Acerca de
Esta habilidad mapea terminología de ciencia de materiales y descripciones de estructuras cristalinas a clases estandarizadas de ontologías, soportando múltiples ontologías como CMSO y ASMO. Traduce términos en lenguaje natural (por ejemplo, "hierro CCC") a entradas formales de ontología con puntuaciones de confianza y genera metadatos legibles por máquinas. Los desarrolladores deben usarla al anotar entradas de simulación para cumplimiento FAIR o para conectar vocabulario informal de laboratorio con términos formales de ontologías.
Instalación rápida
Claude Code
Recomendadonpx skills add HeshamFS/materials-simulation-skills -a claude-code/plugin add https://github.com/HeshamFS/materials-simulation-skillsgit clone https://github.com/HeshamFS/materials-simulation-skills.git ~/.claude/skills/ontology-mapperCopia y pega este comando en Claude Code para instalar esta habilidad
Documentación
Ontology Mapper
Goal
Translate real-world materials science descriptions into standardized ontology annotations. Given terms like "FCC copper" or structured data like {"material": "iron", "structure": "BCC", "lattice_a": 2.87}, produce the corresponding ontology classes and properties for any registered ontology.
Requirements
- Python 3.8+
- No external dependencies (Python standard library only)
- Requires ontology-explorer's summary JSON and
ontology_registry.json - Per-ontology mapping config (
<name>_mappings.json) for ontology-specific synonyms and labels
Inputs to Gather
| Input | Description | Example |
|---|---|---|
| Ontology | Ontology name from registry | cmso, asmo |
| Term(s) | Natural-language materials concept(s) | "unit cell", "FCC,copper,lattice" |
| Crystal system | One of the 7 crystal systems | cubic, hexagonal |
| Bravais lattice | Lattice type (symbol or common name) | FCC, cF, BCC |
| Space group | Space group number (1-230) | 225 |
| Lattice parameters | a, b, c in angstroms; alpha, beta, gamma in degrees | a=3.615 |
| Sample description | JSON dict with material properties | {"material":"copper","structure":"FCC"} |
Decision Guidance
What do you need to map?
├── A concept or term to find its ontology class
│ └── concept_mapper.py --ontology <name> --term "<term>"
├── Crystal structure parameters to ontology terms
│ └── crystal_mapper.py --ontology <name> --bravais <type> --space-group <N> --a <val>
├── A full sample description to ontology annotations
│ └── sample_annotator.py --ontology <name> --sample '<json>'
└── Multiple terms at once
└── concept_mapper.py --ontology <name> --terms "term1,term2,term3"
Script Outputs (JSON Fields)
| Script | Key Outputs |
|---|---|
scripts/concept_mapper.py | results.matches, results.unmatched, results.suggestions |
scripts/crystal_mapper.py | results.ontology_classes, results.ontology_properties, results.effective_system, results.bravais_lattice, results.validation_warnings |
scripts/sample_annotator.py | results.annotations, results.sample_type, results.material_type, results.unmapped_fields, results.suggested_properties |
Workflow
- If the user provides natural-language terms, use
concept_mapper.pyto find matching ontology classes. - If the user describes crystal structure parameters, use
crystal_mapper.pyto map them and validate constraints. - For a complete sample description, use
sample_annotator.pyto produce full ontology annotations. - Review any validation warnings (e.g., lattice parameter mismatches for the crystal system).
- Check
unmapped_fieldsandsuggested_propertiesfor completeness.
Conversational Workflow Example
User: I'm setting up an MD simulation of BCC iron with lattice parameter 2.87 angstroms.
What CMSO terms should I use?
Agent: Let me map your iron sample to CMSO ontology terms.
[Runs: sample_annotator.py --ontology cmso --sample '{"material":"iron","structure":"BCC","lattice_a":2.87,"space_group":229}' --json]
Your BCC iron simulation maps to these CMSO annotations:
- **Sample**: Atomic Scale Sample (subclass of Computational Sample)
- **Material**: Crystalline Material
- **Unit Cell**: Bravais lattice = "cI" (body-centered cubic)
- **Space Group**: number = 229 (Im-3m)
- **Lattice**: a = 2.87 Å
- **Element**: Fe
Suggested additions:
- Number of atoms in the simulation cell
- Simulation cell vectors and angles
CLI Examples
# Map a single concept
python3 skills/ontology/ontology-mapper/scripts/concept_mapper.py \
--ontology cmso --term "space group" --json
# Map multiple terms
python3 skills/ontology/ontology-mapper/scripts/concept_mapper.py \
--ontology cmso --terms "FCC,copper,lattice constant" --json
# Map crystal parameters (with ontology-specific labels)
python3 skills/ontology/ontology-mapper/scripts/crystal_mapper.py \
--ontology cmso --bravais FCC --space-group 225 --a 3.615 --json
# Map crystal parameters (generic labels, no ontology specified)
python3 skills/ontology/ontology-mapper/scripts/crystal_mapper.py \
--bravais FCC --space-group 225 --a 3.615 --json
# Annotate a full sample
python3 skills/ontology/ontology-mapper/scripts/sample_annotator.py \
--ontology cmso \
--sample '{"material":"copper","structure":"FCC","space_group":225,"lattice_a":3.615}' \
--json
Adding a New Ontology
To support a new ontology (e.g., ASMO), create a <name>_mappings.json in references/:
{
"ontology": "asmo",
"synonyms": { "simulation method": "Simulation Method", ... },
"property_synonyms": { "timestep": "has timestep", ... },
"material_type_rules": { "keyword_rules": [...], "default": "Material" },
"sample_schema": { "sample_class": "Simulation", ... },
"crystal_output": { "base_classes": [...], "property_map": {...} },
"annotation_routing": { "unit_cell_indicators": [...], ... }
}
Then add "mappings_file": "asmo_mappings.json" to the ontology's entry in ontology_registry.json. No code changes needed.
Error Handling
| Error | Cause | Resolution |
|---|---|---|
space_group must be between 1 and 230 | Invalid space group number | Use a valid space group number |
a must be positive | Non-positive lattice parameter | Provide positive values in angstroms |
Sample must be a non-empty dict | Empty or missing sample data | Provide a valid JSON sample dict |
| Validation warnings | Lattice parameters inconsistent with crystal system | Check that a=b=c for cubic, etc. |
Interpretation Guidance
- Confidence scores: 1.0 = exact match, 0.9 = synonym match, 0.7 = substring match, 0.5 = description match
- Validation warnings: indicate potential mistakes (e.g., specifying a!=b for cubic). These are warnings, not errors — the mapping still proceeds.
- Unmapped fields: input keys that the annotator doesn't recognize. These may need manual mapping.
- Suggested properties: additional ontology properties that would make the annotation more complete.
Security
Input Validation
--ontologyis validated against registered ontology names inontology_registry.json(fixed allowlist)--termand--termsare length-limited and used only for substring matching against pre-processed synonym tables (never interpolated into code)--bravaisis validated against a fixed set of recognized lattice type symbols--space-groupis validated as an integer between 1 and 230- Lattice parameters (
--a,--b,--c,--alpha,--beta,--gamma) are validated as finite positive numbers --sampleJSON is parsed withjson.loads()and validated as a non-empty dict; keys and values are type-checked
File Access
- Scripts read pre-processed JSON files from the
references/directory:ontology_registry.json,*_mappings.json,*_summary.json,crystal_systems.json,element_data.json(all read-only) - No scripts write to the filesystem; all output goes to stdout
- No network access is required
Tool Restrictions
- Read: Used to inspect script source, reference files, and ontology data
- Grep: Used to search reference files for mapping patterns or ontology terms
- Glob: Used to locate reference files and ontology data
- Notably, this skill has no Bash or Write access, giving it the lowest attack surface of all skills
Safety Measures
- No
eval(),exec(), or dynamic code generation - No subprocess calls of any kind; all logic runs within Python scripts invoked by the agent
- No file writes; the skill is purely read-only and analytical
- Minimal tool surface (Read, Grep, Glob only) means the agent cannot execute arbitrary commands or modify the filesystem
Limitations
- Concept mapping uses string matching and a per-ontology synonym table; it does not understand arbitrary natural language
- Crystal system validation checks basic constraints only (not all crystallographic rules)
- The element resolver recognizes common element names and symbols but may miss unusual spellings
- Bravais lattice aliases cover common usage (FCC, BCC, HCP) but not all crystallographic notation variants
References
- Mapping Patterns — common mapping examples
- Crystal Systems — crystal system definitions and Bravais lattices
- Element Data — periodic table data
- CMSO Mappings — CMSO-specific synonym tables and annotation config
- CMSO Guide — CMSO ontology overview
Version History
| Date | Version | Changes |
|---|---|---|
| 2026-02-25 | 1.1 | Refactored for multi-ontology support: externalized CMSO-specific knowledge to config |
| 2026-02-25 | 1.0 | Initial release with CMSO mapping support |
Repositorio GitHub
Frequently asked questions
What is the ontology-mapper skill?
ontology-mapper is a Claude Skill by HeshamFS. Skills package instructions and resources that Claude loads on demand, so Claude can perform ontology-mapper-related tasks without extra prompting.
How do I install ontology-mapper?
Use the install commands on this page: add ontology-mapper to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does ontology-mapper belong to?
ontology-mapper is in the Other category, tagged ai and data.
Is ontology-mapper free to use?
Yes. ontology-mapper is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
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