Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-19 23:46 UTC

SHI-Labs / NATTEN

Fast Multi-dimensional Sparse Attention

C++ · PythonMIT★ 797 stars⑂ 71 forkssince Oct 2022View on GitHub ↗

SHI-Labs/NATTEN holds a health index of 63 out of 100, placing it in the Moderate band. It scores highest on Vitality (68/100) and lowest on Security (40/100). It was last updated 55 days ago. A single contributor accounts for most of its recent work.

63
overall / 100
Moderate

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.

63
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 59 is calibrated to 63 on the published index scale (record calibration 2026-08-02).

Ownership

SHI LabsOrganization
510 followers65 public repossince Apr 2020

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPInatten0.21.7-1955 days agoattentionsparse-attentionneighborhood-attentiondeep-learningmachine-learningmlartificial-intelligenceai

Metrics by category

Vitality

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

68Good · 21% of overall
How it's scored
18/36Push recency — last push 55 days ago
15.9/36Commit cadence — 23/52 weeks with commits
14.9/18Commit volume — 45 commits in the last year
9/10OpenSSF Scorecard: Maintained — 3 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 9
Inputs used
commits_last_year45
human_commit_share1
days_since_last_push55
active_weeks_last_year23
How it's scored
27/27Ships releases — 19 releases published
36/36Release recency — latest release 55 days ago
19.8/27Release cadence — a release every ~70.2 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count19
latest_release_tagv0.21.7
releases_from_tagsno
days_since_latest_release55
mean_days_between_releases70.2

Community & Adoption

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

60Moderate · 17% of overall
How it's scored
47.1/60Stars — 797 stars
15.4/25Forks — 71 forks
6/15Watchers — 13 watchers
Inputs used
forks71
stars797
watchers13
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
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_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

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

63Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
2.5/22.5Commit distribution — top contributor authored 89% of commits
13.5/13.5Contributor breadth — 11 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled11
top_contributor_share0.887
How it's scored
38.7/42Issue resolution — 92% of issues closed
27/30PR acceptance — 133/148 decided PRs merged
0/13Newcomer PR acceptance — 0/2 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs133
open_issues15
closed_issues175
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d2
issue_closed_ratio0.921
closed_unmerged_prs15
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d2
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
19.5/25Owner reach — 510 followers of SHI-Labs
25/25Track record — 65 public repos, account ~6 yr old
Inputs used
followers510
owner_typeOrganization
is_verifiedno
owner_loginSHI-Labs
public_repos65
account_age_days2,340

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 — 19 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesnatten
ecosystemspypi
any_deprecatedno
min_days_since_publish55

Engineering Quality

Are baseline engineering and documentation practices in place?

60Moderate · 19% of overall
How it's scored
0/24CI workflows
24/24Tests present
16/16Linter config — .flake8
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 30 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://natten.org
10/10Repository description
10/10Topics — 4 topics
0/10Wiki
Inputs used
topicscuda, pytorch, neighborhood-attention, sparse-attention
has_wikino
homepagehttps://natten.org
docs_sitehttps://natten.org
has_readmeyes
has_docs_diryes
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
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
0/2.5CI-Tests — 0 out of 30 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
0/10Dangerous-Workflow — no data
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
6.8/7.5Maintained — 3 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 9
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — no data
0.8/7.5Vulnerabilities — 9 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated14
scorecard_versionv5.5.0
checks_inconclusive4
scorecard_aggregate2.4
Excluded from scoring (no data or not applicable): Branch-Protection, Dangerous-Workflow, Packaging, Token-Permissions. Remaining weights renormalized.

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
advisories4
affected_packages2
assessed_packages14
unassessed_packages7
affected_by_severitymoderate 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 14 resolved dependencies against OSV. 7 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.

65Good · 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 — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — .flake8
11/11Static type checking — C++ (statically typed)
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageyes
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
45/45Type-checkable code — C++ (statically typed)
52.9/55Manageable file sizes — 8/209 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes106,686
source_files_sampled209
oversized_source_files8

Key facts

797GitHub stars
11contributors
45commits, last 12 months
55days since last push
19releases
1bus factor
15open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'natten' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 71 ⇿
0Stars
71Forks
19Releases

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.

01325385063757132022-102024-092026-08
Major 0Minor 4Patch 15

Each point covers 4 days.

OpenSSF Scorecard 2.4 / 10
2.4aggregate

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 · 2026-09-19 23:45 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
0CI-Tests0 out of 30 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
n/aDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
9Maintained3 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 9
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
n/aToken-PermissionsNo tokens found
1Vulnerabilities9 existing vulnerabilities detected
All dependencies 21

Full resolved dependency set from the GitHub dependency graph: 0 direct and 21 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIbuild—indirect
PyPIcmake4.3.1indirect
PyPIflake87.0.0indirect
PyPIitanium-demangler1.1indirect
PyPImarkdown-grid-tables0.4.0indirect
PyPImkdocs1.6.1indirect
PyPImkdocs-glightbox0.4.0indirect
PyPImkdocs-material9.6.14indirect
PyPImkdocstrings-python1.16.11indirect
PyPImypy1.8.0indirect
PyPIpackaging—indirect
PyPIpygments—indirect
PyPIpymdown-extensions—indirect
PyPIpytest7.4.4indirect
PyPIrich14.0.0indirect
PyPIruff0.11.12indirect
PyPIsetuptools—indirect
PyPItorch—indirect
PyPItqdm4.67.1indirect
PyPItwine—indirect
PyPIufmt2.3.0indirect
Dependency advisories 2

This repository publishes no package the index resolves, so its own dependency graph was assessed — 14 packages, which also include development and test pins that never ship: 2 carry known advisories, of which 0 are direct. 7 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
mkdocs-material9.6.14indirectmoderate29.7.7
pytest7.4.4indirectmoderate29.0.3

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 — PyPI.