project. Claude Code loads it when a task matches its description.
How do I install Mat Lattice Thermal Conductivity in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill mat-lattice-thermal-conductivity -a codex`. Or copy the skill folder (skills/mat-lattice-thermal-conductivity in learningmatter-mit/AtomisticSkills) into .agents/skills/mat-lattice-thermal-conductivity in your project. Codex loads it when a task matches its description.
Can I use Mat Lattice Thermal Conductivity 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 mat-lattice-thermal-conductivity -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mat-lattice-thermal-conductivity, .gemini/skills/mat-lattice-thermal-conductivity, .github/skills/mat-lattice-thermal-conductivity and .opencode/skills/mat-lattice-thermal-conductivity in your project.
What does Mat Lattice Thermal Conductivity need to run?
Going by SKILL.md and its folder, Mat Lattice Thermal Conductivity needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Mat Lattice Thermal Conductivity 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 Mat Lattice Thermal Conductivity 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Mat Lattice Thermal Conductivity use?
Mat Lattice Thermal Conductivity 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 Mat Lattice Thermal Conductivity use?
About 969 tokens (SKILL.md is roughly 3.9k 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 Mat Lattice Thermal Conductivity?
Skills that share tags, products or a category with Mat Lattice Thermal Conductivity: Code Of Conduct (sickn33/agentic-awesome-skills, 47k stars), Investor Materials (affaan-m/ECC, 276k stars), Material Design (sickn33/agentic-awesome-skills, 47k stars) and Material (bergside/awesome-design-skills, 3.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mat Lattice Thermal Conductivity?
learningmatter-mit (a GitHub organization) maintains it in learningmatter-mit/AtomisticSkills, which has 176 GitHub stars. The repository holds 129 skills in this directory. The repository was last updated on October 7, 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.