Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-21 10:23 UTC

jupyterlab / maintainer-tools

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

PythonBSD-3-Clause★ 21 stars⑂ 25 forkssince Nov 2021View on GitHub ↗

jupyterlab/maintainer-tools holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Vitality (83/100) and lowest on AI Readiness (36/100). It was last updated 1 day ago. 2 contributors account for most of its recent work.

80
overall / 100
Excellent

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.

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

Ownership

JupyterLabOrganization · verified domain
1,639 followers68 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?

83Excellent · 21% of overall
How it's scored
36/36Push recency — last push 1 days ago
17.3/36Commit cadence — 25/52 weeks with commits
14.9/18Commit volume — 45 commits in the last year
10/10OpenSSF Scorecard: Maintained — 9 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year45
human_commit_share0.98
days_since_last_push1
active_weeks_last_year25
How it's scored
27/27Ships releases — 99 releases published
36/36Release recency — latest release 1 days ago
27/27Release cadence — a release every ~24.5 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count99
latest_release_tagv1.1.0
releases_from_tagsno
days_since_latest_release1
mean_days_between_releases24.5

Community & Adoption

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

50Moderate · 17% of overall
How it's scored
21.1/60Stars — 21 stars
11.5/25Forks — 25 forks
4.3/15Watchers — 7 watchers
Inputs used
forks25
stars21
watchers7
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized 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 factor — 2 contributor(s) cover half of all commits
16.3/22.5Commit distribution — top contributor authored 28% of commits
13.5/13.5Contributor breadth — 21 contributors
10/10OpenSSF Scorecard: Contributors — project has 35 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled21
top_contributor_share0.275
How it's scored
30/42Issue resolution — 71% of issues closed
29.4/30PR acceptance — 247/252 decided PRs merged
13/13Newcomer PR acceptance — 1/1 first-time contributors' PRs merged in 30d
9/15OpenSSF Scorecard: Code-Review — Found 19/30 approved changesets -- score normalized to 6
Inputs used
merged_prs247
open_issues16
closed_issues40
prs_merged_7d2
prs_decided_7d2
prs_merged_30d2
prs_decided_30d2
issue_closed_ratio0.714
closed_unmerged_prs5
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
23.1/25Owner reach — 1,639 followers of jupyterlab
25/25Track record — 68 public repos, account ~10 yr old
Inputs used
followers1,639
owner_typeOrganization
is_verifiedyes
owner_loginjupyterlab
public_repos68
account_age_days3,660

Engineering Quality

Are baseline engineering and documentation practices in place?

62Moderate · 19% of overall
How it's scored
24/24CI workflows — 8 workflow(s)
0/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 20 out of 20 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
10/10Topics — 2 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?

65Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 20 out of 20 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
4.5/7.5Code-Review — Found 19/30 approved changesets -- score normalized to 6
2.5/2.5Contributors — project has 35 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 — 9 commit(s) and 4 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
3.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
2.5/5SAST — SAST tool is not run on all commits -- score normalized to 5
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.5
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 instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 92 of 98 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.939
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
0/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
0/10Reproducible environment
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
7/10OpenSSF Scorecard: Pinned-Dependencies — dependency 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 code — Python without a type-check config
55/55Manageable file sizes — 0/5 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes17,590
source_files_sampled5
oversized_source_files0

Key facts

21GitHub stars
21contributors
45commits, last 12 months
1days since last push
99releases
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 ★ / 25 ⇿
0Stars
25Forks
98Releases

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.

05101520252522021-112024-042026-10
Major 1Minor 34Patch 63

Each point covers 5 days.

OpenSSF Scorecard 6.5 / 10
6.5aggregate

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 · 2026-10-21 10:23 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests20 out of 20 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
10Maintained9 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
5SASTSAST tool is not run on all commits -- score normalized to 5
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-core—direct
PyPIhatchling—indirect
PyPIpytest—indirect
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 statistics — PyPI.