Agent skill

Data Quality Framework

데이터 품질 차원(정확성, 완전성, 적시성, 일관성 등)별 검증 규칙 설계와 Great Expectations, dbt tests 등의 도구 활용 가이드.

Apache-2.0Auto-check passedData & Analytics

Install Data Quality Framework

skills CLI

$ npx skills add revfactory/harness-100 --skill data-quality-framework -a claude-code

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

GitHub CLI

$ gh skill install revfactory/harness-100 data-quality-framework --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/27-data-pipeline/.claude/skills/data-quality-framework .claude/skills/data-quality-framework && 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 "data-quality-framework" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/27-data-pipeline/.claude/skills/data-quality-framework into .claude/skills/data-quality-framework/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-framework", 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/ko/27-data-pipeline/.claude/skills/data-quality-framework

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 data-quality-framework -a codex

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

GitHub CLI

$ gh skill install revfactory/harness-100 data-quality-framework --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/ko/27-data-pipeline/.claude/skills/data-quality-framework .agents/skills/data-quality-framework && 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 "data-quality-framework" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/27-data-pipeline/.claude/skills/data-quality-framework into .agents/skills/data-quality-framework/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-framework", 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 data-quality-framework -a cursor

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

GitHub CLI

$ gh skill install revfactory/harness-100 data-quality-framework --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/ko/27-data-pipeline/.claude/skills/data-quality-framework .cursor/skills/data-quality-framework && 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 "data-quality-framework" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/27-data-pipeline/.claude/skills/data-quality-framework into .cursor/skills/data-quality-framework/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-framework", 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 ko/27-data-pipeline/.claude/skills/data-quality-framework

--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 data-quality-framework -a gemini-cli

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

GitHub CLI

$ gh skill install revfactory/harness-100 data-quality-framework --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/ko/27-data-pipeline/.claude/skills/data-quality-framework .gemini/skills/data-quality-framework && 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 "data-quality-framework" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/27-data-pipeline/.claude/skills/data-quality-framework into .gemini/skills/data-quality-framework/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-framework", 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 data-quality-framework

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 data-quality-framework -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/ko/27-data-pipeline/.claude/skills/data-quality-framework .github/skills/data-quality-framework && 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 "data-quality-framework" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/27-data-pipeline/.claude/skills/data-quality-framework into .github/skills/data-quality-framework/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-framework", 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 data-quality-framework -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 data-quality-framework --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/ko/27-data-pipeline/.claude/skills/data-quality-framework .opencode/skills/data-quality-framework && 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 "data-quality-framework" agent skill from https://github.com/revfactory/harness-100/tree/main/ko/27-data-pipeline/.claude/skills/data-quality-framework into .opencode/skills/data-quality-framework/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-quality-framework", 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: data-quality-framework
GitHub stars: 1.3k
Token cost: ~989 tokens
SKILL.md length: 151 words
Files: 1
Skills in repo: 464
Repo updated: Mar 22, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

데이터 품질 차원(정확성, 완전성, 적시성, 일관성 등)별 검증 규칙 설계와 Great Expectations, dbt tests 등의 도구 활용 가이드.

Tasks that involve Data pipelines and ETL
SKILL.md covers 데이터 품질 6차원, 검증 규칙 설계 패턴, Great Expectations 구현 and dbt Tests 구현, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
Tasks that involve Data cleaning

What it does

Data Quality Framework is an agent skill from revfactory/harness-100. 데이터 품질 차원(정확성, 완전성, 적시성, 일관성 등)별 검증 규칙 설계와 Great Expectations, dbt tests 등의 도구 활용 가이드. '데이터 품질', '검증 규칙', 'Great Expectations', 'dbt test', '데이터 프로파일링', '이상 탐지', '데이터 계약' 등 데이터 품질 관리 시 이 스킬을 사용한다. data-quality-manager의 품질 검증 역량을 강화한다. 단, 파이프라인 스케줄링이나 전체 아키텍처 설계는 이 스킬의 범위가 아니다.

Its SKILL.md is about 990 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 Data & Analytics, covering Data pipelines and ETL, Data cleaning and Test data and fixtures. It works with dbt. The licence is Apache-2.0.

When your agent uses it

Tasks that involve Data pipelines and ETL
Tasks that involve Data cleaning
Tasks that involve Test data and fixtures

Example prompts

“Great Expectations”
“dbt test”
“/data-quality-framework”

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 yaml and 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

Data Quality Framework loads about 989 tokens when it runs. Until then it costs about 75 tokens; SKILL.md has 151 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~75
When it runs · the whole SKILL.md, loaded when a task matches: ~989

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). 151 words, ~989 tokens.

Download SKILL.mdSave it as .claude/skills/data-quality-framework/SKILL.md (or your agent's skills folder).

name: data-quality-framework
description: 데이터 품질 차원(정확성, 완전성, 적시성, 일관성 등)별 검증 규칙 설계와 Great Expectations, dbt tests 등의 도구 활용 가이드. '데이터 품질', '검증 규칙', 'Great Expectations', 'dbt test', '데이터 프로파일링', '이상 탐지', '데이터 계약' 등 데이터 품질 관리 시 이 스킬을 사용한다. data-quality-manager의 품질 검증 역량을 강화한다. 단, 파이프라인 스케줄링이나 전체 아키텍처 설계는 이 스킬의 범위가 아니다.

Data Quality Framework — 데이터 품질 프레임워크 가이드

데이터 품질을 체계적으로 정의, 측정, 모니터링하는 프레임워크.

데이터 품질 6차원

차원	정의	측정 방법	임계값 예시
정확성 (Accuracy)	현실을 올바르게 반영	소스 대조, 비즈니스 규칙 검증	정확도 > 99.9%
완전성 (Completeness)	필수 데이터 존재	NULL 비율, 필수 필드 충족	NULL < 1%
적시성 (Timeliness)	기대 시간 내 도착	지연시간, 데이터 신선도	지연 < 30분
일관성 (Consistency)	시스템 간 일치	교차 검증, 참조 무결성	불일치 = 0
유일성 (Uniqueness)	중복 없음	중복 행/키 비율	중복 = 0%
유효성 (Validity)	포맷/범위 준수	정규식, 범위 체크	유효율 > 99%

검증 규칙 설계 패턴

P0 (필수 — 실패 시 파이프라인 중단)

yaml

rules:
  - name: pk_uniqueness
    type: uniqueness
    column: order_id
    threshold: 0  # 중복 0건

  - name: not_null_critical
    type: completeness
    columns: [order_id, customer_id, total_amount]
    max_null_rate: 0

  - name: row_count_sanity
    type: volume
    min_rows: 1000  # 일일 최소 주문 수
    max_deviation: 0.5  # 전일 대비 50% 이상 변동 시 경고

  - name: referential_integrity
    type: consistency
    source: orders.customer_id
    reference: customers.id
    match_rate: 1.0

P1 (중요 — 경고 후 진행)

yaml

rules:
  - name: email_format
    type: validity
    column: email
    pattern: "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
    threshold: 0.99

  - name: amount_range
    type: accuracy
    column: total_amount
    min: 0
    max: 100000000  # 1억 초과 주문은 의심

  - name: freshness
    type: timeliness
    column: created_at
    max_age_hours: 24

Great Expectations 구현

python

import great_expectations as gx

# 기대 정의
suite = context.add_expectation_suite("orders_quality")

# 완전성
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)

# 유일성
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeUnique(column="order_id")
)

# 유효성
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeBetween(
        column="total_amount", min_value=0, max_value=100000000
    )
)

# 볼륨
suite.add_expectation(
    gx.expectations.ExpectTableRowCountToBeBetween(
        min_value=1000, max_value=1000000
    )
)

dbt Tests 구현

yaml

# schema.yml
models:
  - name: orders
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: customer_id
        tests:
          - not_null
          - relationships:
              to: ref('customers')
              field: id
      - name: total_amount
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0
              max_value: 100000000
    tests:
      - dbt_utils.recency:
          datepart: hour
          field: created_at
          interval: 24

데이터 프로파일링 체크리스트

컬럼별 프로파일:
├── 타입: 실제 타입 vs 선언 타입
├── 기수성(Cardinality): 고유값 수
├── NULL 비율: 결측 패턴
├── 분포: 히스토그램, 왜도, 첨도
├── 이상치: IQR 기반 아웃라이어
├── 패턴: 날짜, 이메일, 전화번호 등 정규식 일치율
└── 의존성: 함수적 종속 관계

테이블별 프로파일:
├── 행 수: 기대 범위 vs 실제
├── 중복률: 전체 행 중 중복
├── 참조 무결성: FK 위반 건수
└── 시간 분포: 레코드 생성 시간 패턴

이상 탐지 기법

기법	적용	공식/방법
Z-Score	정규분포 데이터	\|x - μ\| / σ > 3
IQR	비정규분포	x < Q1-1.5IQR or x > Q3+1.5IQR
이동평균	시계열 볼륨	7일 이동평균 대비 2σ 이탈
전일 대비	일일 적재	\|today - yesterday\| / yesterday > 0.5

데이터 계약 (Data Contract)

yaml

# data-contract.yml
name: orders
version: "2.0.0"
owner: order-team
description: "주문 데이터 계약"

schema:
  - name: order_id
    type: string
    required: true
    unique: true
  - name: total_amount
    type: decimal(10,2)
    required: true
    min: 0

sla:
  freshness: 1h
  availability: 99.9%

quality:
  completeness: 99.9%
  accuracy: 99.99%

Files

Just SKILL.md in ko/27-data-pipeline/.claude/skills/data-quality-framework of revfactory/harness-100.

Open the folder on GitHubat commit 8e8d35c

Compare with similar skills

Data Quality Framework 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.

Data Quality Framework compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Data Quality Framework this skillrevfactory/harness-100	1.3k	—	~989	Automated safety check: Pass	Apache-2.0	6 mo ago
Data Quality Frameworkswshobson/agents	40k	10 repos	~1.1k	Automated safety check: Pass	MIT	2 days ago
Dbt Transformation Patternswshobson/agents	40k	8 repos	~781	Automated safety check: Pass	MIT	2 days ago
Build Artifactsgodatadriven/dbt-bouncer	135	—	~399	Automated safety check: Pass	MIT	2 days ago
Authoring Data Quality ChecksPostHog/posthog-foss	721	—	~2.8k	Automated safety check: Pass	MIT	today
Data Quality Checksmohitagw15856/pm-claude-skills	1.4k	—	~919	Automated safety check: Pass	MIT	yesterday

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Questions about Data Quality Framework

What does Data Quality Framework do?

데이터 품질 차원(정확성, 완전성, 적시성, 일관성 등)별 검증 규칙 설계와 Great Expectations, dbt tests 등의 도구 활용 가이드. Data Quality Framework is an agent skill from revfactory/harness-100. 데이터 품질 차원(정확성, 완전성, 적시성, 일관성 등)별 검증 규칙 설계와 Great Expectations, dbt tests 등의 도구 활용 가이드.

When should I use Data Quality Framework?

Data Quality Framework fits situations like: tasks that involve Data pipelines and ETL; tasks that involve Data cleaning; tasks that involve Test data and fixtures.

How do I install Data Quality Framework in Claude Code?

Run `npx skills add revfactory/harness-100 --skill data-quality-framework -a claude-code`. Or copy the skill folder (ko/27-data-pipeline/.claude/skills/data-quality-framework in revfactory/harness-100) into .claude/skills/data-quality-framework in your project. Claude Code loads it when a task matches its description.

How do I install Data Quality Framework in Codex?

Run `npx skills add revfactory/harness-100 --skill data-quality-framework -a codex`. Or copy the skill folder (ko/27-data-pipeline/.claude/skills/data-quality-framework in revfactory/harness-100) into .agents/skills/data-quality-framework in your project. Codex loads it when a task matches its description.

Can I use Data Quality Framework 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 data-quality-framework -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-quality-framework, .gemini/skills/data-quality-framework, .github/skills/data-quality-framework and .opencode/skills/data-quality-framework in your project.

What does Data Quality Framework need to run?

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

Does Data Quality Framework 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 Data Quality Framework 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 Data Quality Framework use?

Data Quality Framework 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 Data Quality Framework use?

About 989 tokens (SKILL.md is roughly 4k 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 Data Quality Framework?

Skills that share tags, products or a category with Data Quality Framework: Data Quality Frameworks (wshobson/agents, 40k stars), Dbt Transformation Patterns (wshobson/agents, 40k stars), Build Artifacts (godatadriven/dbt-bouncer, 135 stars) and Authoring Data Quality Checks (PostHog/posthog-foss, 721 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Data Quality Framework?

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 Data Quality Framework

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

Data Quality Framework — 데이터 품질 프레임워크 가이드

데이터 품질 6차원

검증 규칙 설계 패턴

P0 (필수 — 실패 시 파이프라인 중단)

P1 (중요 — 경고 후 진행)

Great Expectations 구현

dbt Tests 구현

데이터 프로파일링 체크리스트

이상 탐지 기법

데이터 계약 (Data Contract)

Files

Compare with similar skills

Similar skills

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