Agent skill

Intent Taxonomy Builder

by revfactory in revfactory/harness-100

챗봇의 의도(Intent) 분류 체계를 체계적으로 설계하는 방법론. An agent skill from revfactory/harness-100.

Apache-2.0Auto-check passed

Install Intent Taxonomy Builder

skills CLI
$ npx skills add revfactory/harness-100 --skill intent-taxonomy-builder -a claude-code

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

GitHub CLI
$ gh skill install revfactory/harness-100 intent-taxonomy-builder --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/ko/38-chatbot-builder/.claude/skills/intent-taxonomy-builder .claude/skills/intent-taxonomy-builder && 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/

Facts

Skill name
intent-taxonomy-builder
GitHub stars
1.3k
Token cost
~830 tokens
SKILL.md length
169 words
Files
1
Skills in repo
464
Repo updated
First seen
Licence
Apache-2.0

At a glance

챗봇의 의도(Intent) 분류 체계를 체계적으로 설계하는 방법론. An agent skill from revfactory/harness-100.

  • SKILL.md covers 대상 에이전트, 의도 분류 체계 설계 프레임워크, 엔티티 설계 방법론 and 학습 데이터 생성 가이드, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Intent Taxonomy Builder is an agent skill from revfactory/harness-100. 챗봇의 의도(Intent) 분류 체계를 체계적으로 설계하는 방법론. '의도 분류 설계', 'intent 체계', 'NLU 의도 목록', '엔티티 사전', '슬롯 설계' 등 챗봇 의도 분류 체계 설계 시 사용한다. 단, 실제 NLU 모델 학습, 클라우드 NLU 서비스 배포는 이 스킬의 범위가 아니다.

Its SKILL.md is about 830 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The licence is Apache-2.0.

Example prompts

  • “intent 체계”
  • “NLU 의도 목록”
  • “/intent-taxonomy-builder”

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 yaml).

    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

Intent Taxonomy Builder loads about 830 tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 169 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~830

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). 169 words, ~830 tokens.

Download SKILL.mdSave it as .claude/skills/intent-taxonomy-builder/SKILL.md (or your agent's skills folder).
name
intent-taxonomy-builder
description
챗봇의 의도(Intent) 분류 체계를 체계적으로 설계하는 방법론. '의도 분류 설계', 'intent 체계', 'NLU 의도 목록', '엔티티 사전', '슬롯 설계' 등 챗봇 의도 분류 체계 설계 시 사용한다. 단, 실제 NLU 모델 학습, 클라우드 NLU 서비스 배포는 이 스킬의 범위가 아니다.

Intent Taxonomy Builder — 의도 분류 체계 설계 방법론

nlu-developer와 conversation-designer의 의도 분류 설계를 강화하는 스킬.

대상 에이전트

  • nlu-developer — 의도/엔티티/슬롯 체계를 설계할 때 사용
  • conversation-designer — 대화 시나리오와 의도를 매핑할 때 사용

의도 분류 체계 설계 프레임워크

1단계: 도메인 의도 수집
사용자 발화 수집 → 그룹핑 → 의도 후보 도출

수집 소스:
- 기존 FAQ 문서
- CS 문의 로그
- 경쟁사 챗봇 분석
- 사용자 인터뷰/설문
- 도메인 전문가 브레인스토밍
2단계: 의도 계층 구조
Level 0 (도메인)
├── Level 1 (카테고리)
│   ├── Level 2 (세부 의도)
│   └── Level 2
└── Level 1
    └── Level 2

예시 (이커머스):
commerce
├── order (주문)
│   ├── order.place — "주문하고 싶어요"
│   ├── order.status — "주문 상태 확인"
│   ├── order.cancel — "주문 취소해줘"
│   └── order.modify — "주문 변경하고 싶어요"
├── product (상품)
│   ├── product.search — "이런 상품 있어요?"
│   ├── product.detail — "이 상품 상세 정보"
│   └── product.compare — "두 상품 비교해줘"
├── payment (결제)
│   ├── payment.method — "결제 방법 알려줘"
│   ├── payment.refund — "환불 요청"
│   └── payment.receipt — "영수증 발급"
└── general (일반)
    ├── general.greeting — "안녕하세요"
    ├── general.goodbye — "감사합니다"
    └── general.fallback — (미인식)
3단계: 의도 품질 체크리스트
기준설명통과 조건
상호배타성의도 간 중복 없음발화 1개 = 의도 1개
완전성모든 사용자 시나리오 커버fallback < 10%
균형성의도당 학습 데이터 균등최소 20개 발화/의도
명확성이름만으로 목적 파악verb.noun 형식
적정 개수관리 가능 범위2050개 (소규모), 50150개 (대규모)

엔티티 설계 방법론

엔티티 유형
유형설명예시
시스템 엔티티플랫폼 내장@sys.date, @sys.number, @sys.email
사전 엔티티도메인 고정 목록메뉴명, 사이즈, 색상
패턴 엔티티정규식 기반주문번호(ORD-\d{8}), 전화번호
복합 엔티티엔티티 조합주소(시+구+동), 날짜범위
엔티티-슬롯 매핑
의도: order.place
필수 슬롯:
  - product_name (@product) — "아메리카노"
  - quantity (@sys.number) — "두 잔"
선택 슬롯:
  - size (@size) — "톨 사이즈"
  - option (@option) — "얼음 적게"
  - takeout (@boolean) — "포장이요"

슬롯 미충족 시 → 프롬프트:
  - product_name 누락: "어떤 메뉴를 주문하시겠어요?"
  - quantity 누락: "몇 개 주문하시겠어요?"

학습 데이터 생성 가이드

발화 변형 패턴
원본: "주문 취소하고 싶어요"

변형 전략:
1. 어미 변형: "취소해주세요", "취소할래요", "취소 부탁드립니다"
2. 표현 대체: "주문 철회", "주문 취소", "주문 안 할래요"
3. 문맥 추가: "방금 주문한 거 취소", "아까 시킨 것 취소"
4. 오타/축약: "주문취소", "취소여", "캔슬"
5. 간접 표현: "주문한 거 안 받고 싶어요", "그냥 안 살래요"
6. 엔티티 포함: "ORD-12345678 취소해줘"
발화 수 가이드
의도 복잡도최소 발화 수권장 발화 수
단순 (인사/작별)1020
보통 (조회/확인)2050
복잡 (주문/변경)3080
혼동 가능 (유사 의도)50100+

의도 혼동 매트릭스 분석

높은 혼동 쌍 예시:
- order.cancel ↔ payment.refund (취소 vs 환불)
- product.search ↔ product.detail (검색 vs 상세)
- order.modify ↔ order.cancel (변경 vs 취소)

해결 전략:
1. 차별화 발화 추가 (각 의도의 고유 키워드 강화)
2. 의도 합병 (구분 불필요시)
3. 컨텍스트 의존 분리 (대화 상태 기반)
4. 확인 질문 ("취소를 원하시나요, 환불을 원하시나요?")

산출물 템플릿

yaml
intent_taxonomy:
  - intent: order.place
    description: "새 주문 접수"
    examples:
      - "아메리카노 두 잔 주문할게요"
      - "이거 주문하고 싶어요"
    required_slots:
      - name: product_name
        entity: "@product"
        prompt: "어떤 메뉴를 주문하시겠어요?"
    optional_slots:
      - name: quantity
        entity: "@sys.number"
        default: 1
    responses:
      success: "{product_name} {quantity}개 주문 접수되었습니다."
      slot_missing: "주문할 메뉴를 말씀해 주세요."

© 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/38-chatbot-builder/.claude/skills/intent-taxonomy-builder of revfactory/harness-100.

Open the folder on GitHubat commit 8e8d35c

Compare with similar skills

Intent Taxonomy Builder 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.

Intent Taxonomy Builder compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Intent Taxonomy Builder this skillrevfactory/harness-1001.3k—~830Automated safety check: PassApache-2.0
Taxonomy BuilderWILLOSCAR/research-units-pipeline-skills513—~1kAutomated safety check: PassNone
Intent Requirements IntakeYeachan-Heo/oh-my-claudecode40k—~1.5kAutomated safety check: PassMIT
Browser Intentruvnet/ruflo74k—~1.2kAutomated safety check: NotesMIT
Team Builderaffaan-m/ECC274k1 repos~1.8kAutomated safety check: PassMIT
Team Builderaffaan-m/ECC274k1 repos~808Automated safety check: PassMIT

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Questions about Intent Taxonomy Builder

What does Intent Taxonomy Builder do?

챗봇의 의도(Intent) 분류 체계를 체계적으로 설계하는 방법론. An agent skill from revfactory/harness-100. Intent Taxonomy Builder is an agent skill from revfactory/harness-100. 챗봇의 의도(Intent) 분류 체계를 체계적으로 설계하는 방법론.

How do I install Intent Taxonomy Builder in Claude Code?

Run `npx skills add revfactory/harness-100 --skill intent-taxonomy-builder -a claude-code`. Or copy the skill folder (ko/38-chatbot-builder/.claude/skills/intent-taxonomy-builder in revfactory/harness-100) into .claude/skills/intent-taxonomy-builder in your project. Claude Code loads it when a task matches its description.

How do I install Intent Taxonomy Builder in Codex?

Run `npx skills add revfactory/harness-100 --skill intent-taxonomy-builder -a codex`. Or copy the skill folder (ko/38-chatbot-builder/.claude/skills/intent-taxonomy-builder in revfactory/harness-100) into .agents/skills/intent-taxonomy-builder in your project. Codex loads it when a task matches its description.

Can I use Intent Taxonomy Builder 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 intent-taxonomy-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/intent-taxonomy-builder, .gemini/skills/intent-taxonomy-builder, .github/skills/intent-taxonomy-builder and .opencode/skills/intent-taxonomy-builder in your project.

What does Intent Taxonomy Builder need to run?

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

Does Intent Taxonomy Builder 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 Intent Taxonomy Builder 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 Intent Taxonomy Builder use?

Intent Taxonomy Builder 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 Intent Taxonomy Builder use?

About 830 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 Intent Taxonomy Builder?

Skills that share tags, products or a category with Intent Taxonomy Builder: Taxonomy Builder (WILLOSCAR/research-units-pipeline-skills, 513 stars), Intent Requirements Intake (Yeachan-Heo/oh-my-claudecode, 40k stars), Browser Intent (ruvnet/ruflo, 74k stars) and Team Builder (affaan-m/ECC, 274k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Intent Taxonomy Builder?

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.