Astropy
zLanqing/codex-claude-academic-skills
Comprehensive Python library for astronomy and astrophysics.
Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.
Install ML Generative Diffcsp
$ npx skills add learningmatter-mit/AtomisticSkills --skill ml-generative-diffcsp -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-generative-diffcsp --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/ml-generative-diffcsp .claude/skills/ml-generative-diffcsp && 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 "ml-generative-diffcsp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-generative-diffcsp into .claude/skills/ml-generative-diffcsp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-generative-diffcsp", 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/ml-generative-diffcspType 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 ml-generative-diffcsp -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-generative-diffcsp --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/ml-generative-diffcsp .agents/skills/ml-generative-diffcsp && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ml-generative-diffcsp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-generative-diffcsp into .agents/skills/ml-generative-diffcsp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-generative-diffcsp", 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 ml-generative-diffcsp -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-generative-diffcsp --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/ml-generative-diffcsp .cursor/skills/ml-generative-diffcsp && 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 "ml-generative-diffcsp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-generative-diffcsp into .cursor/skills/ml-generative-diffcsp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-generative-diffcsp", 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/ml-generative-diffcsp--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 ml-generative-diffcsp -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install learningmatter-mit/AtomisticSkills ml-generative-diffcsp --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/ml-generative-diffcsp .gemini/skills/ml-generative-diffcsp && 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 "ml-generative-diffcsp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-generative-diffcsp into .gemini/skills/ml-generative-diffcsp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-generative-diffcsp", 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 ml-generative-diffcspInstalls 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 ml-generative-diffcsp -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/ml-generative-diffcsp .github/skills/ml-generative-diffcsp && 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 "ml-generative-diffcsp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-generative-diffcsp into .github/skills/ml-generative-diffcsp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-generative-diffcsp", 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 ml-generative-diffcsp -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 ml-generative-diffcsp --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/ml-generative-diffcsp .opencode/skills/ml-generative-diffcsp && 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 "ml-generative-diffcsp" agent skill from https://github.com/learningmatter-mit/AtomisticSkills/tree/main/skills/ml-generative-diffcsp into .opencode/skills/ml-generative-diffcsp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ml-generative-diffcsp", 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
ml-generative-diffcsp- GitHub stars
- 175
- Token cost
- ~1.4k tokens
- SKILL.md length
- 461 words
- Files
- 21 (incl. scripts)
- Skills in repo
- 129
- Repo updated
- First seen
- Licence
- MIT
At a glance
Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.
- Works in 7 steps: Prerequisites → Available Models → Usage Modes → …
- Tasks that involve Physical and earth sciences
- SKILL.md covers Goal, 1. Prerequisites, 2. Available Models and 3. Usage Modes, plus 4 more sections
- Calls git; reaches github.com
What it does
ML Generative Diffcsp is an agent skill from learningmatter-mit/AtomisticSkills. Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 24 other files, including scripts (for example `examples/Li2ZrCl6-symmetry/README.md`, `examples/Li2ZrCl6-symmetry/generation_metadata.json` and `examples/batch-json/README.md`).
It sits in Research & Science, covering Physical and earth sciences. The repository describes itself as: Integrating AtomisticSkills into Agentic IDEs (Cursor, Claude Code, Codex, Google Antigravity, Hermes Agent, etc). The licence is MIT.
When your agent uses it
- Tasks that involve Physical and earth sciences
Example prompts
- “/ml-generative-diffcsp”
Requirements
- Python 3
Workflow steps
7 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 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
Ships 1 file in scripts/, which the agent can run.
Shell commands in SKILL.md call:
git
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
github.com
Also links to:
drive.google.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
ML Generative Diffcsp loads about 1.4k tokens when it runs. Until then it costs about 45 tokens; SKILL.md has 461 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 7f2d86d, republished under its MIT licence (© learningmatter-mit). 461 words, ~1,432 tokens.
Download SKILL.mdSave it as
.claude/skills/ml-generative-diffcsp/SKILL.md (or your agent's skills folder). This skill also uses 20 other files; get the full folder from GitHub.- name
- ml-generative-diffcsp
- description
- Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.
- metadata.category
- machine-learning, materials
- metadata.venv
- diffcsp
DiffCSP++ Crystal Structure Generation
Goal
Generate novel crystal structures using DiffCSP++ (ICLR 2024), a diffusion model that leverages space group symmetry constraints for crystal structure prediction (CSP) and ab initio generation.
1. Prerequisites
[!IMPORTANT] GPU Required: DiffCSP++ inference is significantly faster on GPU.
- Runs as the
diffcspMCP server and its scripts run in thediffcspenvironment: on x86_64 a uv environment created on first use (CUDA 12.6 or 13 by driver), on aarch64 thegenerativecontainer image. - DiffCSP++ repo (
git clone https://github.com/jiaor17/DiffCSP-PP) cloned next to this project as../DiffCSP-PP, or anywhere withDIFFCSP_REPOpointing to it.venv/runmounts it into the container on aarch64. - Pre-trained checkpoints in the repository's
checkpoints/directory (e.g.checkpoints/mp_csp/). The DiffCSP-PP README links them on Google Drive; Google Drive needs an interactive download, so fetch them by hand (or withgdown --folder).
2. Available Models
| Model | Type | Description |
|---|---|---|
mp_csp | CSP | Materials Project — composition-constrained generation |
mp_gen | Gen | Materials Project — unconditional generation |
perov_csp | CSP | Perovskite — composition-constrained generation |
perov_gen | Gen | Perovskite — unconditional generation |
carbon_gen | Gen | Carbon — unconditional generation |
mpts_csp | CSP | MPTS-52 — composition-constrained generation |
3. Usage Modes
Mode 1: Single Composition via MCP Tool (Recommended)
Generate structures with exact composition using the generate_structures_with_symmetry MCP tool:
diffcsp.generate_structures_with_symmetry(
spacegroup=58, # Space group number (1-230)
wyckoff_letters="2a,2d,4g", # Wyckoff positions (comma-separated or shorthand "adg")
atom_types="Mn,Li,O", # Element per Wyckoff position
model_name="mp_csp", # CSP model
num_samples=5, # Number of structures to generate
step_lr=1e-5, # Langevin step size
output_dir="research/my_project"
)Mode 2: Batch Generation from JSON File
Generate multiple structures from a JSON specification file. This is useful when you have many different compositions to generate at once.
JSON format (see examples/example.json):
[
{"spacegroup_number": 58, "wyckoff_letters": ["2a","2d","4g"], "atom_types": ["Mn","Li","O"]},
{"spacegroup_number": 194, "wyckoff_letters": "abff", "atom_types": ["Tm","Tm","Ni","As"]}
]Run the batch generation script:
${CLAUDE_SKILL_DIR}/../../venv/run diffcsp python ${CLAUDE_SKILL_DIR}/scripts/batch_generate.py \
--json_file ${CLAUDE_SKILL_DIR}/examples/example.json \
--model mp_csp \
--output_dir diffcsp_batch_output \
--step_lr 1e-5Mode 3: Ab Initio (Unconditional) Generation
Generate structures from the training distribution without specifying composition. Requires a generation model (mp_gen, perov_gen, or carbon_gen).
${CLAUDE_SKILL_DIR}/../../venv/run diffcsp python ${CLAUDE_SKILL_DIR}/scripts/unconditional_generate.py \
--model mp_gen \
--num_structures 100 \
--output_dir diffcsp_gen_output \
--step_lr 5e-6Show full SKILL.md (205 more words)Show less
4. Parameters
| Parameter | Default | Description |
|---|---|---|
spacegroup | — | Space group number (1-230) |
wyckoff_letters | — | Wyckoff positions (e.g., "2a,2d,4g" or shorthand "adg") |
atom_types | — | Element for each Wyckoff position (e.g., "Mn,Li,O") |
model_name | mp_csp | Pre-trained model name |
num_samples | 1 | Number of structures per composition |
step_lr | 1e-5 | Langevin dynamics step size |
batch_size | 128 | Batch size for parallel generation |
5. Output Files
structure_XXXX.cif: Generated crystal structure files (pymatgen CIF format)generation_metadata.json: Generation parameters and statistics
6. Constraints
[!WARNING] Space Group Knowledge Required: You need to know the space group number and Wyckoff positions for your target composition. Use ICSD, Materials Project, or pyxtal to find these.
[!NOTE] Wyckoff Notation: Positions can be given as full labels (
"2a,2d,4g") or shorthand letters ("adg"). The number prefix is the site multiplicity — it's automatically determined from the space group.
- Environment:
diffcsp(uv on x86_64; thegenerativeimage on aarch64). - GPU: A CUDA GPU is recommended for reasonable generation speed.
- CSP vs Gen models: CSP models require
atom_types; Gen models can generate without them.
7. Workflow Integration
DiffCSP++ works well in combination with:
- Structure relaxation: Use MLIP tools (MACE, FairChem, MatGL) to optimize generated structures
- Stability analysis: Use mat-stability to calculate E_hull
- Comparison: Generate structures with DiffCSP++, ADiT, and MatterGen for diversity
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
SKILL.md and 20 other files (scripts) in skills/ml-generative-diffcsp of learningmatter-mit/AtomisticSkills.
- SKILL.md
- examples/Li2ZrCl6-symmetry/README.md
- examples/Li2ZrCl6-symmetry/generation_metadata.json
- examples/Li2ZrCl6-symmetry/structure_0000.cif
- examples/Li2ZrCl6-symmetry/structure_0001.cif
- examples/Li2ZrCl6-symmetry/structure_0002.cif
- examples/batch-json/README.md
- examples/batch-json/generation_metadata.json
- examples/batch-json/structure_0000.cif
- examples/batch-json/structure_0001.cif
- examples/example.json
- examples/unconditional/README.md
- examples/unconditional/generation_metadata.json
- examples/unconditional/structure_0000.cif
- examples/unconditional/structure_0001.cif
- examples/unconditional/structure_0002.cif
- examples/unconditional/structure_0003.cif
- … and 4 more
Open the folder on GitHubat commit 7f2d86d
Compare with similar skills
ML Generative Diffcsp 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 |
|---|---|---|---|---|---|---|
| ML Generative Diffcsp this skilllearningmatter-mit/AtomisticSkills | 175 | — | ~1.4k | Automated safety check: Pass | MIT | |
| AstropyzLanqing/codex-claude-academic-skills | 4.6k | 14 repos | ~2.9k | Automated safety check: Pass | BSD-3-Clause | |
| PymatgenzLanqing/codex-claude-academic-skills | 4.6k | 12 repos | ~5k | Automated safety check: Pass | MIT | |
| Cantera Ignition DelayK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~2.2k | Automated safety check: Pass | MIT | |
| Weathertrpc-group/trpc-agent-go | 1.8k | 8 repos | ~591 | Automated safety check: Pass | Apache-2.0 | |
| Pymol VisualizationChatMol/ChatMol | 372 | — | ~1.2k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about ML Generative Diffcsp
What does ML Generative Diffcsp do?
Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions. ML Generative Diffcsp is an agent skill from learningmatter-mit/AtomisticSkills. Generate crystal structures with exact composition control using DiffCSP++ (space group + Wyckoff positions), or unconditionally from trained distributions.
When should I use ML Generative Diffcsp?
ML Generative Diffcsp fits situations like: tasks that involve Physical and earth sciences.
How do I install ML Generative Diffcsp in Claude Code?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill ml-generative-diffcsp -a claude-code`. Or copy the skill folder (skills/ml-generative-diffcsp in learningmatter-mit/AtomisticSkills) into .claude/skills/ml-generative-diffcsp in your project. Claude Code loads it when a task matches its description.
How do I install ML Generative Diffcsp in Codex?
Run `npx skills add learningmatter-mit/AtomisticSkills --skill ml-generative-diffcsp -a codex`. Or copy the skill folder (skills/ml-generative-diffcsp in learningmatter-mit/AtomisticSkills) into .agents/skills/ml-generative-diffcsp in your project. Codex loads it when a task matches its description.
Can I use ML Generative Diffcsp 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 ml-generative-diffcsp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ml-generative-diffcsp, .gemini/skills/ml-generative-diffcsp, .github/skills/ml-generative-diffcsp and .opencode/skills/ml-generative-diffcsp in your project.
What does ML Generative Diffcsp need to run?
Going by SKILL.md and its folder, ML Generative Diffcsp needs the command-line tools its instructions call (git). Our summary lists: Python 3.
Does ML Generative Diffcsp access the network?
SKILL.md names 2 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: drive.google.com. This is read from the text; nothing was executed.
Is ML Generative Diffcsp 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 ML Generative Diffcsp use?
ML Generative Diffcsp 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 ML Generative Diffcsp use?
About 1.4k tokens (SKILL.md is roughly 5.7k 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 ML Generative Diffcsp?
Skills that share tags, products or a category with ML Generative Diffcsp: 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 ML Generative Diffcsp?
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.