Pymatgen is a comprehensive Python library for materials analysis that powers the Materials Project. Create, analyze, and manipulate crystal structures and molecules, compute phase diagrams and thermodynamic properties, analyze electronic structure (band structures, DOS), generate surfaces and interfaces, and access Materials Project's database of computed materials. Supports 100+ file formats from various computational codes.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpymatgenExecute the skills CLI command in your project's root directory to begin installation:
Fetches pymatgen from davila7/claude-code-templates and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate pymatgen. Access via /pymatgen in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Pymatgen is a comprehensive Python library for materials analysis that powers the Materials Project. Create, analyze, and manipulate crystal structures and molecules, compute phase diagrams and thermodynamic properties, analyze electronic structure (band structures, DOS), generate surfaces and interfaces, and access Materials Project's database of computed materials. Supports 100+ file formats from various computational codes.
This skill should be used when:
# Core pymatgen
uv pip install pymatgen
# With Materials Project API access
uv pip install pymatgen mp-api
# Optional dependencies for extended functionality
uv pip install pymatgen[analysis] # Additional analysis tools
uv pip install pymatgen[vis] # Visualization tools
from pymatgen.core import Structure, Lattice
# Read structure from file (automatic format detection)
struct = Structure.from_file("POSCAR")
# Create structure from scratch
lattice = Lattice.cubic(3.84)
struct = Structure(lattice, ["Si", "Si"], [[0,0,0], [0.25,0.25,0.25]])
# Write to different format
struct.to(filename="structure.cif")
# Basic properties
print(f"Formula: {struct.composition.reduced_formula}")
print(f"Space group: {struct.get_space_group_info()}")
print(f"Density: {struct.density:.2f} g/cm³")
# Set up API key
export MP_API_KEY="your_api_key_here"
from mp_api.client import MPRester
with MPRester() as mpr:
# Get structure by material ID
struct = mpr.get_structure_by_material_id("mp-149")
# Search for materials
materials = mpr.materials.summary.search(
formula="Fe2O3",
energy_above_hull=(0, 0.05)
)
Create structures using various methods and perform transformations.
From files:
# Automatic format detection
struct = Structure.from_file("structure.cif")
struct = Structure.from_file("POSCAR")
mol = Molecule.from_file("molecule.xyz")
From scratch:
from pymatgen.core import Structure, Lattice
# Using lattice parameters
lattice = Lattice.from_parameters(a=3.84, b=3.84, c=3.84,
alpha=120, beta=90, gamma=60)
coords = [[0, 0, 0], [0.75, 0.5, 0.75]]
struct = Structure(lattice, ["Si", "Si"], coords)
# From space group
struct = Structure.from_spacegroup(
"Fm-3m",
Lattice.cubic(3.5),
["Si"],
[[0, 0, 0]]
)
Transformations:
from pymatgen.transformations.standard_transformations import (
SupercellTransformation,
SubstitutionTransformation,
PrimitiveCellTransformation
)
# Create supercell
trans = SupercellTransformation([[2,0,0],[0,2,0],[0,0,2]])
supercell = trans.apply_transformation(struct)
# Substitute elements
trans = SubstitutionTransformation({"Fe": "Mn"})
new_struct = trans.apply_transformation(struct)
# Get primitive cell
trans = PrimitiveCellTransformation()
primitive = trans.apply_transformation(struct)
Reference: See references/core_classes.md for comprehensive documentation of Structure, Lattice, Molecule, and related classes.
Convert between 100+ file formats with automatic format detection.
Using convenience methods:
# Read any format
struct = Structure.from_file("input_file")
# Write to any format
struct.to(filename="output.cif")
struct.to(filename="POSCAR")
struct.to(filename="output.xyz")
Using the conversion script:
# Single file conversion
python scripts/structure_converter.py POSCAR structure.cif
# Batch conversion
python scripts/structure_converter.py *.cif --output-dir ./poscar_files --format poscar
Reference: See references/io_formats.md for detailed documentation of all supported formats and code integrations.
Analyze structures for symmetry, coordination, and other properties.
Symmetry analysis:
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
sga = SpacegroupAnalyzer(struct)
# Get space group information
print(f"Space group: {sga.get_space_group_symbol()}")
print(f"Number: {sga.get_space_group_number()}")
print(f"Crystal system: {sga.get_crystal_system()}")
# Get conventional/primitive cells
conventional = sga.get_conventional_standard_structure()
primitive = sga.get_primitive_standard_structure()
Coordination environment:
from pymatgen.analysis.local_env import CrystalNN
cnn = CrystalNN()
neighbors = cnn.get_nn_info(struct, n=0)Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
davila7/claude-code-templates
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pymatgen reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: pymatgen is focused, and the summary matches what you get after install.
pymatgen is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
pymatgen has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: pymatgen is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added pymatgen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
pymatgen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added pymatgen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: pymatgen is the kind of skill you can hand to a new teammate without a long onboarding doc.
Useful defaults in pymatgen — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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