Agent skill

Dbt Testing

by Kilo-Org in Kilo-Org/kilo-marketplace

dbt testing strategies using dbtconstraints for database-level enforcement, generic tests, and singular tests.

Apache-2.0Auto-check passedData & Analytics

Install Dbt Testing

skills CLI
$ npx skills add Kilo-Org/kilo-marketplace --skill dbt-testing -a claude-code

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

GitHub CLI
$ gh skill install Kilo-Org/kilo-marketplace dbt-testing --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/Kilo-Org/kilo-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dbt-testing .claude/skills/dbt-testing && 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
dbt-testing
GitHub stars
189
Token cost
~4k tokens
SKILL.md length
872 words
Files
2
Skills in repo
87
Repo updated
First seen
Licence
Apache-2.0

At a glance

dbt testing strategies using dbtconstraints for database-level enforcement, generic tests, and singular tests.

  • Works in 5 steps: Test Early and Often → Layer-Appropriate Testing → Use dbt_constraints for Production → …
  • Implementing data quality checks
  • SKILL.md covers Purpose, When to Use This Skill, Testing Philosophy and Why Use dbt_constraints?, plus 8 more sections
  • Calls dbt

What it does

Dbt Testing is an agent skill from Kilo-Org/kilo-marketplace. dbt testing strategies using dbtconstraints for database-level enforcement, generic tests, and singular tests. Use this skill when implementing data quality checks, adding primary/foreign key constraints, creating custom tests, or establishing comprehensive testing frameworks across bronze/silver/gold layers.

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file.

It sits in Data & Analytics, covering Data pipelines and ETL, Data cleaning and Test strategy. It works with dbt. The repository describes itself as: Kilo Marketplace - A curated collection of Skills, MCP Servers, and Modes for enhancing AI agent capabilities across the Kilo ecosystem—including Kilo Code (VS Code extension)… The licence is Apache-2.0.

When your agent uses it

  • Implementing data quality checks
  • Adding primary/foreign key constraints
  • Creating custom tests
  • Establishing comprehensive testing frameworks across bronze/silver/gold layers

Example prompts

  • “/dbt-testing”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Test Early and Often
  2. Layer-Appropriate Testing
  3. Use dbt_constraints for Production
  4. Document Test Purpose
  5. Balance Coverage vs Performance

What it can do on your machine

Read from SKILL.md and the folder at commit ff51758. 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

    Shell commands in SKILL.md call:

    • dbt

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.getdbt.com
    • github.com

    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

Dbt Testing loads about 4k tokens when it runs. Until then it costs about 81 tokens; SKILL.md has 872 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~81
When it runs · the whole SKILL.md, loaded when a task matches
~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 Kilo-Org/kilo-marketplace at commit ff51758, republished under its Apache-2.0 licence (© Kilo-Org). 872 words, ~3,990 tokens.

Download SKILL.mdSave it as .claude/skills/dbt-testing/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
dbt-testing
description
dbt testing strategies using dbt_constraints for database-level enforcement, generic tests, and singular tests. Use this skill when implementing data quality checks, adding primary/foreign key constraints, creating custom tests, or establishing comprehensive testing frameworks across bronze/silver/gold layers.
metadata.category
data

dbt Testing

Purpose

Transform AI agents into experts on dbt testing strategies, providing guidance on implementing comprehensive data quality checks with database-enforced constraints, generic tests, and custom singular tests to ensure data integrity across all layers.

When to Use This Skill

Activate this skill when users ask about:

  • Implementing data quality tests
  • Adding primary key and foreign key constraints
  • Using dbt_constraints package for database-level enforcement
  • Creating generic (reusable) tests
  • Writing singular (one-off) tests
  • Testing strategies by layer (bronze/silver/gold)
  • Debugging test failures
  • Configuring test severity levels
  • Storing test failures for analysis

Official dbt Documentation: Testing


Testing Philosophy

Implement tests in this order for maximum data quality:

  1. Primary Keys - Every dimension must have one
  2. Foreign Keys - All fact relationships
  3. Unique Keys - Business key constraints
  4. Business Rules - Domain-specific validations
  5. Data Quality - Completeness, accuracy, consistency

Why Use dbt_constraints?

The dbt_constraints package provides database-level enforcement (not just dbt tests):

✅ Database Enforcement - Creates actual constraints in the data warehouse ✅ Performance - Database-level constraints improve query optimization ✅ Data Integrity - Prevents invalid data at all access points (not just dbt) ✅ Documentation - Constraints visible in database metadata and BI tools ✅ Query Optimization - Database can use constraints for better execution plans

Standard dbt tests only validate during dbt test runs. dbt_constraints creates real database constraints that are enforced 24/7.

Official dbt_constraints Documentation: GitHub - Snowflake-Labs/dbt_constraints


Package Installation

yaml
# packages.yml
packages:
  - package: Snowflake-Labs/dbt_constraints
    version: [">=0.8.0", "<1.0.0"]

  - package: dbt-labs/dbt_utils
    version: [">=1.0.0", "<2.0.0"]

Install packages:

bash
dbt deps

Official dbt Docs: Package Management


Primary Key Testing

Simple Primary Key (dbt_constraints)

Required for every dimension:

yaml
# models/gold/_models.yml
models:
  - name: dim_customers
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.primary_key
Composite Primary Key

When primary key spans multiple columns:

yaml
models:
  - name: fct_order_lines
    tests:
      - dbt_constraints.primary_key:
          column_names:
            - order_id
            - line_number
Alternative: Built-in dbt Tests

Not recommended - no database enforcement:

yaml
columns:
  - name: product_id
    tests:
      - not_null
      - unique

Limitation: Only validates during dbt test runs, doesn't prevent bad data from other sources.


Foreign Key Testing

Simple Foreign Key (dbt_constraints)

Ensures referential integrity:

yaml
models:
  - name: fct_orders
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_customers')
              pk_column_name: customer_id
Multiple Foreign Keys

For facts with multiple dimension relationships:

yaml
models:
  - name: fct_order_lines
    columns:
      - name: order_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('fct_orders')
              pk_column_name: order_id

      - name: product_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_products')
              pk_column_name: product_id

      - name: customer_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_customers')
              pk_column_name: customer_id
Alternative: Built-in dbt Relationships Test

Not recommended - no database enforcement:

yaml
columns:
  - name: customer_id
    tests:
      - relationships:
          to: ref('dim_customers')
          field: customer_id

Unique Key Testing

Simple Unique Key (dbt_constraints)

For business keys (non-primary keys that must be unique):

yaml
columns:
  - name: customer_email
    tests:
      - dbt_constraints.unique_key
Composite Unique Key

When uniqueness spans multiple columns:

yaml
models:
  - name: stg_orders
    tests:
      - dbt_constraints.unique_key:
          column_names:
            - order_number
            - order_source

Generic Tests (Reusable)

Built-in dbt Tests
yaml
columns:
  - name: order_status
    tests:
      - not_null
      - accepted_values:
          values: ["pending", "processing", "shipped", "delivered", "cancelled"]

  - name: order_amount
    tests:
      - not_null
dbt_utils Tests

Powerful generic tests from dbt_utils package:

yaml
columns:
  - name: customer_email
    tests:
      - dbt_utils.not_null_proportion:
          at_least: 0.95 # 95% of rows must have email

  - name: order_amount
    tests:
      - dbt_utils.accepted_range:
          min_value: 0
          max_value: 1000000

  - name: customer_status
    tests:
      - dbt_utils.not_empty_string

Official dbt_utils Documentation: dbt_utils - Generic Tests


Custom Generic Tests

Create reusable test for common patterns:

sql
-- tests/generic/test_positive_values.sql
{% test positive_values(model, column_name) %}

select count(*)
from {{ model }}
where {{ column_name }} <= 0

{% endtest %}

Usage:

yaml
columns:
  - name: order_total
    tests:
      - positive_values

  - name: quantity
    tests:
      - positive_values

Another Example: Date Range Test

sql
-- tests/generic/test_recent_data.sql
{% test recent_data(model, column_name, days_ago=30) %}

select count(*)
from {{ model }}
where {{ column_name }} < dateadd(day, -{{ days_ago }}, current_date())

{% endtest %}

Usage:

yaml
columns:
  - name: order_date
    tests:
      - recent_data:
          days_ago: 7 # Alert if no orders in last 7 days

Singular Tests (One-Off)

For complex business logic that doesn't fit generic tests:

sql
-- tests/singular/test_order_dates_sequential.sql
with date_validation as (
    select
        o.order_id,
        o.order_date,
        c.signup_date
    from {{ ref('fct_orders') }} o
    join {{ ref('dim_customers') }} c
        on o.customer_id = c.customer_id
    where o.order_date < c.signup_date  -- Order before signup = invalid
)

select * from date_validation

Test fails if ANY rows are returned.

More Singular Test Examples

Revenue Reconciliation:

sql
-- tests/singular/test_revenue_reconciliation.sql
-- Ensure fact table revenue matches source system
with fact_revenue as (
    select sum(order_amount) as total_revenue
    from {{ ref('fct_orders') }}
    where order_date = current_date() - 1
),

source_revenue as (
    select sum(amount) as total_revenue
    from {{ source('erp', 'orders') }}
    where order_date = current_date() - 1
),

comparison as (
    select
        f.total_revenue as fact_revenue,
        s.total_revenue as source_revenue,
        abs(f.total_revenue - s.total_revenue) as difference
    from fact_revenue f
    cross join source_revenue s
)

select * from comparison
where difference > 0.01  -- Tolerance of 1 cent

Referential Integrity Check:

sql
-- tests/singular/test_orphaned_orders.sql
-- Find orders with invalid customer_id (not in dim_customers)
select
    o.order_id,
    o.customer_id
from {{ ref('fct_orders') }} o
left join {{ ref('dim_customers') }} c
    on o.customer_id = c.customer_id
where c.customer_id is null
  and o.customer_id != -1  -- Exclude ghost key

Official dbt Documentation: Singular Tests


Testing by Layer

Bronze Layer (Staging)

Focus: Basic data quality at source

yaml
models:
  - name: stg_tpc_h__customers
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.primary_key

      - name: customer_email
        tests:
          - not_null

Keep it simple - just verify source data integrity.


Silver Layer (Intermediate)

Focus: Business rule validation, calculated fields

yaml
models:
  - name: int_customers__with_orders
    columns:
      - name: customer_id
        tests:
          - dbt_constraints.primary_key

      - name: lifetime_orders
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0

      - name: lifetime_value
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0

Add business logic validation - ensure calculated fields make sense.


Gold Layer (Marts)

Focus: Comprehensive constraint enforcement with dbt_constraints

yaml
models:
  - name: dim_customers
    description: "Customer dimension with full history and metrics"
    columns:
      - name: customer_id
        description: "Unique customer identifier"
        tests:
          - dbt_constraints.primary_key

      - name: customer_tier
        description: "Customer value classification"
        tests:
          - accepted_values:
              values: ["bronze", "silver", "gold", "platinum"]

      - name: customer_email
        tests:
          - dbt_constraints.unique_key

  - name: fct_orders
    description: "Order transactions fact table"
    columns:
      - name: order_id
        tests:
          - dbt_constraints.primary_key

      - name: customer_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_customers')
              pk_column_name: customer_id

      - name: product_id
        tests:
          - dbt_constraints.foreign_key:
              pk_table_name: ref('dim_products')
              pk_column_name: product_id

      - name: order_amount
        tests:
          - not_null
          - dbt_utils.accepted_range:
              min_value: 0

Maximum enforcement - use all constraint types to ensure production data quality.


Test Configuration

Store Test Failures

Analyze failed test records:

bash
dbt test --store-failures
yaml
# dbt_project.yml
tests:
  +store_failures: true
  +schema: dbt_test_failures

Query failures:

sql
select * from dbt_test_failures.not_null_dim_customers_customer_email

Test Severity Levels

Warn vs Error:

yaml
columns:
  - name: customer_email
    tests:
      - dbt_constraints.unique_key:
          config:
            severity: warn # or 'error' (default)

Severity Behavior:

  • error: Test failure stops dbt execution (exit code 1)
  • warn: Test failure logs warning but continues (exit code 0)

Use warn for:

  • Data quality checks that shouldn't block deployment
  • Known edge cases during migration
  • Monitoring tests

Limit Test Execution

Test specific model:

bash
dbt test --select dim_customers

Test by type:

bash
dbt test --select test_type:generic    # All generic tests
dbt test --select test_type:singular   # All singular tests

Test with dependencies:

bash
dbt test --select +dim_customers+  # Test model and all dependencies

Official dbt Documentation: Test Selection


Show full SKILL.md (350 more words)Show less

Running Tests

bash
# Run all tests
dbt test

# Build models and test together (recommended)
dbt build  # Runs models, then tests

# Test specific model
dbt test --select dim_customers

# Test specific column
dbt test --select dim_customers,column:customer_id

# Test by layer
dbt test --select tag:gold

# Test with failures stored
dbt test --store-failures --select fct_orders

Best Practice: Use dbt build instead of dbt run + dbt test separately.


Testing Best Practices

1. Test Early and Often

Add tests as you build models, not after deployment.

2. Layer-Appropriate Testing
  • Bronze: Basic not_null and primary key tests
  • Silver: Business rule validation, range checks
  • Gold: Comprehensive constraint enforcement with dbt_constraints
3. Use dbt_constraints for Production

Database-level constraints provide:

  • 24/7 enforcement (not just during dbt runs)
  • Performance optimization
  • Better integration with BI tools
4. Document Test Purpose
yaml
columns:
  - name: customer_tier
    description: "Customer segmentation based on lifetime value"
    tests:
      - accepted_values:
          values: ["bronze", "silver", "gold", "platinum"]
          config:
            severity: error
5. Balance Coverage vs Performance
  • Don't over-test trivial columns
  • Focus on business-critical fields
  • Use sampling for very large tables if needed

Testing Checklist

Before moving to production:

  • All dimensions have primary key tests
  • All facts have foreign key tests to dimensions
  • Business rules are validated with tests
  • Data quality tests are in place (not_null, accepted_values)
  • Tests run successfully in CI/CD pipeline
  • dbt_constraints enabled for all production marts
  • Test failures configured to store in database
  • Singular tests created for complex business logic

Helping Users with Testing

Strategy for Assisting Users

When users ask about testing:

  1. Identify model type: Dimension? Fact? Intermediate?
  2. Recommend appropriate tests: By layer and purpose
  3. Prioritize constraints: Primary keys → Foreign keys → Business rules
  4. Provide complete examples: Working YAML configurations
  5. Explain benefits: Why dbt_constraints over standard tests
  6. Show how to run: Commands and debugging approaches
Common User Questions

"What tests should I add?"

  • Start with dbt_constraints for primary/foreign keys
  • Add not_null for required fields
  • Use accepted_values for enums
  • Create singular tests for complex business logic

"Why use dbt_constraints instead of regular tests?"

  • Database-level enforcement (24/7, not just during dbt runs)
  • Better query performance
  • Prevents bad data from any source
  • Visible in database metadata

"How do I debug test failures?"

  • Use --store-failures to save failing records
  • Query the test failure table
  • Review actual data that failed the test
  • Add more specific tests to isolate issue


Goal: Transform AI agents into expert dbt testers who implement comprehensive, database-enforced data quality checks that protect data integrity across all layers and access patterns.

© Kilo-Org, 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

SKILL.md and 1 other file in skills/dbt-testing of Kilo-Org/kilo-marketplace.

  • SKILL.md
  • LICENSE

Open the folder on GitHubat commit ff51758

Compare with similar skills

Dbt Testing 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.

Dbt Testing compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dbt Testing this skillKilo-Org/kilo-marketplace189—~4kAutomated safety check: PassApache-2.0
Data Quality Frameworkswshobson/agents40k10 repos~1.1kAutomated safety check: PassMIT
Dbt Transformation Patternswshobson/agents40k8 repos~781Automated 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
Dbt Model Specmohitagw15856/pm-claude-skills1.4k—~939Automated safety check: PassMIT

Similar skills

  • Sets up data quality checks with Great Expectations, dbt tests and data contracts, with checkpoints and pass-fail reports for pipelines.

    40k GitHub starsUsed in 10 repos~1.1k tokens
    Data & AnalyticsAuto-check passed
  • Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies.

    40k GitHub starsUsed in 8 repos~781 tokens
    Data & AnalyticsAuto-check passed
  • Authoring Data Quality Checks

    PostHog/posthog-foss

    Official

    Adds and runs data quality checks (dbt-test style assertions) on a project's warehouse tables and saved-query views, and HogQL catalog metrics: not-null, uniqueness, accepted values, referential…

    721 GitHub stars~2.8k tokensUpdated today
    Data & AnalyticsAuto-check passed
  • Data Quality Checks

    mohitagw15856/pm-claude-skills

    Design the data quality checks for a table or pipeline across the standard dimensions.

    1.4k GitHub stars~919 tokensUpdated yesterday
    Data & AnalyticsAuto-check passed
  • Dbt Model Spec

    mohitagw15856/pm-claude-skills

    Spec a dbt model — its grain, sources, transformations, tests, and materialization.

    1.4k GitHub stars~939 tokensUpdated yesterday
    Data & AnalyticsAuto-check passed
  • Data Quality Framework

    revfactory/harness-100

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

    1.3k GitHub stars~1.1k tokensUpdated 6 mo ago
    Data & AnalyticsAuto-check passed

More from Kilo-Org/kilo-marketplace

All 87 skills in this repo
  • AzureML Project Scaffolding

    Kilo-Org/kilo-marketplace

    Sets up and maintains AzureML-ready Python projects as uv workspaces with devcontainers, a Makefile and job YAML, so local runs match cloud jobs and experiments stay reproducible.

    189 GitHub stars~3.1k tokensUpdated 8 days ago
    Auto-check: notes
  • Jupyter Notebook Builder

    Kilo-Org/kilo-marketplace

    Creates, inspects, edits and runs Jupyter notebooks, scaffolding experiment or tutorial notebooks from templates and preferring a Jupyter MCP server over raw JSON edits.

    189 GitHub stars~1.3k tokensUpdated 8 days ago
    Auto-check passed
  • Tableau Dashboard Creator

    Kilo-Org/kilo-marketplace

    Takes a plain-language dashboard request through brand setup, data exploration, planning, an interactive HTML mock and a Tableau implementation spec.

    189 GitHub stars~3.8k tokensUpdated 8 days ago
    Auto-check: notes
  • Elasticsearch File Ingest

    Kilo-Org/kilo-marketplace

    Ingest and transform data files (CSV/JSON/Parquet/Arrow IPC) into Elasticsearch with stream processing and custom transforms.

    189 GitHub stars~2.8k tokensUpdated 8 days ago
    Auto-check passed
  • Nifi Flow Layout

    Kilo-Org/kilo-marketplace

    A skill your agent uses when arranging Apache NiFi processors, process groups, ports, comments, numbering, crossing connections, dense fan-in/fan-out, or reusable readable canvas layouts.

    189 GitHub stars~1.5k tokensUpdated 8 days ago
    Auto-check passed
  • Splunk Ingest Processor Setup

    Kilo-Org/kilo-marketplace

    Render Cisco Data Fabric ingest-time routing workflows and Splunk Cloud Platform Ingest Processor setup plans with SPL2 pipelines, source types, destinations, lifecycle handoffs, queue and…

    189 GitHub stars~1.2k tokensUpdated 8 days ago
    Auto-check passed

Works with

Questions about Dbt Testing

What does Dbt Testing do?

dbt testing strategies using dbtconstraints for database-level enforcement, generic tests, and singular tests. Dbt Testing is an agent skill from Kilo-Org/kilo-marketplace. dbt testing strategies using dbtconstraints for database-level enforcement, generic tests, and singular tests.

When should I use Dbt Testing?

Dbt Testing fits situations like: implementing data quality checks; adding primary/foreign key constraints; creating custom tests; establishing comprehensive testing frameworks across bronze/silver/gold layers.

How do I install Dbt Testing in Claude Code?

Run `npx skills add Kilo-Org/kilo-marketplace --skill dbt-testing -a claude-code`. Or copy the skill folder (skills/dbt-testing in Kilo-Org/kilo-marketplace) into .claude/skills/dbt-testing in your project. Claude Code loads it when a task matches its description.

How do I install Dbt Testing in Codex?

Run `npx skills add Kilo-Org/kilo-marketplace --skill dbt-testing -a codex`. Or copy the skill folder (skills/dbt-testing in Kilo-Org/kilo-marketplace) into .agents/skills/dbt-testing in your project. Codex loads it when a task matches its description.

Can I use Dbt Testing 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 Kilo-Org/kilo-marketplace --skill dbt-testing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dbt-testing, .gemini/skills/dbt-testing, .github/skills/dbt-testing and .opencode/skills/dbt-testing in your project.

What does Dbt Testing need to run?

Going by SKILL.md and its folder, Dbt Testing needs the command-line tools its instructions call (dbt).

Does Dbt Testing access the network?

SKILL.md names 2 domains. As links in the text: docs.getdbt.com and github.com. This is read from the text; nothing was executed.

Is Dbt Testing 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 Dbt Testing use?

Dbt Testing is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dbt Testing use?

About 4k tokens (SKILL.md is roughly 16k 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 Dbt Testing?

Skills that share tags, products or a category with Dbt Testing: Data Quality Frameworks (wshobson/agents, 40k stars), Dbt Transformation Patterns (wshobson/agents, 40k stars), Authoring Data Quality Checks (PostHog/posthog-foss, 721 stars) and Data Quality Checks (mohitagw15856/pm-claude-skills, 1.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dbt Testing?

Kilo-Org (a GitHub organization) maintains it in Kilo-Org/kilo-marketplace, which has 189 GitHub stars. The repository holds 87 skills in this directory. The repository was last updated on September 28, 2026.

Source: Kilo-Org/kilo-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.