pymatgen▌
davila7/claude-code-templates · updated Apr 8, 2026
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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.
Pymatgen - Python Materials Genomics
Overview
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.
When to Use This Skill
This skill should be used when:
- Working with crystal structures or molecular systems in materials science
- Converting between structure file formats (CIF, POSCAR, XYZ, etc.)
- Analyzing symmetry, space groups, or coordination environments
- Computing phase diagrams or assessing thermodynamic stability
- Analyzing electronic structure data (band gaps, DOS, band structures)
- Generating surfaces, slabs, or studying interfaces
- Accessing the Materials Project database programmatically
- Setting up high-throughput computational workflows
- Analyzing diffusion, magnetism, or mechanical properties
- Working with VASP, Gaussian, Quantum ESPRESSO, or other computational codes
Quick Start Guide
Installation
# 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
Basic Structure Operations
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")
Materials Project Integration
# 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)
)
Core Capabilities
1. Structure Creation and Manipulation
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.
2. File Format Conversion
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.
3. Structure Analysis and Symmetry
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)How to use pymatgen on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add pymatgen
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches pymatgen from GitHub repository davila7/claude-code-templates and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate pymatgen. Access the skill through slash commands (e.g., /pymatgen) or your agent's skill management interface.
Security & Verification Notice
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.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
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
Competitive Analysis
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
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ 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.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★42 reviews- ★★★★★Zara Wang· Dec 20, 2024
pymatgen reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kofi Desai· Dec 8, 2024
Solid pick for teams standardizing on skills: pymatgen is focused, and the summary matches what you get after install.
- ★★★★★Sakura Rahman· Dec 4, 2024
pymatgen is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zara Gonzalez· Nov 27, 2024
pymatgen has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Zara Liu· Nov 23, 2024
Keeps context tight: pymatgen is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Amina Gonzalez· Nov 11, 2024
We added pymatgen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Zara Torres· Oct 18, 2024
pymatgen fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Tariq Dixit· Oct 14, 2024
We added pymatgen from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Hiroshi Kapoor· Oct 2, 2024
Keeps context tight: pymatgen is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Isabella Anderson· Sep 25, 2024
Useful defaults in pymatgen — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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