Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 20:20 UTC

moj-analytical-services / splink

Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends

Python · JavaScriptMIT★ 2,411 stars⑂ 264 forkssince Nov 2019View on GitHub ↗

moj-analytical-services/splink holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (99/100) and lowest on AI Readiness (72/100). It was last updated today. 2 contributors account for most of its recent work.

98
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.

98
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 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

MoJ Analytical ServicesOrganization · verified domain
548 followers21 public repossince Feb 2017

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIsplink4.0.17-16314 days ago

Metrics by category

Vitality

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

99Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
35.3/36Commit cadence — 51/52 weeks with commits
18/18Commit volume — 1,471 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 12 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year1,471
human_commit_share0.75
days_since_last_push0
active_weeks_last_year51

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 14 days ago
27/27Release cadence — a release every ~28.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv4.0.17
releases_from_tagsno
days_since_latest_release14
mean_days_between_releases28.9
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?

80Excellent · 17% of overall
How it's scored
54.9/60Stars — 2,411 stars
20.2/25Forks — 264 forks
6.7/15Watchers — 17 watchers
Inputs used
forks264
stars2,411
watchers17
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateyes

Sustainability & Governance

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

82Excellent · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
12.8/22.5Commit distribution — top contributor authored 43% of commits
13.5/13.5Contributor breadth — 77 contributors
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled77
top_contributor_share0.432
How it's scored
32.4/42Issue resolution — 77% of issues closed
26.9/30PR acceptance — 1,727/1,923 decided PRs merged
6.5/13Newcomer PR acceptance — 1/2 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs1,727
open_issues188
closed_issues632
prs_merged_7d5
prs_decided_7d9
prs_merged_30d14
prs_decided_30d23
issue_closed_ratio0.771
closed_unmerged_prs196
first_time_authors_30d2
first_time_prs_merged_30d1
first_time_prs_decided_30d2
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
19.7/25Owner reach — 548 followers of moj-analytical-services
21.8/25Track record — 21 public repos, account ~9 yr old
Inputs used
followers548
owner_typeOrganization
is_verifiedyes
owner_loginmoj-analytical-services
public_repos21
account_age_days3,496

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 14 days ago
20/20Version history — 163 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagessplink
ecosystemspypi
any_deprecatedno
min_days_since_publish14

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall
How it's scored
24/24CI workflows — 15 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.isort])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 13 out of 13 merged PRs checked by a CI test -- score normalized to 10
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 site — https://moj-analytical-services.github.io/splink/
10/10Repository description
10/10Topics — 11 topics
10/10Wiki
Inputs used
topicsrecord-linkage, spark, em-algorithm, deduplication, deduplicate-data, entity-resolution, data-matching, fuzzy-matching, data-science, duckdb, uk-gov-data-science
has_wikiyes
homepagehttps://moj-analytical-services.github.io/splink/
docs_sitehttps://moj-analytical-services.github.io/splink/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

82Excellent · 16% of overall
How it's scored
5.2/7.5Binary-Artifacts — binaries present in source code
5.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 13 out of 13 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 12 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
4/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8
5/5SAST — SAST tool is run on all commits
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
6.8/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
5.2/7.5Vulnerabilities — 3 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate7.7
Excluded from scoring (no data or not applicable): Packaging, Signed-Releases. Remaining weights renormalized.

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_packages19
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:splink@4.0.17 runtime dependency closure — what installing the published package pulls in — 19 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.

72Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
32/40Legible commit history — 45 of 75 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.6
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.isort])
11/11Static type checking — splink/py.typed
10/10Reproducible environment — Dockerfile, lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 25 of the last 100 commits are automated dependency updates
8/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configssplink/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.25
How it's scored
27/45Type-checkable code — Python with type-check config (splink/py.typed)
54.4/55Manageable file sizes — 3/256 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes920,153
source_files_sampled256
oversized_source_files3
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — demos, examples
Inputs used
example_dirsdemos, examples
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

2,411GitHub stars
77contributors
1,471commits, last 12 months
0days since last push
100releases
2bus factor
188open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'splink' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 264 ⇿
0Stars
264Forks
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.

05010015020025030026252020-032023-062026-09
Major 2Minor 9Patch 59

Each point covers 6 days.

OpenSSF Scorecard 7.7 / 10
7.7aggregate

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 · 2026-09-17 20:19 UTC

7Binary-Artifactsbinaries present in source code
7Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests13 out of 13 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 12 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
8Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
10SASTSAST tool is run on all commits
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
9Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7Vulnerabilities3 existing vulnerabilities detected
Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPIduckdb>=0.9.2pyproject.toml
PyPIsqlglot>=17.6.0pyproject.toml
All dependencies 203

Full resolved dependency set from the GitHub dependency graph: 4 direct and 199 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIduckdb1.5.4direct
PyPIduckdb1.5.5direct
PyPIsqlglot30.11.0direct
PyPIsqlglot30.18.0direct
PyPIaltair6.2.2indirect
PyPIannotated-types0.7.0indirect
PyPIanyio4.13.0indirect
PyPIappnope0.1.4indirect
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PyPIattrs26.1.0indirect
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PyPImkdocs-rss-plugin1.19.0indirect
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PyPIpandas2.3.3indirect
PyPIpandas3.0.3indirect
PyPIpandas-stubs2.2.3.250308indirect
PyPIpandocfilters1.5.1indirect
PyPIparso0.8.7indirect
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PyPIpygments2.20.0indirect
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PyPIpyspark4.2.0indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-xdist3.8.0indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpytz2026.2indirect
PyPIpyyaml6.0.3indirect
PyPIpyyaml-env-tag1.1indirect
PyPIpyzmq27.1.0indirect
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PyPIrequests2.34.2indirect
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PyPIrpds-py2026.5.1indirect
PyPIruff0.15.18indirect
PyPIscipy1.15.3indirect
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PyPIsmmap5.0.3indirect
PyPIsoupsieve2.8.4indirect
PyPIsoupsieve2.9.1indirect
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PyPIsplink-demos5.0.0indirect
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PyPIstack-data0.6.3indirect
PyPItables3.10.1indirect
PyPItables3.11.1indirect
PyPItexttable1.7.0indirect
PyPItextual8.2.7indirect
PyPItextual-plotext1.0.1indirect
PyPIthreadpoolctl3.6.0indirect
PyPItinycss21.4.0indirect
PyPItinycss21.5.1indirect
PyPItomli2.4.1indirect
PyPItornado6.5.8indirect
PyPItqdm4.68.3indirect
PyPItraitlets5.15.0indirect
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PyPIty0.0.52indirect
PyPItypes-pytz2026.2.0.20260518indirect
PyPItypes-pyyaml6.0.12.20260518indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2025.3indirect
PyPItzdata2026.2indirect
PyPIuc-micro-py2.0.0indirect
PyPIurllib32.7.0indirect
PyPIvivarium2.3.7indirect
PyPIvivarium-build-utils1.1.2indirect
PyPIvivarium-dependencies1.0.4indirect
PyPIwatchdog6.0.0indirect
PyPIwcwidth0.7.0indirect
PyPIwcwidth0.8.1indirect
PyPIwebencodings0.5.1indirect
PyPIwidgetsnbextension4.0.16indirect
PyPIwin32-setctime1.2.0indirect
Dependency advisories 0

Installing pypi:splink@4.0.17 pulls in 19 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.