Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-16 12:55 UTC

pydantic / pydantic-extra-types

Extra Pydantic types.

PythonMIT★ 335 stars⑂ 104 forkssince Feb 2023View on GitHub ↗

KindLibraryDesktop applicationCommand-line toolhow this is determined

#countries #mac-address #phone-number #pydantic #python

pydantic/pydantic-extra-types holds a health index of 95 out of 100, placing it in the Exceptional band. It scores highest on Sustainability & Governance (89/100) and lowest on Community & Adoption (68/100). It was last updated 1 day ago. 6 contributors account 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 82 is calibrated to 95 on the published index scale (record calibration 2026-08-02).

Ownership

PydanticOrganization · verified domain

3,036 followers86 public repossince Aug 2022

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
PyPI	pydantic-extra-types	2.11.1	54,452,991	24	184 days ago

Metrics by category

Vitality

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

81Excellent · 21% of overall

Development activity

80Excellent

How it's scored

36/36	Push recency — last push 1 days ago
17.3/36	Commit cadence — 25/52 weeks with commits
16.4/18	Commit volume — 66 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 26 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	66
human_commit_share	0.73
days_since_last_push	1
active_weeks_last_year	25

Release discipline

82Excellent

How it's scored

27/27	Ships releases — 23 releases published
27/36	Release recency — latest release 163 days ago
19.8/27	Release cadence — a release every ~59.9 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	23
latest_release_tag	v2.11.2
releases_from_tags	no
days_since_latest_release	163
mean_days_between_releases	59.9

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?

68Good · 17% of overall

Popularity & adoption

63Moderate

How it's scored

40.9/60	Stars — 335 stars
16.8/25	Forks — 104 forks
5.3/15	Watchers — 10 watchers

Inputs used

forks	104
stars	335
watchers	10
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

50Moderate

How it's scored

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

Inputs used

has_readme	yes
has_license	yes
readme_badges	4
has_contributing	no
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	codecov.io, github.com, shields.io
has_pull_request_template	no

Ecosystem adoption (downloads)

100Exceptional

How it's scored

80/80	Monthly downloads — 54,452,991 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	pydantic-extra-types
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	54,452,991
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?

89Excellent · 23% of overall

Maintainer resilience (bus factor)

88Excellent

How it's scored

48.6/54	Bus factor — 6 contributor(s) cover half of all commits
16/22.5	Commit distribution — top contributor authored 29% of commits
13.5/13.5	Contributor breadth — 75 contributors
10/10	OpenSSF Scorecard: Contributors — project has 25 contributing companies or organizations

Inputs used

bus_factor	6
contributors_sampled	75
top_contributor_share	0.288

Issue & PR responsiveness

80Excellent

How it's scored

33.1/42	Issue resolution — 79% of issues closed
26.1/30	PR acceptance — 228/262 decided PRs merged
7.8/13	Newcomer PR acceptance — 3/5 first-time contributors' PRs merged in 30d
13.5/15	OpenSSF Scorecard: Code-Review — Found 18/19 approved changesets -- score normalized to 9

Inputs used

merged_prs	228
open_issues	27
closed_issues	101
prs_merged_7d	2
prs_decided_7d	3
prs_merged_30d	3
prs_decided_30d	5
issue_closed_ratio	0.789
closed_unmerged_prs	34
first_time_authors_30d	5
first_time_prs_merged_30d	3
first_time_prs_decided_30d	5

Ownership & stewardship

96Exceptional

How it's scored

30/30	Ownership backing — organization-owned
20/20	Verified domain
25/25	Owner reach — 3,036 followers of pydantic
21.2/25	Track record — 86 public repos, account ~4 yr old

Inputs used

followers	3,036
owner_type	Organization
is_verified	yes
owner_login	pydantic
public_repos	86
account_age_days	1,500

Package maintenance

91Excellent

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
26/35	Publish recency — latest publish 184 days ago
20/20	Version history — 24 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	pydantic-extra-types
ecosystems	pypi
any_deprecated	no
min_days_since_publish	184

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall

Engineering practices

94Exceptional

How it's scored

24/24	CI workflows — 1 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

75Good

How it's scored

30/30	README
0/25	Documentation directory
15/15	Documentation / homepage site — https://docs.pydantic.dev/latest/
10/10	Repository description
10/10	Topics — 5 topics
10/10	Wiki

Inputs used

topics	countries, mac-address, phone-number, pydantic, python
has_wiki	yes
homepage	—
docs_site	https://docs.pydantic.dev/latest/
has_readme	yes
has_docs_dir	no
has_description	yes

Security

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

87Excellent · 16% of overall

Security posture

84Excellent

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
0/7.5	Branch-Protection — no data
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
6.8/7.5	Code-Review — Found 18/19 approved changesets -- score normalized to 9
2.5/2.5	Contributors — project has 25 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 — 26 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5	Packaging — packaging workflow detected
5/5	Pinned-Dependencies — all dependencies are pinned
0/5	SAST — SAST tool is not run on all commits -- score normalized to 0
5/5	Security-Policy — security policy file detected
0/7.5	Signed-Releases — no data
7.5/7.5	Token-Permissions — GitHub workflow tokens follow principle of least privilege
6/7.5	Vulnerabilities — 2 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	16
scorecard_version	v5.5.0
checks_inconclusive	2
scorecard_aggregate	8.4

Excluded from scoring (no data or not applicable): Branch-Protection, 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	5
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:pydantic-extra-types@2.11.1 runtime dependency closure — what installing the published package pulls in — 5 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.

75Good · 4% of overall

Agent context & guidance

40Weak

How it's scored

0/45	Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15	Machine-readable docs (llms.txt)
40/40	Legible commit history — 73 of 73 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	no
llms_txt_url	—
legible_history_share	1
agent_instruction_files	—
agent_instruction_max_bytes	—

Verify loop (build / test / typecheck)

100Exceptional

How it's scored

18/18	One-command bootstrap — Makefile
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.ruff])
11/11	Static type checking — pydantic_extra_types/py.typed
10/10	Reproducible environment — lockfile
10/10	Demonstrated agent practice — 7 of the last 100 commits agent-authored or agent-credited
8/8	Automated maintenance — 27 of the last 100 commits are automated dependency updates
10/10	OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned

Inputs used

has_nix	no
has_tests	yes
lockfiles	uv.lock
has_dockerfile	no
typed_language	no
bootstrap_files	Makefile
has_devcontainer	no
has_linter_config	yes
typecheck_configs	pydantic_extra_types/py.typed
agent_commit_share	0.07
toolchain_manifests	—
dependency_bot_commit_share	0.27

Code legibility for models

81Excellent

How it's scored

27/45	Type-checkable code — Python with type-check config (pydantic_extra_types/py.typed)
54.1/55	Manageable file sizes — 1/62 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	151,665
source_files_sampled	62
oversized_source_files	1

Key facts

335GitHub stars

75contributors

66commits, last 12 months

1days since last push

23releases

6bus factor

27open 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 ★ / 104 ⇿

0Stars

104Forks

23Releases

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.

Major 1Minor 11Patch 11

Each point covers 4 days.

OpenSSF Scorecard 8.4 / 10

8.4aggregate

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 · 2026-09-16 12:55 UTC

10	Binary-Artifacts	no binaries found in the repo
n/a	Branch-Protection	internal 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
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
9	Code-Review	Found 18/19 approved changesets -- score normalized to 9
10	Contributors	project has 25 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	26 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10	Packaging	packaging workflow detected
10	Pinned-Dependencies	all dependencies are pinned
0	SAST	SAST tool is not run on all commits -- score normalized to 0
10	Security-Policy	security policy file detected
n/a	Signed-Releases	no releases found
10	Token-Permissions	GitHub workflow tokens follow principle of least privilege
8	Vulnerabilities	2 existing vulnerabilities detected

Direct dependencies 2

Registry	Package	Version constraint	Manifest
PyPI	pydantic	`>=2.5.2`	pyproject.toml
PyPI	typing-extensions	`—`	pyproject.toml

All dependencies 53

Full resolved dependency set from the GitHub dependency graph: 2 direct and 51 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	pydantic	`2.13.4`	direct
PyPI	typing-extensions	`4.15.0`	direct
PyPI	annotated-types	`0.7.0`	indirect
PyPI	attrs	`26.1.0`	indirect
PyPI	colorama	`0.4.6`	indirect
PyPI	coverage	`7.10.7`	indirect
PyPI	coverage	`7.13.0`	indirect
PyPI	cron-converter	`1.3.1`	indirect
PyPI	dirty-equals	`0.11`	indirect
PyPI	dnspython	`2.7.0`	indirect
PyPI	dnspython	`2.8.0`	indirect
PyPI	exceptiongroup	`1.3.1`	indirect
PyPI	iniconfig	`2.1.0`	indirect
PyPI	iniconfig	`2.3.0`	indirect
PyPI	jsonschema	`4.25.1`	indirect
PyPI	jsonschema	`4.26.0`	indirect
PyPI	jsonschema-specifications	`2025.9.1`	indirect
PyPI	librt	`0.7.5`	indirect
PyPI	markdown-it-py	`3.0.0`	indirect
PyPI	markdown-it-py	`4.0.0`	indirect
PyPI	mdurl	`0.1.2`	indirect
PyPI	mypy	`1.19.1`	indirect
PyPI	mypy-extensions	`1.1.0`	indirect
PyPI	packaging	`25.0`	indirect
PyPI	pathspec	`0.12.1`	indirect
PyPI	pendulum	`3.2.0`	indirect
PyPI	phonenumbers	`9.0.33`	indirect
PyPI	pluggy	`1.6.0`	indirect
PyPI	pycountry	`24.6.1`	indirect
PyPI	pydantic-core	`2.46.4`	indirect
PyPI	pygments	`2.19.2`	indirect
PyPI	pymongo	`4.17.0`	indirect
PyPI	pytest	`8.4.2`	indirect
PyPI	pytest	`9.0.2`	indirect
PyPI	pytest-pretty	`1.3.0`	indirect
PyPI	python-dateutil	`2.9.0.post0`	indirect
PyPI	python-ulid	`3.1.0`	indirect
PyPI	pytz	`2026.2`	indirect
PyPI	referencing	`0.36.2`	indirect
PyPI	referencing	`0.37.0`	indirect
PyPI	rich	`14.2.0`	indirect
PyPI	rpds-py	`0.27.1`	indirect
PyPI	rpds-py	`0.30.0`	indirect
PyPI	ruff	`0.15.19`	indirect
PyPI	semver	`3.0.4`	indirect
PyPI	six	`1.17.0`	indirect
PyPI	tomli	`2.3.0`	indirect
PyPI	types-jsonschema	`4.26.0.20260202`	indirect
PyPI	types-jsonschema	`4.26.0.20260402`	indirect
PyPI	types-pytz	`2025.2.0.20251108`	indirect
PyPI	typing-inspection	`0.4.2`	indirect
PyPI	tzdata	`2026.2`	indirect
PyPI	uuid-utils	`0.14.1`	indirect

Dependency advisories 0

Installing pypi:pydantic-extra-types@2.11.1 pulls in 5 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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