Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-13 07:20 UTC

cubert-hyperspectral / cuvis-ai

Cuvis.AI is an open-source, extensible framework for building modular AI pipelines that process, analyze, and deploy hyperspectral video data in real-time applications.

Jupyter Notebook · PythonApache-2.0★ 15 stars⑂ 0 forkssince Jan 2026View on GitHub ↗
KindLibraryCommand-line toolhow this is determined
#ai#computer-vision#data-pipelines#edge-ai#hyperspectral-imaging#machine#modular-architecture#open-source#plugin-system#real-time#remote-sensing#spectral-analysis#video-processing

cubert-hyperspectral/cuvis-ai holds a health index of 78 out of 100, placing it in the Good band. It scores highest on Engineering Quality (90/100) and lowest on Community & Adoption (46/100). It was last updated today. A single contributor accounts for most of its recent work.

78
overall / 100
Good

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.

78
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 67 is calibrated to 78 on the published index scale (record calibration 2026-08-02).

Ownership

Cubert HyperspectralOrganization
43 followers44 public repossince Jan 2023

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIcuvis-ai0.18.42,057500 days ago

Metrics by category

Vitality

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

88Excellent · 21% of overall

Development activity

87Excellent
How it's scored
36/36Push recency — last push 0 days ago
22.8/36Commit cadence — 33/52 weeks with commits
18/18Commit volume — 124 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year124
human_commit_share0.93
days_since_last_push0
active_weeks_last_year33

Release discipline

90Excellent
How it's scored
27/27Ships releases — 52 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~2.9 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count52
latest_release_tagv0.18.4
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases2.9

Community & Adoption

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

46Weak · 17% of overall

Popularity & adoption

19Critical
How it's scored
18.6/60Stars — 15 stars
0/25Forks — 0 forks
0/15Watchers — 2 watchers
Inputs used
forks0
stars15
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

55Moderate
How it's scored
44.2/80Monthly downloads — 2,057 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagescuvis-ai
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,057
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?

53Moderate · 23% of overall

Maintainer resilience (bus factor)

22At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
3.1/22.5Commit distribution — top contributor authored 86% of commits
4.1/13.5Contributor breadth — 3 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled3
top_contributor_share0.862

Issue & PR responsiveness

43Weak
How it's scored
10.5/42Issue resolution — 25% of issues closed
25.7/30PR acceptance — 114/133 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-Review — Found 4/30 approved changesets -- score normalized to 1
Inputs used
merged_prs114
open_issues3
closed_issues1
prs_merged_7d7
prs_decided_7d8
prs_merged_30d37
prs_decided_30d38
issue_closed_ratio0.25
closed_unmerged_prs19
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

61Moderate
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
11.8/25Owner reach — 43 followers of cubert-hyperspectral
19.5/25Track record — 44 public repos, account ~3 yr old
Inputs used
followers43
owner_typeOrganization
is_verifiedno
owner_logincubert-hyperspectral
public_repos44
account_age_days1,361

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 50 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagescuvis-ai
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall

Engineering practices

84Excellent
How it's scored
24/24CI workflows — 9 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://docs.cuvis.ai/
10/10Repository description
10/10Topics — 13 topics
10/10Wiki
Inputs used
topicsai, computer-vision, data-pipelines, edge-ai, hyperspectral-imaging, machine-, modular-architecture, open-source, plugin-system, real-time, remote-sensing, spectral-analysis, video-processing
has_wikiyes
homepagehttps://docs.cuvis.ai/
docs_sitehttps://docs.cuvis.ai/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

51Moderate · 16% of overall

Security posture

51Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 30 out of 30 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
0.8/7.5Code-Review — Found 4/30 approved changesets -- score normalized to 1
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
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 1 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
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 — detected GitHub workflow tokens with excessive permissions
5.2/7.5Vulnerabilities — 3 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate5.1

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.

69Good · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — .github/copilot-instructions.md, CLAUDE.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 92 of 93 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.989
agent_instruction_files.github/copilot-instructions.md, CLAUDE.md
agent_instruction_max_bytes8,027

Verify loop (build / test / typecheck)

51Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — Dockerfile, lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
8/8Automated maintenance — 5 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0.05

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Jupyter Notebook without a type-check config
54.8/55Manageable file sizes — 1/253 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes156,428
source_files_sampled253
oversized_source_files1

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 — 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

15GitHub stars
3contributors
124commits, last 12 months
0days since last push
52releases
1bus factor
3open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • deps.dev does not index pypi:cuvis-ai@0.18.4; advisories assessed against the repository dependency graph instead

More detail

OpenSSF Scorecard 5.1 / 10
5.1aggregate

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 · 2026-10-13 07:20 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 4/30 approved changesets -- score normalized to 1
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
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
10Packagingpackaging workflow 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.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7Vulnerabilities3 existing vulnerabilities detected
Direct dependencies 33
RegistryPackageVersion constraintManifest
PyPIcuvis-ai-core>=0.18.1pyproject.toml
PyPIcuvis-ai-schemas>=0.14.0,<0.15pyproject.toml
PyPIpydantic>=2.13.3,<3.0.0pyproject.toml
PyPIdefusedxml>=0.7.1pyproject.toml
PyPImarkupsafe>=3.0.3pyproject.toml
PyPImatplotlib>=3.10.9pyproject.toml
PyPIopencv-python-headless>=4.13.0.92pyproject.toml
PyPIimageio-ffmpeg>=0.6.0pyproject.toml
PyPIgitpython>=3.1.62pyproject.toml
PyPIpyyaml>=6.0.3pyproject.toml
PyPIscikit-image>=0.26.0pyproject.toml
PyPIscikit-learn>=1.8.0pyproject.toml
PyPItensorboard>=2.20.0pyproject.toml
PyPIsetuptools>=81.0.0,<85pyproject.toml
PyPItorch>=2.11.0pyproject.toml
PyPItorch>=2.11.0pyproject.toml
PyPItorchvision>=0.26.0pyproject.toml
PyPItorchvision>=0.26.0pyproject.toml
PyPItorchmetrics>=1.9.0pyproject.toml
PyPIpytorch-lightning>=2.6.6pyproject.toml
PyPItzdata>=2026.2pyproject.toml
PyPIloguru>=0.7.3pyproject.toml
PyPIpackaging>=26.2pyproject.toml
PyPIomegaconf>=2.3.0pyproject.toml
PyPIhydra-core>=1.3.7pyproject.toml
PyPIdotenv>=0.9.9pyproject.toml
PyPIregex>=2026.4.4pyproject.toml
PyPIhuggingface-hub>=1.12.0pyproject.toml
PyPItransformers>=5.16.1pyproject.toml
PyPIhf-xet>=1.4.3pyproject.toml
PyPIopencv-python>=4.13.0.92pyproject.toml
PyPIrequests>=2.33.1pyproject.toml
PyPIscipy>=1.17.1pyproject.toml
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

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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.