Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 08:51 UTC

microsoft / dbt-fabric

PythonMIT★ 148 stars⑂ 51 forkssince Feb 2023View on GitHub ↗

microsoft/dbt-fabric holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Sustainability & Governance (79/100) and lowest on AI Readiness (64/100). It was last updated 2 days ago. 2 contributors account for most of its recent work.

89
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.

89
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 75 is calibrated to 89 on the published index scale (record calibration 2026-08-02).

Ownership

MicrosoftOrganization · verified domain
129,859 followers8,308 public repossince Dec 2013

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIdbt-fabric1.11.1-4228 days ago

Metrics by category

Vitality

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

78Good · 21% of overall
How it's scored
36/36Push recencylast push 2 days ago
6.2/36Commit cadence9/52 weeks with commits
16.5/18Commit volume68 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 24 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year68
human_commit_share1
days_since_last_push2
active_weeks_last_year9
How it's scored
27/27Ships releases41 releases published
36/36Release recencylatest release 23 days ago
19.8/27Release cadencea release every ~53.7 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count41
latest_release_tagv1.11.2rc1
releases_from_tagsno
days_since_latest_release23
mean_days_between_releases53.7
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?

68Good · 17% of overall
How it's scored
35.2/60Stars148 stars
14.2/25Forks51 forks
5/15Watchers9 watchers
Inputs used
forks51
stars148
watchers9
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Sustainability & Governance

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

79Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
14.4/22.5Commit distributiontop contributor authored 36% of commits
13.5/13.5Contributor breadth29 contributors
10/10OpenSSF Scorecard: Contributorsproject has 11 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled29
top_contributor_share0.359
How it's scored
36.1/42Issue resolution86% of issues closed
7.2/30PR acceptance65/269 decided PRs merged
9.8/13Newcomer PR acceptance6/8 first-time contributors' PRs merged in 30d
9/15OpenSSF Scorecard: Code-ReviewFound 7/11 approved changesets -- score normalized to 6
Inputs used
merged_prs65
open_issues24
closed_issues148
prs_merged_7d6
prs_decided_7d6
prs_merged_30d9
prs_decided_30d12
issue_closed_ratio0.86
closed_unmerged_prs204
first_time_authors_30d4
first_time_prs_merged_30d6
first_time_prs_decided_30d8
How it's scored
30/30Ownership backingorganization-owned
20/20Verified domain
25/25Owner reach129,859 followers of microsoft
25/25Track record8,308 public repos, account ~12 yr old
Inputs used
followers129,859
owner_typeOrganization
is_verifiedyes
owner_loginmicrosoft
public_repos8,308
account_age_days4,663

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 28 days ago
20/20Version history42 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesdbt-fabric
ecosystemspypi
any_deprecatedno
min_days_since_publish28

Engineering Quality

Are baseline engineering and documentation practices in place?

72Good · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests11 out of 11 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
0/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_dirno
has_descriptionno

Security

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

78Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2.5/2.5CI-Tests11 out of 11 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
4.5/7.5Code-ReviewFound 7/11 approved changesets -- score normalized to 6
2.5/2.5Contributorsproject has 11 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 24 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
2.5/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
0/5SASTSAST tool is not run on all commits -- score normalized to 0
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
6/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
3/7.5Vulnerabilities6 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate7.3
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages67
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:dbt-fabric@1.11.1 runtime dependency closure — what installing the published package pulls in — 67 packages. Reachability is not analyzed.

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.

64Moderate · 4% of overall
How it's scored
0/45Agent instructionsno 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)
30.9/40Legible commit history58 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_share0.58
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environmentDockerfile, lockfile
10/10Demonstrated agent practice16 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenancedependency automation configured, none observed in the sampled commits
5/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.16
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/83 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes52,674
source_files_sampled83
oversized_source_files0

Key facts

148GitHub stars
29contributors
68commits, last 12 months
2days since last push
41releases
2bus factor
24open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'dbt-fabric' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 51 ⇿
0Stars
51Forks
39Releases

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.

010203040504922023-062025-012026-09
Major 0Minor 7Patch 26

Each point covers 3 days.

OpenSSF Scorecard 7.3 / 10
7.3aggregate

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 · 2026-09-17 08:50 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
10CI-Tests11 out of 11 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 7/11 approved changesets -- score normalized to 6
10Contributorsproject has 11 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 24 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
8Token-Permissionsdetected GitHub workflow tokens with excessive permissions
4Vulnerabilities6 existing vulnerabilities detected
Direct dependencies 7
RegistryPackageVersion constraintManifest
PyPImssql-python>=1.4.0pyproject.toml
PyPIazure-identity>=1.14.0pyproject.toml
PyPIazure-core>=1.26.0pyproject.toml
PyPIrequests>=2.33.0pyproject.toml
PyPIdbt-common>=1.0.4,<2.0pyproject.toml
PyPIdbt-core>=1.11.0pyproject.toml
PyPIdbt-adapters>=1.15.5,<2.0pyproject.toml
All dependencies 95

Full resolved dependency set from the GitHub dependency graph: 12 direct and 83 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIazure-coredirect
PyPIazure-core1.41.0direct
PyPIazure-identitydirect
PyPIazure-identity1.25.3direct
PyPIdbt-adaptersdirect
PyPIdbt-adapters1.23.0direct
PyPIdbt-commondirect
PyPIdbt-common1.39.0direct
PyPIdbt-coredirect
PyPIdbt-core1.11.13direct
PyPImssql-python1.10.0direct
PyPIrequests2.34.2direct
PyPIagate1.9.1indirect
PyPIannotated-types0.8.0indirect
PyPIattrs26.1.0indirect
PyPIbabel2.18.0indirect
PyPIbuildindirect
PyPIcertifi2026.7.22indirect
PyPIcffi2.1.1indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.5.1indirect
PyPIclick8.4.2indirect
PyPIcolorama0.4.6indirect
PyPIcryptography50.0.0indirect
PyPIdaff1.4.2indirect
PyPIdbt-extractor0.6.0indirect
PyPIdbt-protos1.0.541indirect
PyPIdbt-semantic-interfaces0.9.0indirect
PyPIdbt-tests-adapterindirect
PyPIdbt-tests-adapter1.20.0indirect
PyPIdeepdiff8.6.2indirect
PyPIdistlib0.4.3indirect
PyPIexecnet2.1.2indirect
PyPIfilelock3.32.3indirect
PyPIflaky3.7.0indirect
PyPIfreezegun1.5.5indirect
PyPIidentify2.6.19indirect
PyPIidna3.18indirect
PyPIimportlib-metadata8.9.0indirect
PyPIiniconfig2.3.0indirect
PyPIisodate0.7.2indirect
PyPIjinja23.1.6indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPIleather0.4.1indirect
PyPImarkupsafe3.0.3indirect
PyPImashumaro3.14indirect
PyPImore-itertools10.8.0indirect
PyPImsal1.37.0indirect
PyPImsal-extensions1.3.1indirect
PyPImsgpack1.2.1indirect
PyPImypy1.14.1indirect
PyPImypy-extensions1.1.0indirect
PyPInetworkx3.6.1indirect
PyPInodeenv1.10.0indirect
PyPIopentelemetry-api1.44.0indirect
PyPIorderly-set5.5.0indirect
PyPIpackaging26.3indirect
PyPIparsedatetime2.6indirect
PyPIpathspec0.12.1indirect
PyPIplatformdirs4.11.3indirect
PyPIpluggy1.6.0indirect
PyPIpre-commit3.8.0indirect
PyPIprotobuf6.33.6indirect
PyPIpycparser3.0indirect
PyPIpydantic2.13.4indirect
PyPIpydantic-core2.46.4indirect
PyPIpyjwt2.13.0indirect
PyPIpyodbcindirect
PyPIpytest8.0.1indirect
PyPIpytest-dotenv0.5.2indirect
PyPIpytest-xdist3.5.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-discovery1.5.2indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-slugify8.0.4indirect
PyPIpytimeparse1.1.8indirect
PyPIpytz2026.3.post1indirect
PyPIpyyaml6.0.3indirect
PyPIreferencing0.37.0indirect
PyPIrpds-py2026.6.3indirect
PyPIruff0.15.21indirect
PyPIsetuptoolsindirect
PyPIsix1.17.0indirect
PyPIsnowplow-tracker1.1.0indirect
PyPIsqlparse0.5.5indirect
PyPItext-unidecode1.3indirect
PyPItwine5.1.1indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.4indirect
PyPItzdata2026.3indirect
PyPIurllib32.7.0indirect
PyPIvirtualenv21.7.4indirect
PyPIwheel0.44.0indirect
PyPIzipp4.1.0indirect
Dependency advisories 0

Installing pypi:dbt-fabric@1.11.1 pulls in 67 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

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 statisticsPyPI.