Show HN: Technical-debt score for dbt projects on Snowflake/BigQuery/Redshift

Description and use

**Brief Summary (2‑3 sentences)**
The project provides a “technical‑debt” score for dbt (data build tool) projects that run on Snowflake, BigQuery, or Redshift, helping teams gauge code health, maintainability, and potential refactoring effort. It aggregates metadata such as README quality, recent activity, and repository signals to produce a numeric score (out of 100). While the score can be a useful quick sanity check, the underlying data is sparse and requires manual validation before relying on it for critical decisions.

**Value**

• **Rapid health indicator** – Gives data‑engineering teams an at‑a‑glance metric to prioritize which dbt models need cleanup or deeper review.
• **Cross‑platform support** – Works with the three major cloud data warehouses, so the same scoring logic can be reused across Snowflake, BigQuery, and Redshift environments.
• **Decision‑making aid** – When combined with internal CI/CD metrics, the score can help justify refactor tickets, allocate engineering bandwidth, or enforce governance policies.

**Practical Adoption Path**

| Step | Action | Why |
|------|--------|-----|
| 1️⃣ | **Clone & inspect** – Pull the repo, read the README, and run the provided script on a small, non‑production dbt project. | Confirms the tool runs in your environment and surfaces any missing dependencies. |
| 2️⃣ | **Validate scoring logic** – Compare the generated score against known “good” and “bad” dbt repos in your org. | Ensures the metric aligns with your internal definition of technical debt. |
| 3️⃣ | **Integrate into CI** – Add the scoring command to a pre‑merge pipeline (e.g., GitHub Actions) and set a threshold (e.g., ≥ 70) that must be met before merging. | Automates continuous monitoring and prevents regression. |
| 4️⃣ | **Dashboard/reporting** – Export the score to a monitoring tool (e.g., Looker, Grafana) or embed it in a weekly engineering health report. | Gives stakeholders visibility without manual runs. |
| 5️⃣ | **Feedback loop** – Track false positives/negatives, file issues upstream, and consider contributing improvements (e.g., more granular metrics). | Improves the tool’s reliability and aligns it with your workflow. |

**Production Readiness** – *Medium*

• **Suitable for**: prototypes, internal health dashboards, and as a gating check in CI for low‑risk environments.
• **Not yet ready for**: mission‑critical production pipelines where a single numeric score would drive automated remediation, because the underlying metadata is sparse and the scoring algorithm is not extensively vetted.
• **Pre‑adoption checklist**: verify the license (compatible with your org’s policy), confirm recent maintenance activity, review open issues and pull‑request velocity, and test the tool against a representative set of your dbt projects.

If those checks pass, the project can be safely introduced as a supplemental quality metric, with the understanding that deeper code reviews and existing static‑analysis tools (e.g., dbt‑utils, SQLFluff) should still be the primary safeguards.

Use in a workflow

Show HN: Technical-debt score for dbt projects on Snowflake/BigQuery/Redshift may be useful when its README and activity match a concrete workflow.

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.