Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-09 16:56 UTC

gilad-rubin / hypster

HyPSTER - Configuration Framework for Optimizing AI & AI Systems

Python · JavaScriptMIT★ 60 stars⑂ 2 forkssince Jun 2019View on GitHub ↗
KindLibraryhow this is determined
#hyperparameter-optimization#auto-ml#machine-learning#generative-ai

gilad-rubin/hypster holds a health index of 81 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (96/100) and lowest on Sustainability & Governance (52/100). It was last updated 7 days ago. A single contributor accounts 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

Gilad RubinPersonal account
11 followers18 public repossince Dec 2013

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
PyPIhypster0.9.14176855 days agoaiconfigurationmachine-learning

Metrics by category

Vitality

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

78Good · 21% of overall

Development activity

70Good
How it's scored
36/36Push recency — last push 7 days ago
5.5/36Commit cadence — 8/52 weeks with commits
18/18Commit volume — 132 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 26 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year132
human_commit_share1
days_since_last_push7
active_weeks_last_year8

Release discipline

90Excellent
How it's scored
27/27Ships releases — 11 releases published
36/36Release recency — latest release 55 days ago
27/27Release cadence — a release every ~35.8 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count11
latest_release_tagv0.9.1
releases_from_tagsno
days_since_latest_release55
mean_days_between_releases35.8

Community & Adoption

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

55Moderate · 17% of overall

Popularity & adoption

29At Risk
How it's scored
28.7/60Stars — 60 stars
0/25Forks — 2 forks
0/15Watchers — 2 watchers
Inputs used
forks2
stars60
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges7
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicescodecov.io, github.com, shields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

44Weak
How it's scored
34.9/80Monthly downloads — 417 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageshypster
dependents—
ecosystemspypi
total_downloads—
monthly_downloads417
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

52Moderate · 23% of overall

Maintainer resilience (bus factor)

12Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.2/22.5Commit distribution — top contributor authored 99% of commits
2.7/13.5Contributor breadth — 2 contributors
0/10OpenSSF Scorecard: Contributors — project has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.991

Issue & PR responsiveness

64Moderate
How it's scored
39.4/42Issue resolution — 94% of issues closed
16.6/30PR acceptance — 47/85 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/13 approved changesets -- score normalized to 0
Inputs used
merged_prs47
open_issues2
closed_issues31
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.939
closed_unmerged_prs38
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

49Weak
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
7.8/25Owner reach — 11 followers of gilad-rubin
21.3/25Track record — 18 public repos, account ~12 yr old
Inputs used
followers11
owner_typeUser
is_verified—
owner_logingilad-rubin
public_repos18
account_age_days4,658
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 55 days ago
20/20Version history — 68 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageshypster
ecosystemspypi
any_deprecatedno
min_days_since_publish55

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-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_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://gilad-rubin.gitbook.io/hypster
10/10Repository description
10/10Topics — 4 topics
10/10Wiki
Inputs used
topicshyperparameter-optimization, auto-ml, machine-learning, generative-ai
has_wikiyes
homepagehttps://gilad-rubin.gitbook.io/hypster
docs_sitehttps://gilad-rubin.gitbook.io/hypster
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

59Moderate · 16% of overall

Security posture

49Weak
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 — 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
0/7.5Code-Review — Found 0/13 approved changesets -- score normalized to 0
0/2.5Contributors — project has 0 contributing companies or organizations -- score normalized to 0
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 26 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
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
1.5/7.5Vulnerabilities — 8 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate4.9

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
advisories11
affected_packages2
assessed_packages276
unassessed_packages0
affected_by_severityhigh 2
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 276 resolved dependencies against OSV. 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.

85Excellent · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://gilad-rubin.gitbook.io/llms.txt)
40/40Legible commit history — 83 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://gilad-rubin.gitbook.io/llms.txt
legible_history_share0.83
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes796

Verify loop (build / test / typecheck)

70Good
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — packages/react/tsconfig.json, src/hypster/py.typed
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 25 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
1/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
Inputs used
has_nixno
has_testsyes
lockfilespackage-lock.json, uv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configspackages/react/tsconfig.json, src/hypster/py.typed
agent_commit_share0.25
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (packages/react/tsconfig.json, src/hypster/py.typed)
55/55Manageable file sizes — 0/71 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes36,346
source_files_sampled71
oversized_source_files0

Machine-readable interfaces

100Exceptional
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 — examples, notebooks
Inputs used
example_dirsexamples, notebooks
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

60GitHub stars
2contributors
132commits, last 12 months
7days since last push
11releases
1bus factor
2open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 2 ⇿
0Stars
2Forks

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.

111222212019-082019-102019-12
OpenSSF Scorecard 4.9 / 10
4.9aggregate

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 · 2026-09-09 16:55 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-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
0Code-ReviewFound 0/13 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 26 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
1Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 1
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
2Vulnerabilities8 existing vulnerabilities detected
All dependencies 276

Full resolved dependency set from the GitHub dependency graph: 0 direct and 276 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIsetuptools83.0.0indirect
PyPIsix1.17.0indirect
PyPIsoupsieve2.8.4indirect
PyPIsqlalchemy2.0.48indirect
PyPIstack-data0.6.3indirect
PyPIterminado0.18.1indirect
PyPItinycss21.5.1indirect
PyPItomli2.4.0indirect
PyPItornado6.5.7indirect
PyPItqdm4.67.3indirect
PyPItraitlets5.14.3indirect
PyPItraitlets5.15.1indirect
PyPItyping-extensions4.15.0indirect
PyPItyping-extensions4.16.0indirect
PyPItzdata2026.3indirect
PyPIuri-template1.3.0indirect
PyPIurllib32.7.0indirect
PyPIvirtualenv21.2.0indirect
PyPIwcwidth0.6.0indirect
PyPIwcwidth0.8.2indirect
PyPIwebcolors25.10.0indirect
PyPIwebencodings0.5.1indirect
PyPIwebsocket-client1.9.0indirect
PyPIwidgetsnbextension4.0.15indirect
Dependency advisories 2

This repository publishes no package the index resolves, so its own dependency graph was assessed — 276 packages, which also include development and test pins that never ship: 2 carry known advisories, of which 0 are direct.

PackageVersionRelationSeverityAdvisoriesFixed in
jupyterlab4.5.9indirecthigh84.6.2
tornado6.5.7indirecthigh36.5.8

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

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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.