Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-05 04:50 UTC

joke2k / faker

Faker is a Python package that generates fake data for you.

PythonMIT★ 19,354 stars⑂ 2,108 forkssince Nov 2012View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

joke2k/faker holds a health index of 94 out of 100, placing it in the Exceptional band. It scores highest on Community & Adoption (92/100) and lowest on AI Readiness (62/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

94
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.

94
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 80 is calibrated to 94 on the published index scale (record calibration 2026-08-02).

Ownership

Daniele FaragliaPersonal account
565 followers41 public repossince Aug 2010Self Employed

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIFaker40.36.0-50311 days agofakerfixturesdatatestmockgenerator

Metrics by category

Vitality

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

91Excellent · 21% of overall
How it's scored
36/36Push recencylast push 1 days ago
22.2/36Commit cadence32/52 weeks with commits
18/18Commit volume282 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year282
human_commit_share0.95
days_since_last_push1
active_weeks_last_year32

Release discipline

100Exceptional
How it's scored
27/27Ships releases100 releases published
36/36Release recencylatest release 11 days ago
27/27Release cadencea release every ~1.1 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count100
latest_release_tagv40.36.0
releases_from_tagsno
days_since_latest_release11
mean_days_between_releases1.1

Community & Adoption

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

92Excellent · 17% of overall
How it's scored
60/60Stars19,354 stars
25/25Forks2,108 forks
13/15Watchers217 watchers
Inputs used
forks2,108
stars19,354
watchers217
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
7.2/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateyes
has_code_of_conductno
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

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

69Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
7.4/22.5Commit distributiontop contributor authored 67% of commits
13.5/13.5Contributor breadth99 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled99
top_contributor_share0.67
How it's scored
41.6/42Issue resolution99% of issues closed
25.1/30PR acceptance1,309/1,565 decided PRs merged
9.3/13Newcomer PR acceptance5/7 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs1,309
open_issues8
closed_issues833
prs_merged_7d0
prs_decided_7d0
prs_merged_30d13
prs_decided_30d15
issue_closed_ratio0.99
closed_unmerged_prs256
first_time_authors_30d6
first_time_prs_merged_30d5
first_time_prs_decided_30d7
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
19.8/25Owner reach565 followers of joke2k
23.8/25Track record41 public repos, account ~15 yr old
Inputs used
followers565
owner_typeUser
is_verified
owner_loginjoke2k
public_repos41
account_age_days5,839
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 11 days ago
20/20Version history503 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesFaker
ecosystemspypi
any_deprecatedno
min_days_since_publish11

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter configtox.ini
0/9.6Pre-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_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://faker.readthedocs.io
10/10Repository description
10/10Topics9 topics
10/10Wiki
Inputs used
topicspython, fake, testing, dataset, fake-data, test-data, test-data-generator, faker, faker-generator
has_wikiyes
homepagehttps://faker.readthedocs.io
docs_sitehttps://faker.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

64Moderate · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestssetup.cfg, setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
0/25Indirect dependencies free of known advisoriestransitive set not separable from development and test dependencies in this scope
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages4
unassessed_packages20
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 4 resolved dependencies against OSV. 20 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

62Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
18.5/40Legible commit history33 of 95 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.347
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile, docs/Makefile
22/22Automated tests
11/11Lint / format configtox.ini
11/11Static type checkingfaker/py.typed, mypy.ini
0/10Reproducible environment
10/10Demonstrated agent practice34 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance5 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configsfaker/py.typed, mypy.ini
agent_commit_share0.34
toolchain_manifests
dependency_bot_commit_share0.05
How it's scored
27/45Type-checkable codePython with type-check config (faker/py.typed, mypy.ini)
53/55Manageable file sizes30/813 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes769,957
source_files_sampled813
oversized_source_files30

Key facts

19,354GitHub stars
99contributors
282commits, last 12 months
1days since last push
100releases
1bus factor
8open 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 ★ / 2,108 ⇿
0Stars
2,108Forks
100Releases

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.

1,0001,2001,4001,6001,8002,0002,2002,108192020-082023-082026-07
Major 7Minor 58Patch 35

Each point covers 6 days.

All dependencies 24

Full resolved dependency set from the GitHub dependency graph: 0 direct and 24 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIblackindirect
PyPIcheck-manifestindirect
PyPIcoverageindirect
PyPIdoc8indirect
PyPIemail-validator1.0.3indirect
PyPIflake8indirect
PyPIflake8-comprehensionsindirect
PyPIfreezegunindirect
PyPIisortindirect
PyPImock2.0.0indirect
PyPImypyindirect
PyPImypy-extensionsindirect
PyPIpackagingindirect
PyPIpytestindirect
PyPIpython-dateutilindirect
PyPIsetuptoolsindirect
PyPIsixindirect
PyPItext-unidecode1.2indirect
PyPItoxindirect
PyPItwineindirect
PyPItzdataindirect
PyPIukpostcodeparser1.1.2indirect
PyPIvalidatorsindirect
PyPIwheelindirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 4 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 20 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

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.