Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-02 05:10 UTC

jupyterlab / maintainer-tools

Workflows and Actions meant to be used by other repositories to make repo maintenance easier

PythonBSD-3-Clause★ 21 stars⑂ 23 forkssince Nov 2021View on GitHub ↗

jupyterlab/maintainer-tools holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Sustainability & Governance (80/100) and lowest on AI Readiness (36/100). It was last updated 21 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

JupyterLabOrganization · verified domain
1,607 followers67 public repossince Oct 2016

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIfoobarpoints to another repo — not scored1.1-0

Metrics by category

Vitality

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

79Good · 21% of overall
How it's scored
28.8/36Push recencylast push 21 days ago
17.3/36Commit cadence25/52 weeks with commits
14.9/18Commit volume45 commits in the last year
10/10OpenSSF Scorecard: Maintained8 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year45
human_commit_share0.97
days_since_last_push21
active_weeks_last_year25
How it's scored
27/27Ships releases97 releases published
36/36Release recencylatest release 39 days ago
27/27Release cadencea release every ~18.7 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count97
latest_release_tagv0.35.0
releases_from_tagsno
days_since_latest_release39
mean_days_between_releases18.7

Community & Adoption

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

50Moderate · 17% of overall
How it's scored
21.1/60Stars21 stars
11.2/25Forks23 forks
4.3/15Watchers7 watchers
Inputs used
forks23
stars21
watchers7
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (BSD-3-Clause)
0/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingno
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

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

80Excellent · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
16.1/22.5Commit distributiontop contributor authored 28% of commits
13.5/13.5Contributor breadth19 contributors
10/10OpenSSF Scorecard: Contributorsproject has 35 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled19
top_contributor_share0.284
How it's scored
29.8/42Issue resolution71% of issues closed
29.4/30PR acceptance244/249 decided PRs merged
13/13Newcomer PR acceptance1/1 first-time contributors' PRs merged in 30d
9/15OpenSSF Scorecard: Code-ReviewFound 19/30 approved changesets -- score normalized to 6
Inputs used
merged_prs244
open_issues16
closed_issues39
prs_merged_7d0
prs_decided_7d0
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.709
closed_unmerged_prs5
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backingorganization-owned
20/20Verified domain
23.1/25Owner reach1,607 followers of jupyterlab
25/25Track record67 public repos, account ~9 yr old
Inputs used
followers1,607
owner_typeOrganization
is_verifiedyes
owner_loginjupyterlab
public_repos67
account_age_days3,611

Engineering Quality

Are baseline engineering and documentation practices in place?

62Moderate · 19% of overall
How it's scored
24/24CI workflows8 workflow(s)
0/24Tests present
16/16Linter configpyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests21 out of 21 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsno
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics2 topics
0/10Wiki
Inputs used
topicsgithub-actions, maintenance-tool
has_wikino
homepage
docs_site
has_readmeyes
has_docs_dirno
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
7.5/7.5Binary-Artifactsno binaries found in the repo
2.2/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests21 out of 21 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
4.5/7.5Code-ReviewFound 19/30 approved changesets -- score normalized to 6
2.5/2.5Contributorsproject has 35 contributing companies or organizations
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
7.5/7.5Maintained8 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
3.5/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 7
1.5/5SASTSAST tool is not run on all commits -- score normalized to 3
5/5Security-Policysecurity policy file detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.4
Excluded from scoring (no data or not applicable): Packaging. 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.

36Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history92 of 97 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.948
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
0/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff])
0/11Static type checking
0/10Reproducible environment
2/10Demonstrated agent practice1 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenancedependency automation configured, none observed in the sampled commits
7/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 7
Inputs used
has_nixno
has_testsno
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.01
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/5 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes17,590
source_files_sampled5
oversized_source_files0

Key facts

21GitHub stars
19contributors
45commits, last 12 months
21days since last push
97releases
2bus factor
16open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'foobar' points at a different repository (http://ziade.org); excluded from ecosystem scoring
  • deps.dev does not index pypi:foobar@1.1; advisories assessed against the repository dependency graph instead
  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 0 ★ / 23 ⇿
0Stars
23Forks
97Releases

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.

048121620242322021-112024-032026-08
Major 0Minor 34Patch 63

Each point covers 5 days.

OpenSSF Scorecard 6.4 / 10
6.4aggregate

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 · 2026-09-02 05:10 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests21 out of 21 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 19/30 approved changesets -- score normalized to 6
10Contributorsproject has 35 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained8 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
7Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 7
3SASTSAST tool is not run on all commits -- score normalized to 3
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPIjupyter_core>=4.12,!=5.0.*pyproject.toml
All dependencies 3

Full resolved dependency set from the GitHub dependency graph: 1 direct and 2 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIjupyter-coredirect
PyPIhatchlingindirect
PyPIpytestindirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

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 statisticsPyPI.