nlp_expert
Extract named entities (persons, organizations, dates, locations) from text and provide them in structured JSON-LD format.
Best use case
nlp_expert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Extract named entities (persons, organizations, dates, locations) from text and provide them in structured JSON-LD format.
Teams using nlp_expert 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/nlp_expert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nlp_expert Compares
| Feature / Agent | nlp_expert | 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?
Extract named entities (persons, organizations, dates, locations) from text and provide them in structured JSON-LD format.
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
# NLP Expert Skill
The NLP Expert skill uses Gemini 3 to perform advanced Named Entity Recognition (NER). It identifies key entities within dataset descriptions, transcripts, or web results and maps them to standard Schema.org types in JSON-LD.
This is critical for establishing provenance, identifying dataset creators, and mapping geographic and temporal coverage in the Croissant specification.
## Tools
### 1. Extract Named Entities
Analyzes text or a file and returns all detected entities as JSON-LD. Results are stored in `./data/nlp/`.
**Usage:**
```bash
# Process raw text
python3 nlp_expert/scripts/extract_entities.py "Sergei Bodrov was born in Moscow in 1971."
# Process a file (e.g., a transcript)
python3 nlp_expert/scripts/extract_entities.py data/transcripts/6cWcZ2G53gE.txt
```
**Example Output (JSON-LD):**
```jsonld
{
"@context": "https://schema.org/",
"@type": "ItemList",
"itemListElement": [
{
"@type": "Person",
"name": "Sergei Bodrov"
},
{
"@type": "Place",
"name": "Moscow"
}
]
}
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