{"categories":[{"label":"Build Tools","url":"https://skillfed.io/packages/category/software-development-build-tools/4"}],"enrichment":{"capability":"dbt-loom is a dbt Core plugin that fetches public model definitions from upstream dbt project artifacts and injects them into your dbt project, enabling multi-project deployments to share models across repositories.","skillfed_tags":["dbt-plugin","multi-project","artifact-federation"],"use_cases":["Share common data models across multiple dbt projects in a monorepo or multi-repo setup without code duplication.","Fetch and reuse models from a centralized dbt Cloud or Paradime project in downstream dbt projects.","Load dbt artifacts stored in cloud object storage (S3, GCS, Azure) to inject models from archived or external dbt runs.","Integrate dbt projects deployed across different data warehouses (Snowflake, Databricks) by fetching manifests from warehouse stages.","Exclude specific upstream packages from injection to avoid conflicts when running tools like dbt-project-evaluator."],"what_it_does":"dbt-loom is a dbt Core plugin that extends dbt's multi-project capabilities by allowing you to fetch public model definitions from upstream dbt projects and inject them into your own project. It works by reading dbt artifact manifests from multiple sources\u2014local files, HTTP(S) URLs, cloud object storage (S3, GCS, Azure), data warehouse stages (Snowflake, Databricks), or dbt hosting platforms (dbt Cloud, Paradime)\u2014and registering those public models as nodes in your dbt project's dependency graph during the dbt-core lifecycle.\n\nThe plugin is designed for organizations running multiple dbt projects that need to share model definitions without duplicating code or maintaining separate copies. It integrates transparently into dbt's standard workflow: you configure manifest sources in a YAML file, and dbt-loom handles fetching, parsing, and injection automatically. It supports environment variable interpolation, gzipped manifest files, and selective exclusion of packages from upstream projects.","worth_installing":"Yes, if you are running multiple dbt projects and need to share model definitions across them. The plugin is actively maintained, has low install friction, and supports a wide range of artifact sources. However, verify the license terms before adopting in proprietary contexts, as the license metadata is missing from the package. No known security vulnerabilities."},"id":"dbt-loom","links":{"html":"https://skillfed.io/packages/dbt-loom","md":"https://skillfed.io/packages/dbt-loom.md","pypi":"https://pypi.org/project/dbt-loom/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-25","license_spdx":null,"license_treatment":"unclear","name":"dbt-loom","python_support":"supports_current","summary":"A dbt-core plugin to import public nodes in multi-project deployments."},"popularity":{"monthly_downloads":182948,"position":10079,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.5"}