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Agent skill

Data Quality Framework

by revfactory in revfactory/harness-100

data (accuracy, completeness, timeliness, consistency etc.)per verification rule and Great Expectations, dbt tests etc.of also for guide.

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/en/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/

Facts

Skill name
data-quality-framework
GitHub stars
1.3k
Token cost
~1.1k tokens
SKILL.md length
119 words
Files
1
Skills in repo
464
Repo updated
First seen
Licence
Apache-2.0

At a glance

data (accuracy, completeness, timeliness, consistency etc.)per verification rule and Great Expectations, dbt tests etc.of also for guide.

  • Tasks that involve Data cleaning
  • SKILL.md covers data 6, verification rule pattern, Great Expectations and dbt Tests, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Data pipelines and ETL

What it does

Data Quality Framework is an agent skill from revfactory/harness-100. data (accuracy, completeness, timeliness, consistency etc.)per verification rule and Great Expectations, dbt tests etc.of also for guide. 'data ', 'verification rule', 'Great Expectations', 'dbt test', 'data profiling', 'or more detection', 'data ' etc. data this for. data-quality-managerof verification -ize. , pipeline schedulingthis before architecture this of scope .

Its SKILL.md is about 1.1k 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 cleaning, Data pipelines and ETL and Test data and fixtures. It works with dbt. The licence is Apache-2.0.

When your agent uses it

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

Example prompts

  • “verification rule”
  • “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 1.1k tokens when it runs. Until then it costs about 101 tokens; SKILL.md has 119 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~101
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

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). 119 words, ~1,127 tokens.

Download SKILL.mdSave it as .claude/skills/data-quality-framework/SKILL.md (or your agent's skills folder).
name
data-quality-framework
description
data (accuracy, completeness, timeliness, consistency etc.)per verification rule and Great Expectations, dbt tests etc.of also for guide. 'data ', 'verification rule', 'Great Expectations', 'dbt test', 'data profiling', 'or more detection', 'data ' etc. data this for. data-quality-managerof verification -ize. , pipeline schedulingthis before architecture this of scope .

Data Quality Framework — data framework guide

data systematicas of, measurement, monitoringlower framework.

data 6

ofmeasurementthreshold example
accuracy (Accuracy), business rule verificationalso > 99.9%
completeness (Completeness)required dataNULL ratio, required satisfiedNULL < 1%
timeliness (Timeliness)between within alsolatencybetween, data alsolatency < 30minutes
consistency (Consistency)system between dayverification, integrityday = 0
day (Uniqueness)/key ratio= 0%
valid (Validity)/scope compliant, scopeefficiency > 99%

verification rule pattern

P0 (required — failure pipeline )
yaml
rules:
  - name: pk_uniqueness
    type: uniqueness
    column: order_id
    threshold: 0  #  0cases

  - 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  # dayday minimum order count
    max_deviation: 0.5  # beforeday  50% or more   warning

  - name: referential_integrity
    type: consistency
    source: orders.customer_id
    reference: customers.id
    match_rate: 1.0
P1 (important — warning after in progress)
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  # 1 exceeding order of

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

Great Expectations

python
import great_expectations as gx

#  of
suite = context.add_expectation_suite("orders_quality")

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

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

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

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

dbt Tests

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

data profiling list

columnper profile:
├── type: actual type vs  type
├── count(Cardinality): value count
├── NULL ratio:  pattern
├── distribution: the, also, also
├── or more: IQR  this
├── pattern: date, thisday, before-ize etc.  day
└── dependency: function-based  

tableper profile:
├──  count:  scope vs actual
├── : before   
├──  integrity: FK violated casescount
└── between distribution: record creation between pattern

or more detection

-basedfor/
Z-Scoredistribution data|x - μ| / σ > 3
IQRdistributionx < Q1-1.5IQR or x > Q3+1.5IQR
thisaverage7day thisaverage 2σ this
beforedaydayday -based|today - yesterday| / yesterday > 0.5

data (Data Contract)

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

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%

© 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 en/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
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Data Quality Framework this skillrevfactory/harness-1001.3k—~1.1kAutomated safety check: PassApache-2.0
Data Quality Frameworkswshobson/agents40k10 repos~1.1kAutomated safety check: PassMIT
Dbt Transformation Patternswshobson/agents40k8 repos~781Automated safety check: PassMIT
Build Artifactsgodatadriven/dbt-bouncer135—~399Automated safety check: PassMIT
Authoring Data Quality ChecksPostHog/posthog-foss721—~2.8kAutomated safety check: PassMIT
Data Quality Checksmohitagw15856/pm-claude-skills1.4k—~919Automated safety check: PassMIT

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  • Data Quality Frameworks

    wshobson/agents

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    40k GitHub starsUsed in 10 repos~1.1k tokens
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Works with

Categories

Questions about Data Quality Framework

What does Data Quality Framework do?

data (accuracy, completeness, timeliness, consistency etc.)per verification rule and Great Expectations, dbt tests etc.of also for guide. Data Quality Framework is an agent skill from revfactory/harness-100.of also for guide.

When should I use Data Quality Framework?

Data Quality Framework fits situations like: tasks that involve Data cleaning; tasks that involve Data pipelines and ETL; 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 (en/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 (en/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 1.1k tokens (SKILL.md is roughly 4.5k 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 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.