Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 19:10 UTC

PyCQA / flake8

flake8 is a python tool that glues together pycodestyle, pyflakes, mccabe, and third-party plugins to check the style and quality of some python code.

PythonCustom license★ 3,823 stars⑂ 356 forkssince Sep 2014View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

PyCQA/flake8 holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (91/100) and lowest on AI Readiness (39/100). It was last updated 30 days ago. 2 contributors account for most of its recent work.

80
overall / 100
Excellent

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.

80
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 68 is calibrated to 80 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
PyPIflake87.3.042,680,51494453 days ago

Metrics by category

Vitality

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

54Moderate · 21% of overall
How it's scored
28.8/36Push recency — last push 30 days ago
9.7/36Commit cadence — 14/52 weeks with commits
13.3/18Commit volume — 29 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year29
human_commit_share0.76
days_since_last_push30
active_weeks_last_year14
How it's scored
16.2/27Ships releases — 70 version tags (no GitHub releases)
7.2/36Release recency — latest release 453 days ago
19.8/27Release cadence — a release every ~117.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count70
latest_release_tag7.3.0
releases_from_tagsyes
days_since_latest_release453
mean_days_between_releases117.1

Community & Adoption

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

83Excellent · 17% of overall
How it's scored
58.1/60Stars — 3,823 stars
21.3/25Forks — 356 forks
8.7/15Watchers — 37 watchers
Inputs used
forks356
stars3,823
watchers37
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
80/80Monthly downloads — 42,680,514 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesflake8
dependents—
ecosystemspypi
total_downloads—
monthly_downloads42,680,514
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?

73Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
12.2/22.5Commit distribution — top contributor authored 46% of commits
13.5/13.5Contributor breadth — 99 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled99
top_contributor_share0.46
How it's scored
41.4/42Issue resolution — 99% of issues closed
21.3/30PR acceptance — 295/416 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_prs295
open_issues23
closed_issues1,595
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.986
closed_unmerged_prs121
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 ~12 yr old
Inputs used
followers1,027
owner_typeOrganization
is_verifiedno
owner_loginPyCQA
public_repos29
account_age_days4,388
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
14/35Publish recency — latest publish 453 days ago
20/20Version history — 94 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesflake8
ecosystemspypi
any_deprecatedno
min_days_since_publish453

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — .pylintrc, tox.ini
9.6/9.6Pre-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_configyes

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://flake8.pycqa.org
10/10Repository description
10/10Topics — 12 topics
0/10Wiki
Inputs used
topicspython, python3, static-analysis, static-code-analysis, linter, linter-flake8, stylelint, styleguide, style-guide, flake8, pep8, complexity-analysis
has_wikino
homepagehttps://flake8.pycqa.org
docs_sitehttps://flake8.pycqa.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

44Weak · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsexample-plugin/setup.py, setup.cfg, setup.py
has_codeql_workflowno
has_security_policyyes
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_packages3
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:flake8@7.3.0 runtime dependency closure — what installing the published package pulls in — 3 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.

39Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
34.4/40Legible commit history — 49 of 76 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.645
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — .pylintrc, tox.ini
0/11Static type checking
0/10Reproducible environment
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
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
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 — 0/77 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes23,646
source_files_sampled77
oversized_source_files0

Key facts

3,823GitHub stars
99contributors
29commits, last 12 months
30days since last push
70releases
2bus factor
23open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'flake8-example-plugin' from its registry
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 356 ⇿
0Stars
356Forks
57Releases

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.

012525037535382015-032020-122026-09
Major 5Minor 15Patch 37

Each point covers 11 days.

Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPImccabe>=0.7.0,<0.8.0setup.cfg
PyPIpycodestyle>=2.14.0,<2.15.0setup.cfg
PyPIpyflakes>=3.4.0,<3.5.0setup.cfg
All dependencies 5

Full resolved dependency set from the GitHub dependency graph: 0 direct and 5 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIdocutils—indirect
PyPIsphinx—indirect
PyPIsphinx-prompt—indirect
PyPIsphinx-rtd-theme—indirect
PyPItox—indirect
Dependency advisories 0

Installing pypi:flake8@7.3.0 pulls in 3 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.