materials-database-querier
Materials database query skill for accessing structure and property data from multiple repositories
Best use case
materials-database-querier is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Materials database query skill for accessing structure and property data from multiple repositories
Teams using materials-database-querier should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/materials-database-querier/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How materials-database-querier Compares
| Feature / Agent | materials-database-querier | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Materials database query skill for accessing structure and property data from multiple repositories
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Materials Database Querier
## Purpose
The Materials Database Querier skill provides unified access to multiple materials databases for structure and property retrieval, enabling comprehensive materials search and data aggregation across repositories.
## Capabilities
- Materials Project API integration
- AFLOW database queries
- ICSD/CSD structure retrieval
- NOMAD repository access
- Cross-database searches
- Property aggregation and comparison
## Usage Guidelines
### Database Query Workflow
1. **Query Design**
- Define search criteria
- Select target databases
- Set property filters
2. **Data Retrieval**
- Execute queries
- Handle pagination
- Aggregate results
3. **Data Processing**
- Standardize formats
- Compare across sources
- Export for analysis
## Process Integration
- Machine Learning Materials Discovery Pipeline
- DFT Calculation Pipeline for Nanomaterials
- Structure-Property Correlation Analysis
## Input Schema
```json
{
"query_type": "composition|structure|property",
"databases": ["materials_project", "aflow", "icsd"],
"criteria": {
"elements": ["string"],
"property_range": {"property": "string", "min": "number", "max": "number"}
},
"limit": "number"
}
```
## Output Schema
```json
{
"materials": [{
"id": "string",
"formula": "string",
"structure_file": "string",
"properties": {
"bandgap": "number",
"formation_energy": "number"
},
"source": "string"
}],
"total_found": "number",
"query_metadata": {
"databases_searched": ["string"],
"query_time": "number"
}
}
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