Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 07:49 UTC

PyCQA / pydocstyle

docstring style checker

PythonMIT★ 1,116 stars⑂ 188 forkssince Jan 2012archivedView on GitHub ↗
KindCommand-line toolLibraryhow this is determined

PyCQA/pydocstyle holds a health index of 19 out of 100, placing it in the Critical band. It scores highest on Engineering Quality (84/100) and lowest on Vitality (21/100). The repository is archived, so no further maintenance is expected.

19
overall / 100
Critical

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.

19
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 55 is calibrated to 57 on the published index scale (record calibration 2026-08-02).

Ownership

1,027 followers29 public repossince Sep 2014

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIpydocstyle6.3.04,714,970221330 days ago

Metrics by category

Vitality

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

21At Risk · 21% of overall
How it's scored
0/36Push recency — last push 1,040 days ago
0/36Commit cadence — 0/52 weeks with commits
0/18Commit volume — 0 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push1,040
active_weeks_last_year0
How it's scored
27/27Ships releases — 17 releases published
0/36Release recency — latest release 1,330 days ago
19.8/27Release cadence — a release every ~97.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count17
latest_release_tag6.3.0
releases_from_tagsno
days_since_latest_release1,330
mean_days_between_releases97.6

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

72Good · 17% of overall
How it's scored
49.4/60Stars — 1,116 stars
18.9/25Forks — 188 forks
0/15Watchers — 2 watchers
Inputs used
forks188
stars1,116
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges7
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, readthedocs.org, shields.io
has_pull_request_templateyes
How it's scored
80/80Monthly downloads — 4,714,970 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespydocstyle
dependents—
ecosystemspypi
total_downloads—
monthly_downloads4,714,970
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?

61Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
10.9/22.5Commit distribution — top contributor authored 52% of commits
13.5/13.5Contributor breadth — 85 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled85
top_contributor_share0.515
How it's scored
28/42Issue resolution — 67% of issues closed
22.7/30PR acceptance — 235/310 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs235
open_issues113
closed_issues225
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.666
closed_unmerged_prs75
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
21.7/25Owner reach — 1,027 followers of PyCQA
22.8/25Track record — 29 public repos, account ~11 yr old
Inputs used
followers1,027
owner_typeOrganization
is_verifiedno
owner_loginPyCQA
public_repos29
account_age_days4,379
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
4/35Publish recency — latest publish 1,330 days ago
20/20Version history — 22 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespydocstyle
ecosystemspypi
any_deprecatedno
min_days_since_publish1,330

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.black], [tool.isort]), tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — http://pydocstyle.org
10/10Repository description
10/10Topics — 3 topics
0/10Wiki
Inputs used
topicspython, linter, docstrings
has_wikino
homepagehttp://pydocstyle.org
docs_sitehttp://pydocstyle.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

40Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
25/25Dependency lockfiles — poetry.lock
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilespoetry.lock
manifestspyproject.toml, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

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_packages1
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:pydocstyle@6.3.0 runtime dependency closure — what installing the published package pulls in — 1 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.

54Moderate · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
34.1/40Legible commit history — 64 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.64
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Makefile, docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.black], [tool.isort]), tox.ini
0/11Static type checking
10/10Reproducible environment — lockfile
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 — no data
Inputs used
has_nixno
has_testsyes
lockfilespoetry.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/38 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes47,225
source_files_sampled38
oversized_source_files0

Key facts

1,116GitHub stars
85contributors
0commits, last 12 months
1,040days since last push
17releases
1bus factor
113open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 188 ⇿
0Stars
188Forks
17Releases

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.

0408012016020018442012-062019-052026-05
Major 3Minor 7Patch 7

Each point covers 13 days.

Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPIsnowballstemmer>=2.2.0pyproject.toml
PyPItomli>=1.2.3pyproject.toml
PyPIimportlib-metadata>=2.0.0,<5.0.0pyproject.toml
All dependencies 18

Full resolved dependency set from the GitHub dependency graph: 6 direct and 12 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIimportlib-metadata—direct
PyPIimportlib-metadata4.8.3direct
PyPIsnowballstemmer—direct
PyPIsnowballstemmer2.2.0direct
PyPItomli—direct
PyPItomli1.2.3direct
PyPI-—indirect
PyPIblack22.3indirect
PyPIisort5.4.2indirect
PyPIjinja2—indirect
PyPImypy0.930indirect
PyPIpytest6.2.5indirect
PyPIsphinx—indirect
PyPIsphinx-rtd-theme—indirect
PyPItox—indirect
PyPItypes-setuptools—indirect
PyPItyping-extensions4.1.1indirect
PyPIzipp3.6.0indirect
Dependency advisories 0

Installing pypi:pydocstyle@6.3.0 pulls in 1 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 statistics — PyPI.