Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-12 10:13 UTC

Nixtla / mlforecast

Scalable machine 🤖 learning for time series forecasting.

PythonApache-2.0★ 1,277 stars⑂ 132 forkssince Apr 2021View on GitHub ↗
KindLibraryhow this is determined
#forecast#forecasting#machine-learning#lightgbm#xgboost#dask#python#time-series

Nixtla/mlforecast holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Vitality (92/100) and lowest on Security (74/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

96
overall / 100
Exceptional

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.

96
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 84 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

NixtlaOrganization · verified domain
2,849 followers41 public repossince Mar 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImlforecast1.1.0381,3765864 days agopythonforecastforecastingmachine-learningdask

Metrics by category

Vitality

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

92Excellent · 21% of overall

Development activity

92Excellent
How it's scored
36/36Push recency — last push 1 days ago
28.4/36Commit cadence — 41/52 weeks with commits
18/18Commit volume — 152 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year152
human_commit_share0.51
days_since_last_push1
active_weeks_last_year41

Release discipline

92Excellent
How it's scored
27/27Ships releases — 44 releases published
36/36Release recency — latest release 64 days ago
19.8/27Release cadence — a release every ~67.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count44
latest_release_tagv1.1.0
releases_from_tagsno
days_since_latest_release64
mean_days_between_releases67.6
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?

85Excellent · 17% of overall

Popularity & adoption

73Good
How it's scored
50.4/60Stars — 1,277 stars
17.6/25Forks — 132 forks
5/15Watchers — 9 watchers
Inputs used
forks132
stars1,277
watchers9
growth_stateorganic
growth_factor_pct100

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges7
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

93Exceptional
How it's scored
74.4/80Monthly downloads — 381,376 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmlforecast
dependents—
ecosystemspypi
total_downloads—
monthly_downloads381,376
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

78Good · 23% of overall

Maintainer resilience (bus factor)

40Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
7.2/22.5Commit distribution — top contributor authored 68% of commits
13.5/13.5Contributor breadth — 27 contributors
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled27
top_contributor_share0.679

Issue & PR responsiveness

88Excellent
How it's scored
40/42Issue resolution — 95% of issues closed
26.5/30PR acceptance — 389/440 decided PRs merged
6.5/13Newcomer PR acceptance — 1/2 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs389
open_issues10
closed_issues203
prs_merged_7d4
prs_decided_7d4
prs_merged_30d7
prs_decided_30d12
issue_closed_ratio0.953
closed_unmerged_prs51
first_time_authors_30d2
first_time_prs_merged_30d1
first_time_prs_decided_30d2

Ownership & stewardship

98Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
24.8/25Owner reach — 2,849 followers of Nixtla
22.9/25Track record — 41 public repos, account ~5 yr old
Inputs used
followers2,849
owner_typeOrganization
is_verifiedyes
owner_loginNixtla
public_repos41
account_age_days2,019

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 64 days ago
20/20Version history — 58 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmlforecast
ecosystemspypi
any_deprecatedno
min_days_since_publish64

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 10 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 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

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://nixtlaverse.nixtla.io/mlforecast
10/10Repository description
10/10Topics — 8 topics
0/10Wiki
Inputs used
topicsforecast, forecasting, machine-learning, lightgbm, xgboost, dask, python, time-series
has_wikino
homepagehttps://nixtlaverse.nixtla.io/mlforecast
docs_sitehttps://nixtlaverse.nixtla.io/mlforecast
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

74Good · 16% of overall

Security posture

67Good
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
4.5/5SAST — SAST tool is not run on all commits -- score normalized to 9
0/5Security-Policy — security policy file not 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_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.6
Excluded from scoring (no data or not applicable): Packaging, 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
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages24
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:mlforecast@1.1.0 runtime dependency closure — what installing the published package pulls in — 24 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.

78Good · 4% of overall

Agent context & guidance

55Moderate
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://nixtlaverse.nixtla.io/llms.txt)
40/40Legible commit history — 44 of 51 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://nixtlaverse.nixtla.io/llms.txt
legible_history_share0.863
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

88Excellent
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — mlforecast/py.typed
10/10Reproducible environment — lockfile
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 49 of the last 100 commits are automated dependency updates
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configsmlforecast/py.typed
agent_commit_share0.02
toolchain_manifests—
dependency_bot_commit_share0.49

Code legibility for models

78Good
How it's scored
27/45Type-checkable code — Python with type-check config (mlforecast/py.typed)
50.5/55Manageable file sizes — 6/73 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes178,001
source_files_sampled73
oversized_source_files6

Machine-readable interfaces

100Exceptional
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 — notebooks
Inputs used
example_dirsnotebooks
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

1,277GitHub stars
27contributors
152commits, last 12 months
1days since last push
44releases
1bus factor
10open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Star history carried forward from 2026-07-17: GitHub restricted the stargazers API to repository admins, so it can no longer be collected. The repository has 1277 stars today.

More detail

Star and fork history 1,254 ★ / 132 ⇿
1,254Stars
132Forks
44Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

Star history is shown as collected on 2026-07-17. GitHub restricted the stargazers API to repository administrators in July 2026, so this series can no longer be extended; the current star total above remains live.

02505007501,0001,2501,5001,254132162021-062024-012026-09
Major 1Minor 14Patch 29

Each point covers 5 days.

OpenSSF Scorecard 6.6 / 10
6.6aggregate

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 · 2026-09-12 10:12 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 6 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 8 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
9SASTSAST tool is not run on all commits -- score normalized to 9
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
5Vulnerabilities5 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
PyPIcloudpickle—pyproject.toml
PyPIcoreforecast>=0.0.15pyproject.toml
PyPIfsspec—pyproject.toml
PyPInarwhals—pyproject.toml
PyPIoptuna—pyproject.toml
PyPIpandas<3.0pyproject.toml
PyPIscikit-learn—pyproject.toml
PyPIutilsforecast>=0.2.9pyproject.toml
All dependencies 221

Full resolved dependency set from the GitHub dependency graph: 9 direct and 212 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcloudpickle3.1.2direct
PyPIcoreforecast0.0.18direct
PyPIfsspec2026.1.0direct
PyPInarwhals2.15.0direct
PyPIoptuna4.7.0direct
PyPIpandas2.3.3direct
PyPIscikit-learn1.7.2direct
PyPIscikit-learn1.8.0direct
PyPIutilsforecast0.2.15direct
PyPIadagio0.2.6indirect
PyPIadlfs2025.8.0indirect
PyPIaiobotocore3.1.1indirect
PyPIaiohappyeyeballs2.6.1indirect
PyPIaiohttp3.14.3indirect
PyPIaioitertools0.13.0indirect
PyPIaiosignal1.4.0indirect
PyPIalembic1.18.1indirect
PyPIannotated-doc0.0.4indirect
PyPIannotated-types0.7.0indirect
PyPIanyio4.12.1indirect
PyPIasync-timeout5.0.1indirect
PyPIattrs25.4.0indirect
PyPIazure-core1.38.0indirect
PyPIazure-datalake-store0.0.53indirect
PyPIazure-identity1.25.1indirect
PyPIazure-storage-blob12.28.0indirect
PyPIblinker1.9.0indirect
PyPIbokeh3.8.2indirect
PyPIbotocore1.42.30indirect
PyPIcachetools6.2.4indirect
PyPIcatboost1.2.10indirect
PyPIcertifi2026.1.4indirect
PyPIcffi2.0.0indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.4.4indirect
PyPIclick8.3.1indirect
PyPIcolorama0.4.6indirect
PyPIcolorlog6.10.1indirect
PyPIcontourpy1.3.2indirect
PyPIcontourpy1.3.3indirect
PyPIconvertdate2.4.0indirect
PyPIcoverage7.13.1indirect
PyPIcryptography48.0.1indirect
PyPIcycler0.12.1indirect
PyPIdask2026.1.1indirect
PyPIdatabricks-sdk0.80.0indirect
PyPIdatasetsforecast1.0.1indirect
PyPIdecorator5.2.1indirect
PyPIdistlib0.4.0indirect
PyPIdistributed2026.1.1indirect
PyPIdocker7.1.0indirect
PyPIduckdb1.4.3indirect
PyPIexceptiongroup1.3.1indirect
PyPIexecnet2.1.2indirect
PyPIfastapi0.136.3indirect
PyPIfilelock3.20.3indirect
PyPIflask3.1.3indirect
PyPIflask-cors6.0.2indirect
PyPIfonttools4.61.1indirect
PyPIfrozenlist1.8.0indirect
PyPIfugue0.9.4indirect
PyPIgcsfs2026.1.0indirect
PyPIgitdb4.0.12indirect
PyPIgitpython3.1.59indirect
PyPIgoogle-api-core2.29.0indirect
PyPIgoogle-auth2.47.0indirect
PyPIgoogle-auth-oauthlib1.2.4indirect
PyPIgoogle-cloud-core2.5.0indirect
PyPIgoogle-cloud-storage3.8.0indirect
PyPIgoogle-cloud-storage-control1.9.0indirect
PyPIgoogle-crc32c1.8.0indirect
PyPIgoogle-resumable-media2.8.0indirect
PyPIgoogleapis-common-protos1.72.0indirect
PyPIgraphene3.4.3indirect
PyPIgraphql-core3.2.7indirect
PyPIgraphql-relay3.2.0indirect
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PyPIgreenlet3.3.0indirect
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PyPIgrpcio-status1.76.0indirect
PyPIgunicorn23.0.0indirect
PyPIh110.16.0indirect
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PyPIhijridate2.3.0indirect
PyPIholidays0.20indirect
PyPIhuey2.6.0indirect
PyPIidentify2.6.16indirect
PyPIidna3.15indirect
PyPIimportlib-metadata8.7.1indirect
PyPIiniconfig2.3.0indirect
PyPIisodate0.7.2indirect
PyPIitsdangerous2.2.0indirect
PyPIjinja23.1.6indirect
PyPIjmespath1.1.0indirect
PyPIjoblib1.5.3indirect
PyPIjsonschema4.26.0indirect
PyPIjsonschema-specifications2025.9.1indirect
PyPIkiwisolver1.4.9indirect
PyPIkorean-lunar-calendar0.3.1indirect
PyPIlibrt0.7.8indirect
PyPIlightgbm4.6.0indirect
PyPIlocket1.0.0indirect
PyPIlz44.4.5indirect
PyPImako1.3.12indirect
PyPImarkdown-it-py4.0.0indirect
PyPImarkupsafe3.0.3indirect
PyPImatplotlib3.10.8indirect
PyPImdformat1.0.0indirect
PyPImdurl0.1.2indirect
PyPImkdocstrings-parser0.0.1indirect
PyPImlflow3.15.0indirect
PyPImlflow-skinny3.15.0indirect
PyPImlflow-tracing3.15.0indirect
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PyPImsal1.34.0indirect
PyPImsal-extensions1.3.1indirect
PyPImsgpack1.2.1indirect
PyPImultidict6.7.0indirect
PyPImypy1.19.1indirect
PyPImypy-extensions1.1.0indirect
PyPInodeenv1.10.0indirect
PyPInumpy2.2.6indirect
PyPInumpy2.4.1indirect
PyPInvidia-nccl-cu122.31.2indirect
PyPInvidia-nccl-cu132.31.2indirect
PyPIoauthlib3.3.1indirect
PyPIopentelemetry-api1.39.1indirect
PyPIopentelemetry-proto1.39.1indirect
PyPIopentelemetry-sdk1.39.1indirect
PyPIopentelemetry-semantic-conventions0.60b1indirect
PyPIpackaging25.0indirect
PyPIpartd1.4.2indirect
PyPIpathspec1.0.3indirect
PyPIpatsy1.0.2indirect
PyPIpillow12.3.0indirect
PyPIpip-licenses5.5.0indirect
PyPIplatformdirs4.5.1indirect
PyPIplotly6.7.0indirect
PyPIpluggy1.6.0indirect
PyPIpolars1.31.0indirect
PyPIpre-commit4.5.1indirect
PyPIprettytable3.17.0indirect
PyPIpropcache0.4.1indirect
PyPIproto-plus1.27.0indirect
PyPIprotobuf6.33.5indirect
PyPIpsutil7.2.1indirect
PyPIpy-cpuinfo9.0.0indirect
PyPIpy4j0.10.9.9indirect
PyPIpyarrow23.0.1indirect
PyPIpyasn10.6.4indirect
PyPIpyasn1-modules0.4.2indirect
PyPIpycparser3.0indirect
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PyPIpydantic2.13.5indirect
PyPIpydantic-core2.41.5indirect
PyPIpydantic-core2.46.5indirect
PyPIpygments2.20.0indirect
PyPIpyjwt2.13.0indirect
PyPIpymeeus0.5.12indirect
PyPIpyparsing3.3.2indirect
PyPIpyspark4.1.1indirect
PyPIpytest9.0.3indirect
PyPIpytest-benchmark5.2.3indirect
PyPIpytest-codspeed4.2.0indirect
PyPIpytest-cov7.0.0indirect
PyPIpytest-rerunfailures16.1indirect
PyPIpytest-timeout2.4.0indirect
PyPIpytest-xdist3.8.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-dotenv1.2.2indirect
PyPIpytz2025.2indirect
PyPIpywin32311indirect
PyPIpyyaml6.0.3indirect
PyPIray2.56.1indirect
PyPIreferencing0.37.0indirect
PyPIrequests2.33.0indirect
PyPIrequests-oauthlib2.0.0indirect
PyPIrich14.2.0indirect
PyPIrpds-py0.30.0indirect
PyPIrsa4.9.1indirect
PyPIruff0.14.14indirect
PyPIs3fs2026.1.0indirect
PyPIscipy1.15.3indirect
PyPIscipy1.17.0indirect
PyPIsetuptools83.0.0indirect
PyPIsix1.17.0indirect
PyPIskops0.13.0indirect
PyPIsmmap5.0.2indirect
PyPIsortedcontainers2.4.0indirect
PyPIsqlalchemy2.0.46indirect
PyPIsqlparse0.6.0indirect
PyPIstarlette1.3.1indirect
PyPIstatsmodels0.14.6indirect
PyPItblib3.2.2indirect
PyPItensorboardx2.6.5indirect
PyPIthreadpoolctl3.6.0indirect
PyPItomli2.4.0indirect
PyPItoolz1.1.0indirect
PyPItornado6.5.8indirect
PyPItqdm4.67.1indirect
PyPItriad1.0.0indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.3indirect
PyPIurllib32.7.0indirect
PyPIuvicorn0.40.0indirect
PyPIvirtualenv20.36.1indirect
PyPIwaitress3.0.2indirect
PyPIwcwidth0.3.1indirect
PyPIwerkzeug3.1.6indirect
PyPIwrapt2.0.1indirect
PyPIxgboost3.2.0indirect
PyPIxgboost3.4.1indirect
PyPIxlrd2.0.2indirect
PyPIxyzservices2025.11.0indirect
PyPIyarl1.22.0indirect
PyPIzict3.0.0indirect
PyPIzipp3.23.0indirect
Dependency advisories 0

Installing pypi:mlforecast@1.1.0 pulls in 24 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

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