Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-20 17:21 UTC

optuna / optunahub-registry

The registry of the OptunaHub packages

Jupyter Notebook · PythonMIT★ 58 stars⑂ 73 forkssince Apr 2024View on GitHub ↗

optuna/optunahub-registry holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Vitality (90/100) and lowest on Security (40/100). It was last updated today. 2 contributors account for most of its recent work.

81
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.

81
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 69 is calibrated to 81 on the published index scale (record calibration 2026-08-02).

Ownership

optunaOrganization
422 followers17 public repossince Nov 2019

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

Metrics by category

Vitality

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

90Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
32.5/36Commit cadence — 47/52 weeks with commits
18/18Commit volume — 461 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year461
human_commit_share1
days_since_last_push0
active_weeks_last_year47
How it's scored
16.2/27Ships releases — 8 version tags (no GitHub releases)
36/36Release recency — latest release 41 days ago
19.8/27Release cadence — a release every ~119.7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count8
latest_release_tagv0.5.0
releases_from_tagsyes
days_since_latest_release41
mean_days_between_releases119.7

Community & Adoption

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

59Moderate · 17% of overall
How it's scored
28.5/60Stars — 58 stars
15.5/25Forks — 73 forks
0/15Watchers — 1 watchers
Inputs used
forks73
stars58
watchers1
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_badges0
has_contributingyes
has_issue_templateno
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?

72Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
16.2/22.5Commit distribution — top contributor authored 28% of commits
13.5/13.5Contributor breadth — 58 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled58
top_contributor_share0.281
How it's scored
31.5/42Issue resolution — 75% of issues closed
28.9/30PR acceptance — 371/385 decided PRs merged
13/13Newcomer PR acceptance — 3/3 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs371
open_issues11
closed_issues33
prs_merged_7d3
prs_decided_7d3
prs_merged_30d7
prs_decided_30d7
issue_closed_ratio0.75
closed_unmerged_prs14
first_time_authors_30d3
first_time_prs_merged_30d3
first_time_prs_decided_30d3
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
18.9/25Owner reach — 422 followers of optuna
21.1/25Track record — 17 public repos, account ~6 yr old
Inputs used
followers422
owner_typeOrganization
is_verifiedno
owner_loginoptuna
public_repos17
account_age_days2,545

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://hub.optuna.org
10/10Repository description
10/10Topics — 5 topics
0/10Wiki
Inputs used
topicsalgorithms, benchmark, machine-learning, optimization, python
has_wikino
homepagehttps://hub.optuna.org
docs_sitehttps://hub.optuna.org
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

40Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyno
has_dependabot_configyes

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories41
affected_packages3
assessed_packages25
unassessed_packages34
affected_by_severitycritical 1, high 1, moderate 1
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 25 resolved dependencies against OSV. 34 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.

48Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
18.7/40Legible commit history — 35 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.35
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
0/10Reproducible environment
4/10Demonstrated agent practice — 2 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.02
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Jupyter Notebook without a type-check config
54.9/55Manageable file sizes — 1/465 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes61,658
source_files_sampled465
oversized_source_files1
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 — notebooks
Inputs used
example_dirsnotebooks
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

58GitHub stars
58contributors
461commits, last 12 months
0days since last push
8releases
2bus factor
11open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'optunahub-registry' from its registry
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 73 ⇿
0Stars
73Forks
7Releases

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.

01325385063757342024-052025-082026-10
Major 0Minor 5Patch 1

Each point covers 3 days.

Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPImdutils—pyproject.toml
PyPIpython-frontmatter—pyproject.toml
All dependencies 59

Full resolved dependency set from the GitHub dependency graph: 0 direct and 59 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIbotorch—indirect
PyPIcma—indirect
PyPIcmaes—indirect
PyPIcoco-experiment—indirect
PyPIconfopt—indirect
PyPIcontourpy1.1.0indirect
PyPIcycler0.11.0indirect
PyPIcython3.0.0indirect
PyPIdecorator5.1.1indirect
PyPIdiversipy—indirect
PyPIevotorch—indirect
PyPIfonttools4.42.0indirect
PyPIgoogle-vizier0.1.24indirect
PyPIgpy1.13.1indirect
PyPIgpytorch—indirect
PyPIhebo—indirect
PyPIimportlib-resources6.0.1indirect
PyPIjax0.4.34indirect
PyPIjaxlib0.4.34indirect
PyPIkiwisolver1.4.4indirect
PyPIlangchain—indirect
PyPImatplotlib—indirect
PyPImatplotlib3.5.2indirect
PyPImatplotlib3.7.2indirect
PyPInest-asyncio—indirect
PyPInumpy—indirect
PyPInumpy1.26.3indirect
PyPInumpyro0.15.0indirect
PyPIopenai—indirect
PyPIoptproblems—indirect
PyPIoptquest—indirect
PyPIoptuna—indirect
PyPIoptuna-integration—indirect
PyPIoptunahub—indirect
PyPIpackaging23.1indirect
PyPIpandas—indirect
PyPIparamz0.9.6indirect
PyPIpillow10.0.0indirect
PyPIplotly—indirect
PyPIpyparsing3.0.9indirect
PyPIpytest—indirect
PyPIpython-dateutil2.8.2indirect
PyPIpytorch—indirect
PyPIribs—indirect
PyPIscikit-learn—indirect
PyPIscipy—indirect
PyPIscipy1.11.1indirect
PyPIscipy1.13.1indirect
PyPIsetuptools—indirect
PyPIsimple-hpo-behch—indirect
PyPIsimple-hpo-bench—indirect
PyPIsix1.16.0indirect
PyPIsmac2.4.0indirect
PyPIsyne-tune—indirect
PyPItiktoken—indirect
PyPItorch—indirect
PyPItransformers—indirect
PyPItyping-extensions—indirect
PyPIzipp3.16.2indirect
Dependency advisories 3

This repository publishes no package the index resolves, so its own dependency graph was assessed — 25 packages, which also include development and test pins that never ship: 3 carry known advisories, of which 0 are direct. 34 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
pillow10.0.0indirectcritical3527.0.0-beta.2
fonttools4.42.0indirecthigh44.60.2
zipp3.16.2indirectmoderate23.19.1

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.