dbt-coverage
One-stop-shop for docs and test coverage of dbt projects
Decision gist · record as of 2026-08-14
Yes. dbt-coverage fills a clear gap in dbt project quality assurance with minimal dependencies, active maintenance, and no known vulnerabilities. Install it if you want to measure and enforce documentation and test coverage in your dbt projects; the zero-config design and low friction make it a straightforward addition to any dbt workflow.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a dbt project with target/manifest.json and target/catalog.json files, which are generated by running 'dbt docs generate' in your dbt project directory.
- Low friction install with only typer as a runtime dependency.
- Actively maintained as of 2026-04-29 with 241 repository stars.
License · maintenance · safety
MIT (permissive) — MIT license permits unrestricted use, modification, and distribution with minimal legal friction.
last release 2026-04-29 (107 days) · last repo commit 2026-04-29 · 241 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 171,678 downloads/mo, #10,360 on PyPI
Alternatives
Verify before relying
pip install dbt-coverage
cd your_dbt_project
dbt run && dbt docs generate
dbt-coverage compute doc --cov-report coverage.json
# Output shows per-model and total documentation coverage percentages- Whether dbt itself is assumed to be pre-installed or if dbt-coverage handles dbt installation
- Performance characteristics on large dbt projects with hundreds or thousands of models
- Exact dbt version compatibility beyond the 'optimized for dbt 1.0' statement in the description
What it is and what it does
dbt-coverage is a CLI tool that measures how thoroughly your dbt project is documented and tested. It reads the manifest.json and catalog.json files that dbt generates, then reports coverage percentages for each model and the project overall. You run it after dbt docs generate to get a snapshot of which models lack documentation or test coverage.
The tool is designed to integrate into CI/CD pipelines and help teams enforce documentation and testing standards. It supports filtering by model path, outputting results to JSON files, and formatting reports as Markdown tables. With only typer as a runtime dependency and roughly 480 lines of code, it is lightweight and straightforward to audit.
Use it for
- Enforce minimum documentation coverage in CI/CD by failing builds when docs fall below a threshold
- Audit a newly inherited dbt project to quickly identify which models lack documentation or tests
- Track documentation and test coverage trends over time by storing JSON reports in version control
- Generate Markdown coverage reports for team dashboards or pull request comments
- Filter coverage reports to specific model directories to focus on particular layers of your dbt DAG
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
dbt-coverage fills a clear gap in dbt project quality assurance with minimal dependencies, active maintenance, and no known vulnerabilities. Install it if you want to measure and enforce documentation and test coverage in your dbt projects; the zero-config design and low friction make it a straightforward addition to any dbt workflow.
Install
dbt-coverage on PyPI
Before you install
Low friction install with only typer as a runtime dependency. Actively maintained as of 2026-04-29 with 241 repository stars.
Requires a dbt project with target/manifest.json and target/catalog.json files, which are generated by running 'dbt docs generate' in your dbt project directory.
License in practice
MIT license permits unrestricted use, modification, and distribution with minimal legal friction.
Quickstart
pip install dbt-coverage
cd your_dbt_project
dbt run && dbt docs generate
dbt-coverage compute doc --cov-report coverage.json
# Output shows per-model and total documentation coverage percentagesVerify before relying
- Whether dbt itself is assumed to be pre-installed or if dbt-coverage handles dbt installation
- Performance characteristics on large dbt projects with hundreds or thousands of models
- Exact dbt version compatibility beyond the 'optimized for dbt 1.0' statement in the description
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release <4.0,>=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetyper |
| Maintenance | Actively maintained 107 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 171,678 / month, #10,360 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: dbt_coverage-0.4.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “dbt documentation coverage”
- dbt-coverageMeasures documentation and test coverage of dbt projects by analyzing…
- dbt-osmosisdbt-osmosis is a CLI tool and Python package that automates schema…
- dbt-scoredbt-score lints dbt models against configurable rules for…
Give your agent the search over MCP, or paste the wish link into any chat.
More Quality Assurance packages
Coverage.py measures which lines of Python code are executed during test runs, reporting coverage percentages and identifying untested code paths.
Install it if you want to measure test completeness or enforce coverage thresholds in your project.
Ruff is a Python linter and code formatter written in Rust that combines linting, formatting, and code fixing into a single tool, replacing Flake8, Black, isort, and related utilities.
Pexpect spawns and controls interactive console applications by sending input and matching output patterns, automating tasks that would otherwise require manual interaction.
Black reformats Python source code to a consistent style by parsing entire files and rewriting them according to an opinionated, deterministic set of rules, eliminating manual formatting decisions.
pytest-xdist distributes pytest tests across multiple CPU cores or machines to speed up test execution, with the simplest usage being `pytest -n auto` to spawn workers equal to available CPUs.
Install it if your test suite takes long enough that parallelization would save meaningful time.
Validates AWS CloudFormation templates in YAML or JSON format against resource provider schemas and best practices, checking property values and configuration correctness.
Install it if you work with CloudFormation templates.
See also dbt-bouncer · dbt-colibri · dbt-autofix · dbt-core · dbt-loom · openlineage-dbt · collate-dbt-artifacts-parser · interrogate · dbt-artifacts-parser