Agent skill

NLP Preprocessing Toolkit

Text preprocessing technique catalog: tokenization, normalization, stopwords, morphological analysis, embedding selection, and language-specific processing guides.

Apache-2.0Auto-check passedAI & LLM Engineering

Install NLP Preprocessing Toolkit

skills CLI

$ npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a claude-code

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

GitHub CLI

$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --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/revfactory/harness-100.git skills-src && mkdir -p .claude/skills && cp -r skills-src/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .claude/skills/nlp-preprocessing-toolkit && 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-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit into .claude/skills/nlp-preprocessing-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-preprocessing-toolkit", 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/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit

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 revfactory/harness-100 --skill nlp-preprocessing-toolkit -a codex

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

GitHub CLI

$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --agent codex

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

Manual copy

$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .agents/skills && cp -r skills-src/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .agents/skills/nlp-preprocessing-toolkit && 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-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit into .agents/skills/nlp-preprocessing-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-preprocessing-toolkit", 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 revfactory/harness-100 --skill nlp-preprocessing-toolkit -a cursor

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

GitHub CLI

$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --agent cursor

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

Manual copy

$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .cursor/skills/nlp-preprocessing-toolkit && 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-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit into .cursor/skills/nlp-preprocessing-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-preprocessing-toolkit", 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/revfactory/harness-100.git --path en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit

--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 revfactory/harness-100 --skill nlp-preprocessing-toolkit -a gemini-cli

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

GitHub CLI

$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --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/revfactory/harness-100.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .gemini/skills/nlp-preprocessing-toolkit && 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-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit into .gemini/skills/nlp-preprocessing-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-preprocessing-toolkit", 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 revfactory/harness-100 nlp-preprocessing-toolkit

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 revfactory/harness-100 --skill nlp-preprocessing-toolkit -a github-copilot

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

Manual copy

$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .github/skills && cp -r skills-src/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .github/skills/nlp-preprocessing-toolkit && 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-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit into .github/skills/nlp-preprocessing-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-preprocessing-toolkit", 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 revfactory/harness-100 --skill nlp-preprocessing-toolkit -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 revfactory/harness-100 nlp-preprocessing-toolkit --agent opencode

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

Manual copy

$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .opencode/skills/nlp-preprocessing-toolkit && 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-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit into .opencode/skills/nlp-preprocessing-toolkit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nlp-preprocessing-toolkit", 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-preprocessing-toolkit
GitHub stars: 1.3k
Token cost: ~1.4k tokens
SKILL.md length: 214 words
Files: 1
Skills in repo: 464
Repo updated: Mar 22, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Text preprocessing technique catalog: tokenization, normalization, stopwords, morphological analysis, embedding selection, and language-specific processing guides.

Requests involving text preprocessing
SKILL.md covers Preprocessing Pipeline, Language-Specific Processing…, Text Vectorization and Text Quality Metrics, plus 1 more section
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Morphological analysis

What it does

NLP Preprocessing Toolkit is an agent skill from revfactory/harness-100. Text preprocessing technique catalog: tokenization, normalization, stopwords, morphological analysis, embedding selection, and language-specific processing guides. Use this skill for requests involving 'text preprocessing', 'tokenization', 'morphological analysis', 'KoNLPy', 'stopwords', 'normalization', 'TF-IDF', 'embeddings', 'Word2Vec', 'NLP preprocessing', etc. Enhances the text processing capabilities of the preprocessor and extractor agents. Note: sentiment analysis models and classification algorithm…

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Natural language processing, Embeddings and Database schema design. It works with Java. The licence is Apache-2.0.

When your agent uses it

Requests involving text preprocessing
Morphological analysis
NLP preprocessing

Example prompts

“text preprocessing”
“tokenization”
“morphological analysis”
“/nlp-preprocessing-toolkit”

Requirements

Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 8e8d35c. 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 (its code samples are python).
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 Preprocessing Toolkit loads about 1.4k tokens when it runs. Until then it costs about 146 tokens; SKILL.md has 214 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~146
When it runs · the whole SKILL.md, loaded when a task matches: ~1.4k

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 revfactory/harness-100 at commit 8e8d35c, republished under its Apache-2.0 licence (© revfactory). 214 words, ~1,372 tokens.

Download SKILL.mdSave it as .claude/skills/nlp-preprocessing-toolkit/SKILL.md (or your agent's skills folder).

name: nlp-preprocessing-toolkit
description: Text preprocessing technique catalog: tokenization, normalization, stopwords, morphological analysis, embedding selection, and language-specific processing guides. Use this skill for requests involving 'text preprocessing', 'tokenization', 'morphological analysis', 'KoNLPy', 'stopwords', 'normalization', 'TF-IDF', 'embeddings', 'Word2Vec', 'NLP preprocessing', etc. Enhances the text processing capabilities of the preprocessor and extractor agents. Note: sentiment analysis models and classification algorithm selection are outside the scope of this skill.

NLP Preprocessing Toolkit — Text Preprocessing Tools Guide

A catalog of preprocessing techniques for transforming text data into analysis-ready formats.

Preprocessing Pipeline

Raw Text
├── 1. Encoding normalization (UTF-8)
├── 2. HTML/special character removal
├── 3. Unicode normalization (NFKC)
├── 4. Lowercasing (for alphabetic scripts)
├── 5. Tokenization
├── 6. Stopword removal
├── 7. Morphological analysis / stemming
├── 8. Regex filtering
└── 9. Vectorization (TF-IDF / embeddings)

Language-Specific Processing (Korean)

Morphological Analyzer Comparison

Analyzer	Speed	Accuracy	Custom Dictionary	Installation
Mecab	Fastest	High	Yes	C dependency
Okt (Twitter)	Fast	Medium	Yes	Java dependency
Komoran	Medium	High	Yes	Java dependency
Kkma	Slow	High	No	Java dependency
Kiwi	Fast	High	Yes	Python native

python

# Kiwi (easiest installation, excellent performance)
from kiwipiepy import Kiwi
kiwi = Kiwi()

tokens = kiwi.tokenize("FatherEnteredTheRoom")
# [Token(form='Father', tag='NNG'), Token(form='subject', tag='JKS'),
#  Token(form='Room', tag='NNG'), Token(form='to', tag='JKB'),
#  Token(form='entered', tag='VV'), Token(form='hon', tag='EP'),
#  Token(form='past', tag='EP'), Token(form='decl', tag='EF')]

# Extract nouns only
nouns = [t.form for t in tokens if t.tag.startswith('NN')]

Korean Text Normalization

python

import re, unicodedata

def normalize_korean(text):
    # Unicode normalization (compatibility decomposition + canonical composition)
    text = unicodedata.normalize('NFKC', text)

    # Remove repeated characters ("xxxxx" -> "xx")
    text = re.sub(r'(.)\1{2,}', r'\1\1', text)

    # Remove standalone consonants/vowels (repeated consonants/vowels, etc. may be preserved for sentiment analysis)
    # text = re.sub(r'[\u3131-\u3163]+', '', text)

    # Keep only alphanumeric characters, Korean characters, and whitespace
    text = re.sub(r'[^\w\s\u3131-\uD79D]', ' ', text)

    # Remove multiple whitespace
    text = re.sub(r'\s+', ' ', text).strip()

    return text

Korean Stopwords

python

KOREAN_STOPWORDS = {
    # Particles (Korean grammatical markers)
    'i', 'ga', 'eun', 'neun', 'eul', 'reul', 'e', 'ui', 'wa', 'gwa',
    'do', 'ro', 'eseo', 'kkaji', 'buteo', 'man', 'euro',
    # Pronouns
    'geu', 'i', 'jeo', 'geot', 'su', 'deung', 'deul',
    # Adverbs
    'maeu', 'aju', 'jeongmal', 'neomu', 'jal', 'tto', 'deo',
    # Conjunctions/Interjections
    'geurigo', 'hajiman', 'geureonde', 'geuraeseo',
}

Text Vectorization

TF-IDF

python

from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(
    max_features=10000,
    min_df=2,           # Must appear in at least 2 documents
    max_df=0.95,        # Exclude if appearing in more than 95% of documents
    ngram_range=(1, 2), # Unigrams + bigrams
    sublinear_tf=True,  # 1 + log(tf) — dampens high-frequency terms
)
tfidf_matrix = vectorizer.fit_transform(texts)

Embedding Selection Guide

Method	Dimensions	Best For	Characteristics
TF-IDF	High-dimensional (sparse)	Keyword-centric, small-scale	Interpretable, fast
Word2Vec	100-300	Similarity, analogies	Word-level, limited context
FastText	100-300	Korean, OOV handling	Subword-based, robust to unseen words
BERT	768	Classification, NER, QA	Context-dependent, bidirectional
Sentence-BERT	384-768	Document similarity, search	Sentence-level embeddings

python

# Sentence-BERT (Korean)
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('jhgan/ko-sroberta-multitask')
embeddings = model.encode(texts, show_progress_bar=True)
# Cosine similarity
from sklearn.metrics.pairwise import cosine_similarity
sim_matrix = cosine_similarity(embeddings)

Text Quality Metrics

Metric	Calculation	Threshold
Average token count	Tokens per text	< 3 indicates analysis limitations
Vocabulary diversity	Unique tokens / total tokens	0.2-0.8 is acceptable
Language purity	Proportion of primary language	> 90% recommended
Missing rate	Proportion of empty texts	< 5%
Duplication rate	Proportion of identical texts	< 10%

Preprocessing Decision Checklist

Encoding issues resolved (e.g., CP949)
HTML tags/URLs removed
Emoji handling decided (remove vs. convert to text vs. use for sentiment)
Number handling decided (remove vs. tokenize vs. replace with [NUM])
Morphological analyzer selected
Stopword list customized for domain
Minimum token count filtering applied
Vectorization method selected

Files

Just SKILL.md in en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit of revfactory/harness-100.

Open the folder on GitHubat commit 8e8d35c

Compare with similar skills

NLP Preprocessing Toolkit 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 Preprocessing Toolkit compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
NLP Preprocessing Toolkit this skillrevfactory/harness-100	1.3k	—	~1.4k	Automated safety check: Pass	Apache-2.0	6 mo ago
Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel	1.3k	—	~1.1k	Automated safety check: Pass	Custom licence	today
Comparetaishi-i/awesome-japanese-nlp-resources	1k	—	~4.1k	Automated safety check: Notes	CC0-1.0	yesterday
Sentence Transformers EmbeddingsOrchestra-Research/AI-Research-SKILLs	13k	3 repos	~1.6k	Automated safety check: Pass	MIT	3 mo ago
Researchtaishi-i/awesome-japanese-nlp-resources	1k	—	~3.5k	Automated safety check: Notes	CC0-1.0	yesterday
Searchtaishi-i/awesome-japanese-nlp-resources	1k	—	~4.3k	Automated safety check: Notes	CC0-1.0	yesterday

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Questions about NLP Preprocessing Toolkit

What does NLP Preprocessing Toolkit do?

Text preprocessing technique catalog: tokenization, normalization, stopwords, morphological analysis, embedding selection, and language-specific processing guides. NLP Preprocessing Toolkit is an agent skill from revfactory/harness-100. Text preprocessing technique catalog: tokenization, normalization, stopwords, morphological analysis, embedding selection, and language-specific processing guides.

When should I use NLP Preprocessing Toolkit?

NLP Preprocessing Toolkit fits situations like: requests involving text preprocessing; morphological analysis; NLP preprocessing.

How do I install NLP Preprocessing Toolkit in Claude Code?

Run `npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a claude-code`. Or copy the skill folder (en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit in revfactory/harness-100) into .claude/skills/nlp-preprocessing-toolkit in your project. Claude Code loads it when a task matches its description.

How do I install NLP Preprocessing Toolkit in Codex?

Run `npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a codex`. Or copy the skill folder (en/33-text-processor/.claude/skills/nlp-preprocessing-toolkit in revfactory/harness-100) into .agents/skills/nlp-preprocessing-toolkit in your project. Codex loads it when a task matches its description.

Can I use NLP Preprocessing Toolkit 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 revfactory/harness-100 --skill nlp-preprocessing-toolkit -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-preprocessing-toolkit, .gemini/skills/nlp-preprocessing-toolkit, .github/skills/nlp-preprocessing-toolkit and .opencode/skills/nlp-preprocessing-toolkit in your project.

What does NLP Preprocessing Toolkit need to run?

SKILL.md names no scripts, command-line tools or credentials: NLP Preprocessing Toolkit is instructions for the agent only. Our summary lists: Python 3.

Does NLP Preprocessing Toolkit 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 Preprocessing Toolkit 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 Preprocessing Toolkit use?

NLP Preprocessing Toolkit is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does NLP Preprocessing Toolkit use?

About 1.4k tokens (SKILL.md is roughly 5.5k 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 Preprocessing Toolkit?

Skills that share tags, products or a category with NLP Preprocessing Toolkit: Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k stars), Compare (taishi-i/awesome-japanese-nlp-resources, 1k stars), Sentence Transformers Embeddings (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Research (taishi-i/awesome-japanese-nlp-resources, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains NLP Preprocessing Toolkit?

revfactory (a GitHub user) maintains it in revfactory/harness-100, which has 1,290 GitHub stars. The repository holds 464 skills in this directory. The repository was last updated on March 22, 2026.

Source: revfactory/harness-100 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 Preprocessing Toolkit

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

NLP Preprocessing Toolkit — Text Preprocessing Tools Guide

Preprocessing Pipeline

Language-Specific Processing (Korean)

Morphological Analyzer Comparison

Korean Text Normalization

Korean Stopwords

Text Vectorization

TF-IDF

Embedding Selection Guide

Text Quality Metrics

Preprocessing Decision Checklist

Files

Compare with similar skills

Similar skills

More from revfactory/harness-100

Related

Questions about NLP Preprocessing Toolkit