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텍스트 전처리 기법 카탈로그: 토큰화, 정규화, 불용어, 형태소 분석, 임베딩 선택, 한국어 특화 처리 가이드.
Install NLP Preprocessing Toolkit
$ npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .claude/skills && cp -r skills-src/ko/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .claude/skills/nlp-preprocessing-toolkit && rm -rf skills-srcUse ~/.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/
Install the "nlp-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/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 install https://github.com/revfactory/harness-100/tree/main/ko/33-text-processor/.claude/skills/nlp-preprocessing-toolkitType 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.
$ npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .agents/skills && cp -r skills-src/ko/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .agents/skills/nlp-preprocessing-toolkit && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "nlp-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/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.
$ npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/ko/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .cursor/skills/nlp-preprocessing-toolkit && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "nlp-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/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 https://github.com/revfactory/harness-100.git --path ko/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.
$ npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/ko/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .gemini/skills/nlp-preprocessing-toolkit && rm -rf skills-srcUse ~/.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/
Install the "nlp-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/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.
$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkitInstalls 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).
$ npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .github/skills && cp -r skills-src/ko/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .github/skills/nlp-preprocessing-toolkit && rm -rf skills-srcUse ~/.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/
Install the "nlp-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/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.
$ npx skills add revfactory/harness-100 --skill nlp-preprocessing-toolkit -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install revfactory/harness-100 nlp-preprocessing-toolkit --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/revfactory/harness-100.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/ko/33-text-processor/.claude/skills/nlp-preprocessing-toolkit .opencode/skills/nlp-preprocessing-toolkit && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "nlp-preprocessing-toolkit" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/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
- ~886 tokens
- SKILL.md length
- 196 words
- Files
- 1
- Skills in repo
- 464
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
텍스트 전처리 기법 카탈로그: 토큰화, 정규화, 불용어, 형태소 분석, 임베딩 선택, 한국어 특화 처리 가이드.
- Tasks that involve Natural language processing
- SKILL.md covers 전처리 파이프라인, 한국어 특화 처리, 텍스트 벡터화 and 텍스트 품질 메트릭, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
NLP Preprocessing Toolkit is an agent skill from revfactory/harness-100. 텍스트 전처리 기법 카탈로그: 토큰화, 정규화, 불용어, 형태소 분석, 임베딩 선택, 한국어 특화 처리 가이드. '텍스트 전처리', '토큰화', '형태소 분석', 'KoNLPy', '불용어', '정규화', 'TF-IDF', '임베딩', 'Word2Vec', '한국어 NLP' 등 텍스트 전처리 시 이 스킬을 사용한다. preprocessor와 extractor의 텍스트 처리 역량을 강화한다. 단, 감성분석 모델이나 분류 알고리즘 선택은 이 스킬의 범위가 아니다.
Its SKILL.md is about 890 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. It works with Java. The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Natural language processing
Example prompts
- “KoNLPy”
- “TF-IDF”
- “Word2Vec”
- “/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 886 tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 196 words of instructions outside code blocks.
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). 196 words, ~886 tokens.
Download SKILL.mdSave it as
.claude/skills/nlp-preprocessing-toolkit/SKILL.md (or your agent's skills folder).- name
- nlp-preprocessing-toolkit
- description
- 텍스트 전처리 기법 카탈로그: 토큰화, 정규화, 불용어, 형태소 분석, 임베딩 선택, 한국어 특화 처리 가이드. '텍스트 전처리', '토큰화', '형태소 분석', 'KoNLPy', '불용어', '정규화', 'TF-IDF', '임베딩', 'Word2Vec', '한국어 NLP' 등 텍스트 전처리 시 이 스킬을 사용한다. preprocessor와 extractor의 텍스트 처리 역량을 강화한다. 단, 감성분석 모델이나 분류 알고리즘 선택은 이 스킬의 범위가 아니다.
NLP Preprocessing Toolkit — 텍스트 전처리 도구 가이드
텍스트 데이터를 분석 가능한 형태로 변환하는 전처리 기법 카탈로그.
전처리 파이프라인
원본 텍스트
├── 1. 인코딩 정규화 (UTF-8)
├── 2. HTML/특수문자 제거
├── 3. 유니코드 정규화 (NFKC)
├── 4. 소문자 변환 (영문)
├── 5. 토큰화
├── 6. 불용어 제거
├── 7. 형태소 분석 / 어간 추출
├── 8. 정규표현식 필터링
└── 9. 벡터화 (TF-IDF / 임베딩)한국어 특화 처리
형태소 분석기 비교
| 분석기 | 속도 | 정확도 | 사용자 사전 | 설치 |
|---|---|---|---|---|
| Mecab | 가장 빠름 | 높음 | ✅ | C 의존 |
| Okt (Twitter) | 빠름 | 중간 | ✅ | Java 의존 |
| Komoran | 중간 | 높음 | ✅ | Java 의존 |
| Kkma | 느림 | 높음 | ❌ | Java 의존 |
| Kiwi | 빠름 | 높음 | ✅ | Python 네이티브 |
# Kiwi (설치 가장 간편, 성능 우수)
from kiwipiepy import Kiwi
kiwi = Kiwi()
tokens = kiwi.tokenize("아버지가방에들어가셨다")
# [Token(form='아버지', tag='NNG'), Token(form='가', tag='JKS'),
# Token(form='방', tag='NNG'), Token(form='에', tag='JKB'),
# Token(form='들어가', tag='VV'), Token(form='시', tag='EP'),
# Token(form='었', tag='EP'), Token(form='다', tag='EF')]
# 명사만 추출
nouns = [t.form for t in tokens if t.tag.startswith('NN')]한국어 정규화
import re, unicodedata
def normalize_korean(text):
# 유니코드 정규화 (호환 분해 + 정준 결합)
text = unicodedata.normalize('NFKC', text)
# 반복 문자 제거 ("ㅋㅋㅋㅋㅋ" → "ㅋㅋ")
text = re.sub(r'(.)\1{2,}', r'\1\1', text)
# 자음/모음만 있는 것 제거 ("ㅎㅎ", "ㅠㅠ" 등은 감성 분석용 보존 가능)
# text = re.sub(r'[ㄱ-ㅎㅏ-ㅣ]+', '', text)
# 영문+한글+숫자+공백만 보존
text = re.sub(r'[^\w\s가-힣]', ' ', text)
# 다중 공백 제거
text = re.sub(r'\s+', ' ', text).strip()
return text한국어 불용어
KOREAN_STOPWORDS = {
# 조사
'이', '가', '은', '는', '을', '를', '에', '의', '와', '과',
'도', '로', '에서', '까지', '부터', '만', '으로',
# 대명사
'그', '이', '저', '것', '수', '등', '들',
# 부사
'매우', '아주', '정말', '너무', '잘', '또', '더',
# 접속사/감탄사
'그리고', '하지만', '그런데', '그래서',
}텍스트 벡터화
TF-IDF
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer = TfidfVectorizer(
max_features=10000,
min_df=2, # 최소 2개 문서에 등장
max_df=0.95, # 95% 이상 문서에 등장하면 제외
ngram_range=(1, 2), # 유니그램 + 바이그램
sublinear_tf=True, # 1 + log(tf) — 고빈도 완화
)
tfidf_matrix = vectorizer.fit_transform(texts)임베딩 선택 가이드
| 방법 | 차원 | 적합 | 특징 |
|---|---|---|---|
| TF-IDF | 고차원 (희소) | 키워드 중심, 소규모 | 해석 가능, 빠름 |
| Word2Vec | 100~300 | 유사도, 유추 | 단어 수준, 문맥 제한 |
| FastText | 100~300 | 한국어, OOV 처리 | 자소 기반, 미등록어 강건 |
| BERT | 768 | 분류, NER, QA | 문맥 의존적, 양방향 |
| Sentence-BERT | 384~768 | 문서 유사도, 검색 | 문장 수준 임베딩 |
# Sentence-BERT (한국어)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('jhgan/ko-sroberta-multitask')
embeddings = model.encode(texts, show_progress_bar=True)
# 코사인 유사도
from sklearn.metrics.pairwise import cosine_similarity
sim_matrix = cosine_similarity(embeddings)텍스트 품질 메트릭
| 메트릭 | 계산 | 기준 |
|---|---|---|
| 평균 토큰 수 | 텍스트당 토큰 수 | < 3이면 분석 한계 |
| 어휘 다양성 | 고유 토큰 / 전체 토큰 | 0.2~0.8 양호 |
| 언어 순도 | 주 언어 비율 | > 90% 권장 |
| 결측률 | 빈 텍스트 비율 | < 5% |
| 중복률 | 동일 텍스트 비율 | < 10% |
전처리 결정 체크리스트
- 인코딩 문제 해결 (CP949 등)
- HTML 태그/URL 제거
- 이모지 처리 결정 (제거 vs 텍스트 변환 vs 감성 활용)
- 숫자 처리 (제거 vs 토큰화 vs [NUM] 대체)
- 형태소 분석기 선택
- 불용어 목록 도메인 맞춤
- 최소 토큰 수 필터링
- 벡터화 방법 선택
© revfactory, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in ko/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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| NLP Preprocessing Toolkit this skillrevfactory/harness-100 | 1.3k | — | ~886 | Automated safety check: Pass | Apache-2.0 | |
| AI Articleitwanger/toBeBetterJavaer | 18k | — | ~1k | Automated safety check: Pass | None | |
| Hugging Face TokenizersOrchestra-Research/AI-Research-SKILLs | 13k | 6 repos | ~3.4k | Automated safety check: Pass | MIT | |
| OpenMed Model Card Writermaziyarpanahi/openmed | 5.5k | — | ~1.8k | Automated safety check: Pass | Apache-2.0 | |
| Gptqmodel Tokenizer NormalizationModelCloud/GPTQModel | 1.3k | — | ~1.1k | Automated safety check: Pass | Custom licence | |
| Andrej KarpathyK-Dense-AI/mimeo | 282 | — | ~1.9k | Automated safety check: Pass | MIT |
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Questions about NLP Preprocessing Toolkit
What does NLP Preprocessing Toolkit do?
텍스트 전처리 기법 카탈로그: 토큰화, 정규화, 불용어, 형태소 분석, 임베딩 선택, 한국어 특화 처리 가이드. NLP Preprocessing Toolkit is an agent skill from revfactory/harness-100. 텍스트 전처리 기법 카탈로그: 토큰화, 정규화, 불용어, 형태소 분석, 임베딩 선택, 한국어 특화 처리 가이드.
When should I use NLP Preprocessing Toolkit?
NLP Preprocessing Toolkit fits situations like: tasks that involve Natural language processing.
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 (ko/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 (ko/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 886 tokens (SKILL.md is roughly 3.5k 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: AI Article (itwanger/toBeBetterJavaer, 18k stars), Hugging Face Tokenizers (Orchestra-Research/AI-Research-SKILLs, 13k stars), OpenMed Model Card Writer (maziyarpanahi/openmed, 5.5k stars) and Gptqmodel Tokenizer Normalization (ModelCloud/GPTQModel, 1.3k 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 1,295 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.