Agent skill

NLP Engineer

by majiayu000 in majiayu000/claude-skill-registry

Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain.

MITAuto-check passedAI & LLM Engineering

Install NLP Engineer

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry nlp-engineer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-ml/nlp-engineer-skill .claude/skills/nlp-engineer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "nlp-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/nlp-engineer-skill into .claude/skills/nlp-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-engineer", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/nlp-engineer-skill

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry nlp-engineer --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-ml/nlp-engineer-skill .agents/skills/nlp-engineer && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "nlp-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/nlp-engineer-skill into .agents/skills/nlp-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-engineer", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry nlp-engineer --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-ml/nlp-engineer-skill .cursor/skills/nlp-engineer && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "nlp-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/nlp-engineer-skill into .cursor/skills/nlp-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-engineer", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/majiayu000/claude-skill-registry.git --path skills/ai-ml/nlp-engineer-skill

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry nlp-engineer --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-ml/nlp-engineer-skill .gemini/skills/nlp-engineer && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "nlp-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/nlp-engineer-skill into .gemini/skills/nlp-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-engineer", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry nlp-engineer

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-ml/nlp-engineer-skill .github/skills/nlp-engineer && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "nlp-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/nlp-engineer-skill into .github/skills/nlp-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-engineer", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry nlp-engineer --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-ml/nlp-engineer-skill .opencode/skills/nlp-engineer && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "nlp-engineer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/nlp-engineer-skill into .opencode/skills/nlp-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-engineer", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: nlp-engineer
GitHub stars: 666
Used in: 1 other repo
Token cost: ~833 tokens
SKILL.md length: 306 words
Files: 2
Skills in repo: 1,273
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain.

Works in 3 steps: Text Classification Pipeline → NER System → Embedding-Based Search
Building NLP pipelines
SKILL.md covers Purpose, When to Use, Quick Start and Decision Framework, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

NLP Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain. Use when building NLP pipelines, text analysis, or LLM-powered features. Triggers include "NLP", "text classification", "NER", "named entity", "sentiment analysis", "spaCy", "Hugging Face", "transformers".

Its SKILL.md is about 830 tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

It sits in AI & LLM Engineering, covering Natural language processing. It works with Hugging Face, LangChain and Transformers. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.

When your agent uses it

Building NLP pipelines
LLM-powered features
Text classification
Sentiment analysis

Example prompts

“text classification”
“named entity”
“sentiment analysis”
“/nlp-engineer”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

Text Classification Pipeline
NER System
Embedding-Based Search

What it can do on your machine

Read from SKILL.md and the folder at commit 2d14a69. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

NLP Engineer loads about 833 tokens when it runs. Until then it costs about 95 tokens; SKILL.md has 306 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~95
When it runs · the whole SKILL.md, loaded when a task matches: ~833

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 2d14a69, republished under its MIT licence (© majiayu000). 306 words, ~833 tokens.

Download SKILL.mdSave it as .claude/skills/nlp-engineer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: nlp-engineer
description: Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain. Use when building NLP pipelines, text analysis, or LLM-powered features. Triggers include "NLP", "text classification", "NER", "named entity", "sentiment analysis", "spaCy", "Hugging Face", "transformers".

NLP Engineer

Purpose

Provides expertise in Natural Language Processing systems design and implementation. Specializes in text classification, named entity recognition, sentiment analysis, and integrating modern LLMs using frameworks like Hugging Face, spaCy, and LangChain.

When to Use

Building text classification systems
Implementing named entity recognition (NER)
Creating sentiment analysis pipelines
Fine-tuning transformer models
Designing LLM-powered features
Implementing text preprocessing pipelines
Building search and retrieval systems
Creating text generation applications

Quick Start

Invoke this skill when:

Building NLP pipelines (classification, NER, sentiment)
Fine-tuning transformer models
Implementing text preprocessing
Integrating LLMs for text tasks
Designing semantic search systems

Do NOT invoke when:

RAG architecture design → use /ai-engineer
LLM prompt optimization → use /prompt-engineer
ML model deployment → use /mlops-engineer
General data processing → use /data-engineer

Decision Framework

NLP Task Type?
├── Classification
│   ├── Simple → Fine-tuned BERT/DistilBERT
│   └── Zero-shot → LLM with prompting
├── NER
│   ├── Standard entities → spaCy
│   └── Custom entities → Fine-tuned model
├── Generation
│   └── LLM (GPT, Claude, Llama)
└── Semantic Search
    └── Embeddings + Vector store

Core Workflows

1. Text Classification Pipeline

Collect and label training data
Preprocess text (tokenization, cleaning)
Select base model (BERT, RoBERTa)
Fine-tune on labeled dataset
Evaluate with appropriate metrics
Deploy with inference optimization

2. NER System

Define entity types for domain
Create labeled training data
Choose framework (spaCy, Hugging Face)
Train custom NER model
Evaluate precision, recall, F1
Integrate with post-processing rules

3. Embedding-Based Search

Select embedding model (sentence-transformers)
Generate embeddings for corpus
Index in vector database
Implement query embedding
Add hybrid search (keyword + semantic)
Tune similarity thresholds

Best Practices

Start with pretrained models, fine-tune as needed
Use domain-specific preprocessing
Evaluate with task-appropriate metrics
Consider inference latency for production
Implement proper text cleaning pipelines
Use batching for efficient inference

Anti-Patterns

Anti-Pattern	Problem	Correct Approach
Training from scratch	Wastes data and compute	Fine-tune pretrained
No preprocessing	Noisy inputs hurt performance	Clean and normalize text
Wrong metrics	Misleading evaluation	Task-appropriate metrics
Ignoring class imbalance	Biased predictions	Balance or weight classes
Overfitting to eval set	Poor generalization	Proper train/val/test splits

Files

SKILL.md and 1 other file in skills/ai-ml/nlp-engineer-skill of majiayu000/claude-skill-registry.

SKILL.md
metadata.json

Open the folder on GitHubat commit 2d14a69

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.

NeverSight/learn-skills.devdata/skills-md/404kidwiz/claude-supercode-skills/nlp-engineer

Compare with similar skills

NLP Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

NLP Engineer compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
NLP Engineer this skillmajiayu000/claude-skill-registry	666	1 repos	~833	Automated safety check: Pass	MIT	today
Sentence Transformers EmbeddingsOrchestra-Research/AI-Research-SKILLs	13k	3 repos	~1.6k	Automated safety check: Pass	MIT	3 mo ago
Hugging Face Transformers Usagedavila7/claude-code-templates	32k	12 repos	~1.2k	Automated safety check: Pass	MIT	today
Transformers.jshuggingface/skills	11k	1 repos	~6.2k	Automated safety check: Pass	Apache-2.0	6 days ago
Hugging Face Local Model Evalshuggingface/skills	11k	2 repos	~1.6k	Automated safety check: Pass	Apache-2.0	6 days ago
Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs	13k	7 repos	~3.4k	Automated safety check: Pass	MIT	3 mo ago

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Questions about NLP Engineer

What does NLP Engineer do?

Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain. NLP Engineer is an agent skill from majiayu000/claude-skill-registry. Expert in Natural Language Processing, designing systems for text classification, NER, translation, and LLM integration using Hugging Face, spaCy, and LangChain.

When should I use NLP Engineer?

NLP Engineer fits situations like: building NLP pipelines; LLM-powered features; text classification; sentiment analysis.

How do I install NLP Engineer in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a claude-code`. Or copy the skill folder (skills/ai-ml/nlp-engineer-skill in majiayu000/claude-skill-registry) into .claude/skills/nlp-engineer in your project. Claude Code loads it when a task matches its description.

How do I install NLP Engineer in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a codex`. Or copy the skill folder (skills/ai-ml/nlp-engineer-skill in majiayu000/claude-skill-registry) into .agents/skills/nlp-engineer in your project. Codex loads it when a task matches its description.

Can I use NLP Engineer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/claude-skill-registry --skill nlp-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nlp-engineer, .gemini/skills/nlp-engineer, .github/skills/nlp-engineer and .opencode/skills/nlp-engineer in your project.

What does NLP Engineer need to run?

SKILL.md names no scripts, command-line tools or credentials: NLP Engineer is instructions for the agent only.

Does NLP Engineer access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is NLP Engineer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does NLP Engineer use?

NLP Engineer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does NLP Engineer use?

About 833 tokens (SKILL.md is roughly 3.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to NLP Engineer?

Skills that share tags, products or a category with NLP Engineer: Sentence Transformers Embeddings (Orchestra-Research/AI-Research-SKILLs, 13k stars), Hugging Face Transformers Usage (davila7/claude-code-templates, 32k stars), Transformers.js (huggingface/skills, 11k stars) and Hugging Face Local Model Evals (huggingface/skills, 11k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains NLP Engineer?

majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 1,273 skills in this directory. The repository was last updated on October 7, 2026.

Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install NLP Engineer

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

NLP Engineer

Purpose

When to Use

Quick Start

Decision Framework

Core Workflows

1. Text Classification Pipeline

2. NER System

3. Embedding-Based Search

Best Practices

Anti-Patterns

Files

Used in 1 other repository

Compare with similar skills

Similar skills

More from majiayu000/claude-skill-registry

Related

Questions about NLP Engineer