Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-13 00:29 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★ 3,811 stars⑂ 354 forkssince Sep 2014View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

PyCQA/flake8 holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (91/100) and lowest on AI Readiness (39/100). It was last updated 29 days ago. 2 contributors account for most of its recent work.

78
overall / 100
Good

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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

1,023 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
PyPIflake87.3.0-94418 days ago

Metrics by category

Vitality

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

53Moderate · 21% of overall
How it's scored
28.8/36Push recencylast push 29 days ago
9.7/36Commit cadence14/52 weeks with commits
13/18Commit volume27 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year27
human_commit_share0.75
days_since_last_push29
active_weeks_last_year14
How it's scored
16.2/27Ships releases70 version tags (no GitHub releases)
7.2/36Release recencylatest release 418 days ago
19.8/27Release cadencea release every ~117.1 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count70
latest_release_tag7.3.0
releases_from_tagsyes
days_since_latest_release418
mean_days_between_releases117.1

Community & Adoption

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

77Good · 17% of overall
How it's scored
58.1/60Stars3,811 stars
21.2/25Forks354 forks
8.7/15Watchers37 watchers
Inputs used
forks354
stars3,811
watchers37
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5Licenselicense 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

Sustainability & Governance

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

74Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
12.1/22.5Commit distributiontop contributor authored 46% of commits
13.5/13.5Contributor breadth99 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor2
contributors_sampled99
top_contributor_share0.461
How it's scored
41.4/42Issue resolution99% of issues closed
21.2/30PR acceptance293/414 decided PRs merged
0/13Newcomer PR acceptance0/2 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs293
open_issues23
closed_issues1,593
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d2
issue_closed_ratio0.986
closed_unmerged_prs121
first_time_authors_30d2
first_time_prs_merged_30d0
first_time_prs_decided_30d2
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
21.6/25Owner reach1,023 followers of PyCQA
22.8/25Track record29 public repos, account ~11 yr old
Inputs used
followers1,023
owner_typeOrganization
is_verified
owner_loginPyCQA
public_repos29
account_age_days4,352
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
14/35Publish recencylatest publish 418 days ago
20/20Version history94 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesflake8
ecosystemspypi
any_deprecatedno
min_days_since_publish418

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall
How it's scored
24/24CI workflows1 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-Testsno 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 sitehttps://flake8.pycqa.org
10/10Repository description
10/10Topics12 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 advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno 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 instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
34.8/40Legible commit history49 of 75 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.653
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 practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno 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 codePython without a type-check config
55/55Manageable file sizes0/77 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes23,646
source_files_sampled77
oversized_source_files0

Key facts

3,811GitHub stars
99contributors
27commits, last 12 months
29days 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 ★ / 354 ⇿
0Stars
354Forks
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.

012525037535182015-032020-112026-07
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
PyPIdocutilsindirect
PyPIsphinxindirect
PyPIsphinx-promptindirect
PyPIsphinx-rtd-themeindirect
PyPItoxindirect
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 v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.