Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-06 13:40 UTC

GT4SD / gt4sd-core

GT4SD, an open-source library to accelerate hypothesis generation in the scientific discovery process.

Jupyter Notebook · PythonMIT★ 378 stars⑂ 80 forkssince Feb 2022View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

GT4SD/gt4sd-core holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (81/100) and lowest on Security (53/100). It was last updated 25 days ago. A single contributor accounts for most of its recent work.

78
overall / 100
Good

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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

140 followers21 public repossince Feb 2022

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIgt4sd1.5.11748566 days agogt4sdgenerativemodelsinferencetraining

Metrics by category

Vitality

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

55Moderate · 21% of overall
How it's scored
28.8/36Push recency — last push 25 days ago
1.4/36Commit cadence — 2/52 weeks with commits
4.3/18Commit volume — 2 commits in the last year
1/10OpenSSF Scorecard: Maintained — 2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
Inputs used
commits_last_year2
human_commit_share1
days_since_last_push25
active_weeks_last_year2
How it's scored
27/27Ships releases — 61 releases published
36/36Release recency — latest release 66 days ago
12.6/27Release cadence — a release every ~131.4 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count61
latest_release_tagv1.5.1
releases_from_tagsno
days_since_latest_release66
mean_days_between_releases131.4
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?

74Good · 17% of overall
How it's scored
41.8/60Stars — 378 stars
15.8/25Forks — 80 forks
6.4/15Watchers — 15 watchers
Inputs used
forks80
stars378
watchers15
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized 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_badges7
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, github.com, shields.io
has_pull_request_templateno

Sustainability & Governance

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

70Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
9.5/22.5Commit distribution — top contributor authored 58% of commits
13.5/13.5Contributor breadth — 17 contributors
10/10OpenSSF Scorecard: Contributors — project has 10 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled17
top_contributor_share0.58
How it's scored
42/42Issue resolution — 100% of issues closed
26.3/30PR acceptance — 137/156 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
4.5/15OpenSSF Scorecard: Code-Review — Found 10/30 approved changesets -- score normalized to 3
Inputs used
merged_prs137
open_issues0
closed_issues113
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio1
closed_unmerged_prs19
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
15.5/25Owner reach — 140 followers of GT4SD
19.1/25Track record — 21 public repos, account ~4 yr old
Inputs used
followers140
owner_typeOrganization
is_verifiedno
owner_loginGT4SD
public_repos21
account_age_days1,701

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 66 days ago
20/20Version history — 85 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesgt4sd
ecosystemspypi
any_deprecatedno
min_days_since_publish66

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.black]), setup.cfg ([flake8])
0/9.6Pre-commit hooks
0/6.4.editorconfig
4/20OpenSSF Scorecard: CI-Tests — 4 out of 15 merged PRs checked by a CI test -- score normalized to 2
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://gt4sd.github.io/gt4sd-core/
10/10Repository description
10/10Topics — 4 topics
10/10Wiki
Inputs used
topicsmachine-learning, python, deep-learning, generative-models
has_wikiyes
homepagehttps://gt4sd.github.io/gt4sd-core/
docs_sitehttps://gt4sd.github.io/gt4sd-core/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

53Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
0.5/2.5CI-Tests — 4 out of 15 merged PRs checked by a CI test -- score normalized to 2
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
2.2/7.5Code-Review — Found 10/30 approved changesets -- score normalized to 3
2.5/2.5Contributors — project has 10 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
0.8/7.5Maintained — 2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
6/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 27 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.1
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 advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories14
affected_packages5
assessed_packages24
unassessed_packages59
affected_by_severitycritical 1, high 1, moderate 2, low 1
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 24 resolved dependencies against OSV. 59 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

68Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 97 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.97
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.black]), setup.cfg ([flake8])
11/11Static type checking — src/gt4sd/py.typed
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/gt4sd/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Jupyter Notebook with type-check config (src/gt4sd/py.typed)
55/55Manageable file sizes — 0/285 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes53,256
source_files_sampled285
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

378GitHub stars
17contributors
2commits, last 12 months
25days since last push
61releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 80 ⇿
0Stars
80Forks
60Releases

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.

0204060807852022-022024-052026-07
Major 1Minor 24Patch 35

Each point covers 5 days.

OpenSSF Scorecard 4.1 / 10
4.1aggregate

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 · 2026-10-06 13:40 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
2CI-Tests4 out of 15 merged PRs checked by a CI test -- score normalized to 2
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3Code-ReviewFound 10/30 approved changesets -- score normalized to 3
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
1Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
8Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities27 existing vulnerabilities detected
Direct dependencies 45
RegistryPackageVersion constraintManifest
PyPIaccelerate—setup.cfg
PyPIdatasets—setup.cfg
PyPIdiffusers—setup.cfg
PyPIenzeptional—setup.cfg
PyPIimportlib_metadata—setup.cfg
PyPIimportlib_resources—setup.cfg
PyPIipaddress—setup.cfg
PyPIjoblib—setup.cfg
PyPIgt4sd-molformer—setup.cfg
PyPIgt4sd-trainer-hf-pl—setup.cfg
PyPIkeras—setup.cfg
PyPIkeybert—setup.cfg
PyPIminio—setup.cfg
PyPImodlamp—setup.cfg
PyPImolecule_generation—setup.cfg
PyPImolgx—setup.cfg
PyPInglview—setup.cfg
PyPInumpy—setup.cfg
PyPIpytorch_lightning—setup.cfg
PyPIpyarrow—setup.cfg
PyPIpydantic—setup.cfg
PyPIpymatgen—setup.cfg
PyPIpyTDC!=0.3.8setup.cfg
PyPIpyyaml—setup.cfg
PyPIrdkit—setup.cfg
PyPIregex—setup.cfg
PyPIreinvent-chemistry—setup.cfg
PyPIsacremoses—setup.cfg
PyPIscikit-learn—setup.cfg
PyPIscikit-optimize—setup.cfg
PyPIscipy—setup.cfg
PyPIsentencepiece—setup.cfg
PyPIsympy—setup.cfg
PyPItables—setup.cfg
PyPItape-proteins—setup.cfg
PyPItensorboard—setup.cfg
PyPItensorflow—setup.cfg
PyPItorch—setup.cfg
PyPItorchdrug—setup.cfg
PyPItorchmetrics—setup.cfg
PyPItorchvision—setup.cfg
PyPItransformers<4.26.0setup.cfg
PyPItyping_extensions—setup.cfg
PyPIwheel—setup.cfg
PyPIxgboost—setup.cfg
All dependencies 83

Full resolved dependency set from the GitHub dependency graph: 43 direct and 40 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaccelerate—direct
PyPIdatasets—direct
PyPIdiffusers—direct
PyPIenzeptional—direct
PyPIgt4sd-molformer—direct
PyPIgt4sd-trainer-hf-pl—direct
PyPIimportlib-resources—direct
PyPIipaddress—direct
PyPIjoblib—direct
PyPIkeras—direct
PyPIkeybert—direct
PyPIminio7.0.1direct
PyPImodlamp—direct
PyPImolgx—direct
PyPInglview—direct
PyPInumpy—direct
PyPIpyarrow—direct
PyPIpydantic—direct
PyPIpymatgen—direct
PyPIpytdc0.3.7direct
PyPIpytorch-lightning—direct
PyPIpyyaml—direct
PyPIrdkit—direct
PyPIregex—direct
PyPIreinvent-chemistry0.0.38direct
PyPIsacremoses—direct
PyPIscikit-learn—direct
PyPIscikit-optimize—direct
PyPIscipy—direct
PyPIsentencepiece—direct
PyPIsympy—direct
PyPItables—direct
PyPItape-proteins—direct
PyPItensorboard—direct
PyPItensorflow—direct
PyPItorch—direct
PyPItorchdrug—direct
PyPItorchmetrics—direct
PyPItorchvision—direct
PyPItransformers—direct
PyPItyping-extensions—direct
PyPIwheel—direct
PyPIxgboost—direct
PyPIanyio—indirect
PyPIbetter-apidoc0.3.2indirect
PyPIblack22.3.0indirect
PyPIcogmol-inference0.6.0indirect
PyPIdocutils0.17.1indirect
PyPIflake83.8.4indirect
PyPIflask1.1.2indirect
PyPIflask-login0.5.0indirect
PyPIgt4sd—indirect
PyPIhuggingface-hub—indirect
PyPIipykernel—indirect
PyPIipywidgets—indirect
PyPIjinja2—indirect
PyPIjupyter1.0.0indirect
PyPIlicenseheaders0.8.8indirect
PyPImarkdown-it-py—indirect
PyPImols2grid0.2.0indirect
PyPImypy1.0.0indirect
PyPImyst-parser1.0.0indirect
PyPIpandas—indirect
PyPIpolling20.5.0indirect
PyPIprotobuf—indirect
PyPIpydantic-settings—indirect
PyPIpytest6.2.5indirect
PyPIpytest-cov2.10.1indirect
PyPIrdkit-stubs—indirect
PyPIrxn4chemistry1.6.1indirect
PyPIseaborn0.11.2indirect
PyPIsentence-transformers—indirect
PyPIsetuptools78.1.1indirect
PyPIsphinx—indirect
PyPIsphinx-autodoc-typehints1.11.1indirect
PyPIsphinx-rtd-theme0.5.1indirect
PyPItensorboard-data-server—indirect
PyPItensorflow-io-gcs-filesystem—indirect
PyPItorch-cluster—indirect
PyPItorch-geometric—indirect
PyPItorch-sparse—indirect
PyPItqdm4.62.3indirect
PyPIuvloop—indirect
Dependency advisories 5

This repository publishes no package the index resolves, so its own dependency graph was assessed — 24 packages, which also include development and test pins that never ship: 5 carry known advisories, of which 0 are direct. 59 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
black22.3.0indirectcritical426.3.1
flask1.1.2indirecthigh43.1.3
pytest6.2.5indirectmoderate29.0.3
setuptools78.1.1indirectmoderate283.0.0
tqdm4.62.3indirectlow24.66.3

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 statistics — PyPI.