Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-06 18:32 UTC

IntelPython / mkl_umath

Package implementing NumPy's UFuncs based on SVML and MKL VML

PythonBSD-3-Clause★ 7 stars⑂ 8 forkssince Mar 2021View on GitHub ↗

IntelPython/mkl_umath holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Vitality (95/100) and lowest on Community & Adoption (35/100). It was last updated today. 2 contributors account for most of its recent work.

89
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.

89
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 75 is calibrated to 89 on the published index scale (record calibration 2026-08-02).

Ownership

Intel PythonOrganization
133 followers51 public repossince Jul 2016

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImkl_umath0.5.0-120 days agomkl-umath

Metrics by category

Vitality

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

95Exceptional · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
33.2/36Commit cadence48/52 weeks with commits
18/18Commit volume448 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year448
human_commit_share0.91
days_since_last_push0
active_weeks_last_year48
How it's scored
27/27Ships releases12 releases published
36/36Release recencylatest release 36 days ago
19.8/27Release cadencea release every ~69.7 days
8/10OpenSSF Scorecard: Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
Inputs used
releases_count12
latest_release_tag0.4.3
releases_from_tagsno
days_since_latest_release36
mean_days_between_releases69.7

Community & Adoption

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

35Weak · 17% of overall
How it's scored
12.6/60Stars7 stars
7/25Forks8 forks
2.7/15Watchers4 watchers
Inputs used
forks8
stars7
watchers4
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
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesapi.securityscorecards.dev, github.com
has_pull_request_templateno

Sustainability & Governance

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

81Excellent · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
13.6/22.5Commit distributiontop contributor authored 40% of commits
13.5/13.5Contributor breadth14 contributors
10/10OpenSSF Scorecard: Contributorsproject has 6 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled14
top_contributor_share0.396
How it's scored
28/42Issue resolution67% of issues closed
27.5/30PR acceptance224/244 decided PRs merged
13/13Newcomer PR acceptance3/3 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Reviewall changesets reviewed
Inputs used
merged_prs224
open_issues2
closed_issues4
prs_merged_7d4
prs_decided_7d4
prs_merged_30d10
prs_decided_30d10
issue_closed_ratio0.667
closed_unmerged_prs20
first_time_authors_30d1
first_time_prs_merged_30d3
first_time_prs_decided_30d3
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
15.3/25Owner reach133 followers of IntelPython
24.5/25Track record51 public repos, account ~10 yr old
Inputs used
followers133
owner_typeOrganization
is_verified
owner_loginIntelPython
public_repos51
account_age_days3,673
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 0 days ago
20/20Version history12 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesmkl_umath
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

80Excellent · 19% of overall
How it's scored
24/24CI workflows7 workflow(s)
24/24Tests present
16/16Linter config.flake8
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests8 out of 8 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

60Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics3 topics
10/10Wiki
Inputs used
topicsmkl, numpy, python
has_wikiyes
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?

82Excellent · 16% of overall

Security posture

82Excellent
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
3.8/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests8 out of 8 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
7.5/7.5Code-Reviewall changesets reviewed
2.5/2.5Contributorsproject has 6 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
7.5/7.5Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
5/5Pinned-Dependenciesall dependencies are pinned
0/5SASTSAST tool is not run on all commits -- score normalized to 0
5/5Security-Policysecurity policy file detected
6/7.5Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
7.5/7.5Token-PermissionsGitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate8.2
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.

59Moderate · 4% of overall
How it's scored
45/45Agent instructions.github/AGENTS.md, .github/copilot-instructions.md, AGENTS.md, _vendored/AGENTS.md, conda-recipe-cf/AGENTS.md, conda-recipe/AGENTS.md, mkl_umath/AGENTS.md, mkl_umath/src/AGENTS.md, mkl_umath/tests/AGENTS.md
0/15Machine-readable docs (llms.txt)
15.2/40Legible commit history26 of 91 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.286
agent_instruction_files.github/AGENTS.md, .github/copilot-instructions.md, AGENTS.md, _vendored/AGENTS.md, conda-recipe-cf/AGENTS.md, conda-recipe/AGENTS.md, mkl_umath/AGENTS.md, mkl_umath/src/AGENTS.md, mkl_umath/tests/AGENTS.md
agent_instruction_max_bytes4,913
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config.flake8
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice5 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance9 of the last 100 commits are automated dependency updates
10/10OpenSSF Scorecard: Pinned-Dependenciesall dependencies are pinned
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.05
toolchain_manifests
dependency_bot_commit_share0.09
How it's scored
0/45Type-checkable codePython without a type-check config
51.5/55Manageable file sizes2/31 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes144,822
source_files_sampled31
oversized_source_files2

Key facts

7GitHub stars
14contributors
448commits, last 12 months
0days since last push
12releases
2bus factor
2open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:mkl_umath@0.5.0; 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 ★ / 8 ⇿
0Stars
8Forks
11Releases

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.

1234567712023-032024-112026-07
Major 0Minor 3Patch 7

Each point covers 4 days.

OpenSSF Scorecard 8.2 / 10
8.2aggregate

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 · 2026-08-06 18:32 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests8 out of 8 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 1 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
10Pinned-Dependenciesall dependencies are pinned
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
8Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 1
RegistryPackageVersion constraintManifest
PyPInumpy>=1.26.4pyproject.toml
All dependencies 4

Full resolved dependency set from the GitHub dependency graph: 1 direct and 3 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpydirect
PyPImeson-pythonindirect
PyPIpsutilindirect
PyPIsetuptoolsindirect
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 v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.