Public record
Software health reportschema 0.11.0 · metrics 2.10.0 · 2026-07-17 00:59 UTC

hajimes / mmh3

Python extension for MurmurHash (MurmurHash3), a set of fast and robust hash functions.

C · PythonMIT★ 372 stars⑂ 74 forkssince Feb 2013View on GitHub ↗

hajimes/mmh3 holds a health index of 77 out of 100, placing it in the Good band. It scores highest on Engineering Quality (87/100) and lowest on AI Readiness (54/100). It was last updated 10 days ago. A single contributor accounts for most of its recent work.

77
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.

77
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 66 is calibrated to 77 on the published index scale (record calibration 2026-08-02).

Ownership

Hajime SenumaPersonal account
9 followers17 public repossince Dec 2011National Institute of Informatics

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
PyPImmh35.2.1-17133 days agoutilityhashmurmurhash

Metrics by category

Vitality

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

59Moderate · 21% of overall
How it's scored
28.8/36Push recencylast push 10 days ago
2.8/36Commit cadence4/52 weeks with commits
12.1/18Commit volume21 commits in the last year
0/10OpenSSF Scorecard: Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year21
human_commit_share
days_since_last_push10
active_weeks_last_year4
How it's scored
27/27Ships releases19 releases published
27/36Release recencylatest release 133 days ago
19.8/27Release cadencea release every ~119.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count19
latest_release_tagv5.2.1
releases_from_tagsno
days_since_latest_release133
mean_days_between_releases119.5
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?

72Good · 17% of overall
How it's scored
41.7/60Stars372 stars
15.5/25Forks74 forks
3.9/15Watchers6 watchers
Inputs used
forks74
stars372
watchers6
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
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

57Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
2.2/22.5Commit distributiontop contributor authored 90% of commits
13.5/13.5Contributor breadth11 contributors
3/10OpenSSF Scorecard: Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled11
top_contributor_share0.903
How it's scored
41.2/42Issue resolution98% of issues closed
16.5/30PR acceptance72/131 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/26 approved changesets -- score normalized to 0
Inputs used
merged_prs72
open_issues1
closed_issues48
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.98
closed_unmerged_prs59
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
7.2/25Owner reach9 followers of hajimes
21.1/25Track record17 public repos, account ~14 yr old
Inputs used
followers9
owner_typeUser
is_verified
owner_loginhajimes
public_repos17
account_age_days5,328
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 133 days ago
20/20Version history17 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesmmh3
ecosystemspypi
any_deprecatedno
min_days_since_publish133

Engineering Quality

Are baseline engineering and documentation practices in place?

87Excellent · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter configtox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
14/20OpenSSF Scorecard: CI-Tests19 out of 24 merged PRs checked by a CI test -- score normalized to 7
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://pypi.org/project/mmh3/
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicscpython, hash, murmurhash, python, murmurhash3
has_wikiyes
homepagehttps://pypi.org/project/mmh3/
docs_sitehttps://pypi.org/project/mmh3/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

57Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
1.8/2.5CI-Tests19 out of 24 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/26 approved changesets -- score normalized to 0
0.8/2.5Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
5/5Packagingpackaging workflow detected
1.5/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 3
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
7.5/7.5Token-PermissionsGitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.7
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

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 instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
18/18One-command bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format configtox.ini
11/11Static type checkingsrc/mmh3/py.typed
0/10Reproducible environment
0/10Demonstrated agent practiceno data
5/8Automated maintenancedependency automation configured, none observed in the sampled commits
3/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 3
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageyes
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/mmh3/py.typed
agent_commit_share
toolchain_manifests
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.
How it's scored
45/45Type-checkable codeC (statically typed)
51.6/55Manageable file sizes1/16 source files over 60KB
Inputs used
primary_languageC
largest_source_bytes77,792
source_files_sampled16
oversized_source_files1

Key facts

372GitHub stars
11contributors
21commits, last 12 months
10days since last push
19releases
1bus factor
1open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 5.7 / 10
5.7aggregate

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 · 2026-07-17 00:59 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal 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
7CI-Tests19 out of 24 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/26 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
10Packagingpackaging workflow detected
3Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 3
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 0

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

RegistryPackageVersionRelation
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 v0.11.0 — full methodology · metrics wiki.

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