Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-29 00:30 UTC

dbt-labs / dbt-project-evaluator

This package contains macros and models to find DAG issues automatically

ShellApache-2.0★ 577 stars⑂ 94 forkssince Jan 2022View on GitHub ↗

dbt-labs/dbt-project-evaluator holds a health index of 88 out of 100, placing it in the Excellent band. It scores highest on Vitality (86/100) and lowest on AI Readiness (40/100). It was last updated 3 days ago. 2 contributors account for most of its recent work.

88
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

88
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 74 is calibrated to 88 on the published index scale (record calibration 2026-08-02).

Ownership

dbt LabsOrganization · verified domain
2,341 followers421 public repossince Apr 2016

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

86Excellent · 21% of overall
How it's scored
36/36Push recency — last push 3 days ago
12.5/36Commit cadence — 18/52 weeks with commits
17.1/18Commit volume — 80 commits in the last year
10/10OpenSSF Scorecard: Maintained — 18 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year80
human_commit_share1
days_since_last_push3
active_weeks_last_year18

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 53 releases published
36/36Release recency — latest release 3 days ago
27/27Release cadence — a release every ~25.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count53
latest_release_tagv1.4.0
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases25.9
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

71Good · 17% of overall
How it's scored
44.8/60Stars — 577 stars
16.4/25Forks — 94 forks
4.7/15Watchers — 8 watchers
Inputs used
forks94
stars577
watchers8
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

80Excellent · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.6/22.5Commit distribution — top contributor authored 31% of commits
13.5/13.5Contributor breadth — 50 contributors
10/10OpenSSF Scorecard: Contributors — project has 9 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled50
top_contributor_share0.306
How it's scored
39/42Issue resolution — 93% of issues closed
24.7/30PR acceptance — 263/319 decided PRs merged
13/13Newcomer PR acceptance — 2/2 first-time contributors' PRs merged in 30d
3/15OpenSSF Scorecard: Code-Review — Found 3/12 approved changesets -- score normalized to 2
Inputs used
merged_prs263
open_issues20
closed_issues259
prs_merged_7d4
prs_decided_7d4
prs_merged_30d5
prs_decided_30d5
issue_closed_ratio0.928
closed_unmerged_prs56
first_time_authors_30d2
first_time_prs_merged_30d2
first_time_prs_decided_30d2
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
24.2/25Owner reach — 2,341 followers of dbt-labs
25/25Track record — 421 public repos, account ~10 yr old
Inputs used
followers2,341
owner_typeOrganization
is_verifiedyes
owner_logindbt-labs
public_repos421
account_age_days3,825

Engineering Quality

Are baseline engineering and documentation practices in place?

85Excellent · 19% of overall
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config — tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 11 out of 12 merged PRs checked by a CI test -- score normalized to 9
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://dbt-labs.github.io/dbt-project-evaluator/latest/
10/10Repository description
10/10Topics — 1 topics
0/10Wiki
Inputs used
topicsdbt-packages
has_wikino
homepagehttps://dbt-labs.github.io/dbt-project-evaluator/latest/
docs_sitehttps://dbt-labs.github.io/dbt-project-evaluator/latest/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

51Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.2/2.5CI-Tests — 11 out of 12 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 3/12 approved changesets -- score normalized to 2
2.5/2.5Contributors — project has 9 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 18 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
3.8/7.5Vulnerabilities — 5 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate5.1
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

40Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.getdbt.com/llms.txt)
40/40Legible commit history — 79 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.getdbt.com/llms.txt
legible_history_share0.79
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — tox.ini
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice — 8 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
1/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.08
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Shell without a type-check config
0/55Manageable file sizes — no source files detected
Inputs used
primary_languageShell
largest_source_bytes—
source_files_sampled0
oversized_source_files0
Excluded from scoring (no data or not applicable): Manageable file sizes. Remaining weights renormalized.

Key facts

577GitHub stars
50contributors
80commits, last 12 months
3days since last push
53releases
2bus factor
20open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 0 ★ / 94 ⇿
0Stars
94Forks
51Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0204060801009342022-062024-082026-09
Major 1Minor 16Patch 34

Each point covers 4 days.

OpenSSF Scorecard 5.1 / 10
5.1aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-29 00:29 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
9CI-Tests11 out of 12 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 3/12 approved changesets -- score normalized to 2
10Contributorsproject has 9 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained18 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
1Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 1
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
5Vulnerabilities5 existing vulnerabilities detected
All dependencies 5

Full resolved dependency set from the GitHub dependency graph: 0 direct and 5 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIdbt-bigquery—indirect
PyPIdbt-databricks—indirect
PyPIdbt-redshift—indirect
PyPIdbt-snowflake—indirect
PyPIpip—indirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics.