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Agent skill

Defect Generation

by Hello-QM in Hello-QM/catgo-LRG

A skill your agent uses when the user asks to create point defects such as vacancies, substitutional defects, or interstitial atoms in a crystal structure.

AGPL-3.0Auto-check passedResearch & Science

Install Defect Generation

skills CLI
$ npx skills add Hello-QM/catgo-LRG --skill defect-generation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Hello-QM/catgo-LRG defect-generation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Hello-QM/catgo-LRG.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/structure-defect .claude/skills/defect-generation && rm -rf skills-src

Use ~/.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/

Facts

Skill name
defect-generation
GitHub stars
205
Token cost
~1.2k tokens
SKILL.md length
396 words
Files
1
Skills in repo
75
Repo updated
First seen
Licence
AGPL-3.0

At a glance

A skill your agent uses when the user asks to create point defects such as vacancies, substitutional defects, or interstitial atoms in a crystal structure.

  • Works in 5 steps: Fetch and prepare structure → Identify target atom → Create vacancy → …
  • The user asks to create point defects such as vacancies
  • SKILL.md covers Overview, MCP Tool: catgo_structure (via…, Parameters and Complete Workflow: Vacancy…, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Defect Generation is an agent skill from Hello-QM/catgo-LRG. Use when the user asks to create point defects such as vacancies, substitutional defects, or interstitial atoms in a crystal structure.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Research & Science, covering Physical and earth sciences. The repository describes itself as: AI-driven workbench for computational materials science — interactive 3D structure viewer, natural-language CatBot assistant, visual DAG workflow engine, HPC job submission… The licence is AGPL-3.0.

When your agent uses it

  • The user asks to create point defects such as vacancies
  • Substitutional defects
  • Interstitial atoms in a crystal structure

Example prompts

  • “/defect-generation”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Fetch and prepare structure
  2. Identify target atom
  3. Create vacancy
  4. Set up DFT workflow
  5. Compute vacancy formation energy

What it can do on your machine

Read from SKILL.md and the folder at commit fd6291b. 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 json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    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

Defect Generation loads about 1.2k tokens when it runs. Until then it costs about 38 tokens; SKILL.md has 396 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~38
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

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 Hello-QM/catgo-LRG at commit fd6291b, republished under its AGPL-3.0 licence (© Hello-QM). 396 words, ~1,233 tokens.

Download SKILL.mdSave it as .claude/skills/defect-generation/SKILL.md (or your agent's skills folder).
name
defect-generation
description
Use when the user asks to create point defects such as vacancies, substitutional defects, or interstitial atoms in a crystal structure.
tags
structure, defect, vacancy, substitution, interstitial

Defect Generation

Overview

Point defect generation creates vacancy, substitution, or interstitial defects in periodic structures. This is essential for studying:

  • Vacancy formation energies: Removing atoms to find stable vacancy sites
  • Substitutional defects: Replacing host atoms (e.g., N replacing O in TiO2)
  • Interstitial defects: Inserting atoms in interstitial positions
  • Defect-mediated catalysis: Active sites at vacancy or dopant locations

The tool optionally builds a supercell before creating the defect to minimize periodic image interactions.

MCP Tool: catgo_structure (via REST /build/defect)

Defect generation is available through the /build/defect endpoint. In the full MCP server, use the catgo_build_defect tool. The structure is automatically fetched from the viewer.

Create a Vacancy

Remove an atom at a specific site index:

json
{"tool": "catgo_structure", "arguments": {
  "action": "delete",
  "indices": [5]
}}

For a workflow-integrated vacancy with supercell expansion, use the REST endpoint directly:

json
POST /build/defect
{
  "structure": { ... },
  "defect_type": "vacancy",
  "site_index": 5,
  "supercell": "2x2x2"
}
Create All Symmetry-Unique Vacancies

Set site_index to -1 to generate one vacancy structure per symmetry-unique site. This is useful for screening which vacancy site is most stable:

json
POST /build/defect
{
  "structure": { ... },
  "defect_type": "vacancy",
  "site_index": -1,
  "supercell": "2x2x2"
}

Returns multiple structures, each with a different symmetry-unique atom removed.

Create a Substitutional Defect

Replace one atom with a different element:

json
POST /build/defect
{
  "structure": { ... },
  "defect_type": "substitution",
  "site_index": 3,
  "substitute_element": "Fe",
  "supercell": "2x2x2"
}

Or use the viewer-based approach:

json
{"tool": "catgo_structure", "arguments": {
  "action": "replace",
  "index": 3,
  "new_element": "Fe"
}}
Create an Interstitial Defect

Insert an atom near a reference site. The interstitial is placed at the midpoint between the reference site and its nearest neighbor:

json
POST /build/defect
{
  "structure": { ... },
  "defect_type": "interstitial",
  "site_index": 0,
  "substitute_element": "Li",
  "supercell": "2x2x2"
}

Parameters

ParameterTypeDefaultDescription
defect_typestring"vacancy"Type: vacancy, substitution, interstitial
site_indexint0Atom index to act on (-1 for all unique vacancies)
substitute_elementstring""Element for substitution/interstitial
supercellstring"2x2x2"Supercell scaling before defect creation
structuredict--Structure in pymatgen dict format
Show full SKILL.md (146 more words)Show less

Complete Workflow: Vacancy Formation Energy

1. Fetch and prepare structure
json
{"tool": "catgo_fetch", "arguments": {
  "action": "crystal", "formula": "TiO2", "provider": "mp"
}}
json
{"tool": "catgo_structure", "arguments": {
  "action": "supercell", "scaling": [2, 2, 2]
}}
2. Identify target atom
json
{"tool": "catgo_view", "arguments": {"action": "get_state"}}

Find an O atom (e.g., index 12) to create an oxygen vacancy.

3. Create vacancy
json
{"tool": "catgo_structure", "arguments": {
  "action": "delete", "indices": [12]
}}
4. Set up DFT workflow
json
{"tool": "catgo_workflow", "arguments": {
  "action": "create", "name": "O vacancy in TiO2"
}}
json
{"tool": "catgo_workflow", "arguments": {
  "action": "add_node", "workflow_id": "wf_vac",
  "node_type": "geo_opt",
  "params": {"software": "vasp", "ENCUT": 520, "ISPIN": 2,
             "system_name": "TiO2 O-vacancy"}
}}
5. Compute vacancy formation energy
E_f(V_O) = E(TiO2 - O) - E(TiO2_perfect) + 0.5 * E(O2)

Run the same geo_opt for the perfect supercell and gas-phase O2 as references.

Common Pitfalls

  1. Always use a supercell large enough (at least 2x2x2 for bulk, 3x3x1 for surfaces) to minimize defect-defect interactions across periodic boundaries.
  2. Vacancies in transition-metal oxides often require spin polarization (ISPIN=2) and DFT+U corrections for accurate formation energies.
  3. After creating a defect, always relax the structure with geo_opt. The atoms neighboring the defect will move significantly.
  4. For charged defects (e.g., V_O^{2+} in TiO2), additional corrections (Freysoldt, Kumagai) are needed for finite-size effects.
  5. The site_index uses 0-based indexing. Use catgo_view to verify which atom you are removing before proceeding.

© Hello-QM, AGPL-3.0. 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 .claude/skills/structure-defect of Hello-QM/catgo-LRG.

Open the folder on GitHubat commit fd6291b

Compare with similar skills

Defect Generation 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.

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Categories

Questions about Defect Generation

What does Defect Generation do?

A skill your agent uses when the user asks to create point defects such as vacancies, substitutional defects, or interstitial atoms in a crystal structure. Defect Generation is an agent skill from Hello-QM/catgo-LRG. Use when the user asks to create point defects such as vacancies, substitutional defects, or interstitial atoms in a crystal structure.

When should I use Defect Generation?

Defect Generation fits situations like: the user asks to create point defects such as vacancies; substitutional defects; interstitial atoms in a crystal structure.

How do I install Defect Generation in Claude Code?

Run `npx skills add Hello-QM/catgo-LRG --skill defect-generation -a claude-code`. Or copy the skill folder (.claude/skills/structure-defect in Hello-QM/catgo-LRG) into .claude/skills/defect-generation in your project. Claude Code loads it when a task matches its description.

How do I install Defect Generation in Codex?

Run `npx skills add Hello-QM/catgo-LRG --skill defect-generation -a codex`. Or copy the skill folder (.claude/skills/structure-defect in Hello-QM/catgo-LRG) into .agents/skills/defect-generation in your project. Codex loads it when a task matches its description.

Can I use Defect Generation 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 Hello-QM/catgo-LRG --skill defect-generation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/defect-generation, .gemini/skills/defect-generation, .github/skills/defect-generation and .opencode/skills/defect-generation in your project.

What does Defect Generation need to run?

SKILL.md names no scripts, command-line tools or credentials: Defect Generation is instructions for the agent only.

Does Defect Generation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Defect Generation 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 Defect Generation use?

Defect Generation is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Defect Generation use?

About 1.2k tokens (SKILL.md is roughly 4.9k 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 Defect Generation?

Skills that share tags, products or a category with Defect Generation: Astropy (zLanqing/codex-claude-academic-skills, 4.6k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.6k stars), Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars) and Weather (trpc-group/trpc-agent-go, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Defect Generation?

Hello-QM (a GitHub user) maintains it in Hello-QM/catgo-LRG, which has 205 GitHub stars. The repository holds 75 skills in this directory. The repository was last updated on September 22, 2026.

Source: Hello-QM/catgo-LRG on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.