Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-27 21:43 UTC

Nixtla / statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.

PythonApache-2.0★ 4,889 stars⑂ 384 forkssince Nov 2021View on GitHub ↗

Nixtla/statsforecast holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (96/100) and lowest on Sustainability & Governance (77/100). It was last updated today. A single contributor accounts for most of its recent work.

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.

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)

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 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

NixtlaOrganization · verified domain

2,845 followers40 public repossince Mar 2021

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

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish	Tags
PyPI	statsforecast	2.1.1	1,883,713	43	42 days ago	time-series forecasting arima ets

Metrics by category

Vitality

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

88Excellent · 21% of overall

Development activity

86Excellent

How it's scored

36/36	Push recency — last push 0 days ago
22.8/36	Commit cadence — 33/52 weeks with commits
17.3/18	Commit volume — 83 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	83
human_commit_share	0.68
days_since_last_push	0
active_weeks_last_year	33

Release discipline

92Excellent

How it's scored

27/27	Ships releases — 40 releases published
36/36	Release recency — latest release 42 days ago
19.8/27	Release cadence — a release every ~81 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	40
latest_release_tag	v2.1.1
releases_from_tags	no
days_since_latest_release	42
mean_days_between_releases	81

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?

91Excellent · 17% of overall

Popularity & adoption

90Excellent

How it's scored

59.8/60	Stars — 4,889 stars
21.5/25	Forks — 384 forks
9/15	Watchers — 42 watchers

Inputs used

forks	384
stars	4,889
watchers	42
growth_state	organic
growth_factor_pct	100

Community health

85Excellent

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (Apache-2.0)
18/18	CONTRIBUTING guide
13.5/13.5	Code of conduct
0/7.2	Issue template
0/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	9
has_contributing	yes
has_issue_template	no
has_code_of_conduct	yes
readme_badge_services	github.com, shields.io
has_pull_request_template	no

Ecosystem adoption (downloads)

100Exceptional

How it's scored

80/80	Monthly downloads — 1,883,713 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	statsforecast
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	1,883,713
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?

77Good · 23% of overall

Maintainer resilience (bus factor)

40Weak

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
7.4/22.5	Commit distribution — top contributor authored 67% of commits
13.5/13.5	Contributor breadth — 62 contributors
10/10	OpenSSF Scorecard: Contributors — project has 7 contributing companies or organizations

Inputs used

bus_factor	1
contributors_sampled	62
top_contributor_share	0.671

Issue & PR responsiveness

83Excellent

How it's scored

29.6/42	Issue resolution — 70% of issues closed
25.5/30	PR acceptance — 555/652 decided PRs merged
13/13	Newcomer PR acceptance — 2/2 first-time contributors' PRs merged in 30d
15/15	OpenSSF Scorecard: Code-Review — all changesets reviewed

Inputs used

merged_prs	555
open_issues	118
closed_issues	282
prs_merged_7d	1
prs_decided_7d	1
prs_merged_30d	5
prs_decided_30d	5
issue_closed_ratio	0.705
closed_unmerged_prs	97
first_time_authors_30d	2
first_time_prs_merged_30d	2
first_time_prs_decided_30d	2

Ownership & stewardship

98Exceptional

How it's scored

30/30	Ownership backing — organization-owned
20/20	Verified domain
24.8/25	Owner reach — 2,845 followers of Nixtla
22.7/25	Track record — 40 public repos, account ~5 yr old

Inputs used

followers	2,845
owner_type	Organization
is_verified	yes
owner_login	Nixtla
public_repos	40
account_age_days	2,003

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 42 days ago
20/20	Version history — 43 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	statsforecast
ecosystems	pypi
any_deprecated	no
min_days_since_publish	42

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall

Engineering practices

94Exceptional

How it's scored

24/24	CI workflows — 8 workflow(s)
24/24	Tests present
16/16	Linter config — pyproject.toml ([tool.ruff])
9.6/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	yes

Documentation

100Exceptional

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://nixtlaverse.nixtla.io/statsforecast
10/10	Repository description
10/10	Topics — 20 topics
10/10	Wiki

Inputs used

topics	time-series, statistics, forecasting, arima, econometrics, machine-learning, python, exponential-smoothing, ets, baselines, predictions, naive, seasonal-naive, fbprophet, prophet, neuralprophet, mstl, theta, automl, data-science
has_wiki	yes
homepage	https://nixtlaverse.nixtla.io/statsforecast
docs_site	https://nixtlaverse.nixtla.io/statsforecast
has_readme	yes
has_docs_dir	yes
has_description	yes

Security

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

82Excellent · 16% of overall

Security posture

78Good

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
3.8/7.5	Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5	CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5	CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5	Code-Review — all changesets reviewed
2.5/2.5	Contributors — project has 7 contributing companies or organizations
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5	Dependency-Update-Tool — update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
7.5/7.5	Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5	Packaging — packaging workflow detected
3.5/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
3.5/5	SAST — SAST tool is not run on all commits -- score normalized to 7
0/5	Security-Policy — security policy file not detected
0/7.5	Signed-Releases — no data
6.8/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
6/7.5	Vulnerabilities — 2 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	17
scorecard_version	v5.5.0
checks_inconclusive	1
scorecard_aggregate	7.8

Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25	Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	21
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:statsforecast@2.1.1 runtime dependency closure — what installing the published package pulls in — 21 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

0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15	Machine-readable docs (llms.txt) — llms.txt served by the project website (https://nixtlaverse.nixtla.io/llms.txt)
40/40	Legible commit history — 63 of 68 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	yes
llms_txt_url	https://nixtlaverse.nixtla.io/llms.txt
legible_history_share	0.926
agent_instruction_files	—
agent_instruction_max_bytes	—

Verify loop (build / test / typecheck)

87Excellent

How it's scored

18/18	One-command bootstrap — Makefile, experiments/arima/Makefile, experiments/arima_xreg/Makefile, experiments/ces/Makefile, experiments/vn1-competition/Makefile
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.ruff])
11/11	Static type checking — .mypy.ini, python/statsforecast/py.typed
10/10	Reproducible environment — Dockerfile, lockfile
0/10	Demonstrated agent practice — no agent-authored commits among the last 100
8/8	Automated maintenance — 32 of the last 100 commits are automated dependency updates
7/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7

Inputs used

has_nix	no
has_tests	yes
lockfiles	uv.lock
has_dockerfile	yes
typed_language	no
bootstrap_files	Makefile, experiments/arima/Makefile, experiments/arima_xreg/Makefile, experiments/ces/Makefile, experiments/vn1-competition/Makefile
has_devcontainer	no
has_linter_config	yes
typecheck_configs	.mypy.ini, python/statsforecast/py.typed
agent_commit_share	0
toolchain_manifests	—
dependency_bot_commit_share	0.32

Code legibility for models

80Excellent

How it's scored

27/45	Type-checkable code — Python with type-check config (.mypy.ini, python/statsforecast/py.typed)
53.1/55	Manageable file sizes — 4/116 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	274,631
source_files_sampled	116
oversized_source_files	4

Machine-readable interfaces

100Exceptional

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20	MCP server — not applicable to this kind of software
40/40	Runnable examples — notebooks

Inputs used

example_dirs	notebooks
has_mcp_signal	no
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

4,889GitHub stars

62contributors

83commits, last 12 months

0days since last push

40releases

1bus factor

118open issues

PyPIpackage ecosystems

Data collection warnings

Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
Star history carried forward from 2026-07-20: GitHub restricted the stargazers API to repository admins, so it can no longer be collected. The repository has 4889 stars today.
Could not fetch pypi package 'statsforecast._lib' from its registry

More detail

Star and fork history 4,845 ★ / 384 ⇿

4,845Stars

384Forks

40Releases

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 2026-07-20. 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.

Major 2Minor 12Patch 25

Each point covers 5 days.

OpenSSF Scorecard 7.8 / 10

7.8aggregate

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 · 2026-08-27 21:42 UTC

10	Binary-Artifacts	no binaries found in the repo
5	Branch-Protection	branch protection is not maximal on development and all release branches
10	CI-Tests	30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0	CII-Best-Practices	no effort to earn an OpenSSF best practices badge detected
10	Code-Review	all changesets reviewed
10	Contributors	project has 7 contributing companies or organizations
10	Dangerous-Workflow	no dangerous workflow patterns detected
10	Dependency-Update-Tool	update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
10	Maintained	30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10	Packaging	packaging workflow detected
7	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 7
7	SAST	SAST tool is not run on all commits -- score normalized to 7
0	Security-Policy	security policy file not detected
n/a	Signed-Releases	no releases found
9	Token-Permissions	detected GitHub workflow tokens with excessive permissions
8	Vulnerabilities	2 existing vulnerabilities detected

Direct dependencies 10

Registry	Package	Version constraint	Manifest
PyPI	cloudpickle	`—`	pyproject.toml
PyPI	coreforecast	`>=0.0.17`	pyproject.toml
PyPI	numpy	`>=1.21.6`	pyproject.toml
PyPI	pandas	`<3.0.0`	pyproject.toml
PyPI	scipy	`>=1.7.3`	pyproject.toml
PyPI	statsmodels	`>=0.14.5`	pyproject.toml
PyPI	tqdm	`—`	pyproject.toml
PyPI	fugue	`>=0.9.4`	pyproject.toml
PyPI	utilsforecast	`>=0.1.4`	pyproject.toml
PyPI	threadpoolctl	`>=3`	pyproject.toml

All dependencies 144

Full resolved dependency set from the GitHub dependency graph: 13 direct and 131 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	cloudpickle	`3.1.2`	direct
PyPI	coreforecast	`0.0.17`	direct
PyPI	fugue	`0.9.4`	direct
PyPI	numpy	`1.26.4`	direct
PyPI	numpy	`2.3.5`	direct
PyPI	pandas	`2.3.3`	direct
PyPI	scipy	`1.15.3`	direct
PyPI	scipy	`1.17.0`	direct
PyPI	statsmodels	`0.14.6`	direct
PyPI	threadpoolctl	`3.6.0`	direct
PyPI	tqdm	`4.67.1`	direct
PyPI	utilsforecast	`0.2.12`	direct
PyPI	utilsforecast	`0.2.15`	direct
PyPI	adagio	`0.2.6`	indirect
PyPI	aiohappyeyeballs	`2.6.1`	indirect
PyPI	aiohttp	`3.14.3`	indirect
PyPI	aiosignal	`1.4.0`	indirect
PyPI	async-timeout	`5.0.1`	indirect
PyPI	attrs	`25.4.0`	indirect
PyPI	beautifulsoup4	`4.14.3`	indirect
PyPI	blinker	`1.9.0`	indirect
PyPI	certifi	`2026.1.4`	indirect
PyPI	cffi	`2.0.0`	indirect
PyPI	cfgv	`3.5.0`	indirect
PyPI	charset-normalizer	`3.4.4`	indirect
PyPI	click	`8.3.1`	indirect
PyPI	cmdstanpy	`1.3.0`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	contourpy	`1.3.2`	indirect
PyPI	contourpy	`1.3.3`	indirect
PyPI	coverage	`7.13.1`	indirect
PyPI	curl-cffi	`0.13.0`	indirect
PyPI	cycler	`0.12.1`	indirect
PyPI	dash	`3.4.0`	indirect
PyPI	dask	`2026.1.2`	indirect
PyPI	datasetsforecast	`1.0.1`	indirect
PyPI	distlib	`0.4.0`	indirect
PyPI	distributed	`2026.1.2`	indirect
PyPI	duckdb	`1.4.3`	indirect
PyPI	exceptiongroup	`1.3.1`	indirect
PyPI	filelock	`3.20.3`	indirect
PyPI	fire	`0.7.1`	indirect
PyPI	flask	`3.1.3`	indirect
PyPI	fonttools	`4.61.1`	indirect
PyPI	frozendict	`2.4.7`	indirect
PyPI	frozenlist	`1.8.0`	indirect
PyPI	fsspec	`2026.1.0`	indirect
PyPI	griffe	`1.15.0`	indirect
PyPI	griffe2md	`1.2.6`	indirect
PyPI	holidays	`0.89`	indirect
PyPI	identify	`2.6.16`	indirect
PyPI	idna	`3.15`	indirect
PyPI	importlib-metadata	`8.7.1`	indirect
PyPI	importlib-resources	`6.5.2`	indirect
PyPI	iniconfig	`2.3.0`	indirect
PyPI	ipykernel	`—`	indirect
PyPI	itsdangerous	`2.2.0`	indirect
PyPI	jinja2	`3.1.6`	indirect
PyPI	joblib	`1.5.3`	indirect
PyPI	jsonschema	`4.26.0`	indirect
PyPI	jsonschema-specifications	`2025.9.1`	indirect
PyPI	kiwisolver	`1.4.9`	indirect
PyPI	locket	`1.0.0`	indirect
PyPI	markdown-it-py	`4.0.0`	indirect
PyPI	markupsafe	`3.0.3`	indirect
PyPI	matplotlib	`3.10.8`	indirect
PyPI	mdformat	`1.0.0`	indirect
PyPI	mdurl	`0.1.2`	indirect
PyPI	mkdocstrings-parser	`0.0.1`	indirect
PyPI	msgpack	`1.2.1`	indirect
PyPI	multidict	`6.7.0`	indirect
PyPI	multitasking	`0.0.12`	indirect
PyPI	narwhals	`2.15.0`	indirect
PyPI	nest-asyncio	`1.6.0`	indirect
PyPI	nodeenv	`1.10.0`	indirect
PyPI	orjson	`3.11.6`	indirect
PyPI	packaging	`25.0`	indirect
PyPI	partd	`1.4.2`	indirect
PyPI	patsy	`1.0.2`	indirect
PyPI	peewee	`3.19.0`	indirect
PyPI	pillow	`12.3.0`	indirect
PyPI	pip-licenses	`5.5.0`	indirect
PyPI	platformdirs	`4.5.1`	indirect
PyPI	plotly	`5.24.1`	indirect
PyPI	plotly	`6.5.2`	indirect
PyPI	plotly-resampler	`0.10.0`	indirect
PyPI	plotly-resampler	`0.11.0`	indirect
PyPI	pluggy	`1.6.0`	indirect
PyPI	polars	`1.37.1`	indirect
PyPI	polars-runtime-32	`1.37.1`	indirect
PyPI	pre-commit	`4.5.1`	indirect
PyPI	prettytable	`3.17.0`	indirect
PyPI	propcache	`0.4.1`	indirect
PyPI	prophet	`1.2.1`	indirect
PyPI	protobuf	`7.34.0`	indirect
PyPI	psutil	`7.2.1`	indirect
PyPI	py4j	`0.10.9.9`	indirect
PyPI	pyarrow	`24.0.0`	indirect
PyPI	pybind11	`3.0.1`	indirect
PyPI	pycparser	`3.0`	indirect
PyPI	pygments	`2.20.0`	indirect
PyPI	pyparsing	`3.3.2`	indirect
PyPI	pyspark	`4.0.1`	indirect
PyPI	pytest	`9.0.3`	indirect
PyPI	pytest-cov	`7.0.0`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	pytz	`2025.2`	indirect
PyPI	pyyaml	`6.0.3`	indirect
PyPI	ray	`2.56.1`	indirect
PyPI	referencing	`0.37.0`	indirect
PyPI	requests	`2.33.0`	indirect
PyPI	retrying	`1.4.2`	indirect
PyPI	rich	`14.2.0`	indirect
PyPI	rpds-py	`0.30.0`	indirect
PyPI	scikit-learn	`1.7.2`	indirect
PyPI	scikit-learn	`1.8.0`	indirect
PyPI	setuptools	`83.0.0`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	sortedcontainers	`2.4.0`	indirect
PyPI	soupsieve	`2.8.4`	indirect
PyPI	stanio	`0.5.1`	indirect
PyPI	statsforecast	`2.0.2`	indirect
PyPI	statsforecast	`2.1.1`	indirect
PyPI	supersmoother	`0.4`	indirect
PyPI	tblib	`3.2.2`	indirect
PyPI	tenacity	`9.1.2`	indirect
PyPI	termcolor	`3.3.0`	indirect
PyPI	tomli	`2.4.0`	indirect
PyPI	toolz	`1.1.0`	indirect
PyPI	tornado	`6.5.7`	indirect
PyPI	triad	`1.0.0`	indirect
PyPI	tsdownsample	`0.1.4.1`	indirect
PyPI	typing-extensions	`4.15.0`	indirect
PyPI	tzdata	`2025.3`	indirect
PyPI	urllib3	`2.7.0`	indirect
PyPI	virtualenv	`20.36.1`	indirect
PyPI	wcwidth	`0.3.0`	indirect
PyPI	websockets	`16.0`	indirect
PyPI	werkzeug	`3.1.6`	indirect
PyPI	xlrd	`2.0.2`	indirect
PyPI	yarl	`1.22.0`	indirect
PyPI	yfinance	`1.0`	indirect
PyPI	zict	`3.0.0`	indirect
PyPI	zipp	`3.23.0`	indirect

Dependency advisories 0

Installing pypi:statsforecast@2.1.1 pulls in 21 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.

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.

Software health index

Score profile

Ownership

Package ecosystems

Metrics by category

Key facts

Data collection warnings

More detail

Feedback

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