Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-22 21:23 UTC

NVIDIA / cccl

CUDA Core Compute Libraries

C++ · CudaCustom license★ 2,544 stars⑂ 529 forkssince Sep 2020View on GitHub ↗

NVIDIA/cccl holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (96/100) and lowest on Security (59/100). It was last updated today. 6 contributors account for most of its recent work.

98
overall / 100
Exceptional

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.

98
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 86 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

NVIDIA CorporationOrganization · verified domain
31,651 followers827 public repossince May 2012

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIcuda-cccl1.2.15,7872824 days ago

Metrics by category

Vitality

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

96Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 2,986 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year2,986
human_commit_share0.99
days_since_last_push0
active_weeks_last_year52
How it's scored
27/27Ships releases — 58 releases published
36/36Release recency — latest release 15 days ago
27/27Release cadence — a release every ~12.6 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count58
latest_release_tagv3.5.0
releases_from_tagsno
days_since_latest_release15
mean_days_between_releases12.6

Community & Adoption

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

80Excellent · 17% of overall
How it's scored
55.2/60Stars — 2,544 stars
22.7/25Forks — 529 forks
8.4/15Watchers — 33 watchers
Inputs used
forks529
stars2,544
watchers33
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

86Excellent
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
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_badges1
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com
has_pull_request_templateyes
How it's scored
50.2/80Monthly downloads — 5,787 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagescuda-cccl
dependents—
ecosystemspypi
total_downloads—
monthly_downloads5,787
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?

90Excellent · 23% of overall
How it's scored
48.6/54Bus factor — 6 contributor(s) cover half of all commits
18.4/22.5Commit distribution — top contributor authored 18% of commits
13.5/13.5Contributor breadth — 97 contributors
10/10OpenSSF Scorecard: Contributors — project has 24 contributing companies or organizations
Inputs used
bus_factor6
contributors_sampled97
top_contributor_share0.181
How it's scored
27.6/42Issue resolution — 66% of issues closed
26.9/30PR acceptance — 6,542/7,289 decided PRs merged
3.2/13Newcomer PR acceptance — 1/4 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs6,542
open_issues1,482
closed_issues2,857
prs_merged_7d49
prs_decided_7d58
prs_merged_30d49
prs_decided_30d58
issue_closed_ratio0.658
closed_unmerged_prs747
first_time_authors_30d2
first_time_prs_merged_30d1
first_time_prs_decided_30d4
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 31,651 followers of NVIDIA
25/25Track record — 827 public repos, account ~14 yr old
Inputs used
followers31,651
owner_typeOrganization
is_verifiedyes
owner_loginNVIDIA
public_repos827
account_age_days5,277

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 24 days ago
20/20Version history — 28 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagescuda-cccl
ecosystemspypi
any_deprecatedno
min_days_since_publish24

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows — 34 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff]), python/cuda_cccl/pyproject.toml ([tool.ruff]), python/cuda_coop/pyproject.toml ([tool.ruff]), python/cuda_stf/pyproject.toml ([tool.ruff])
9.6/9.6Pre-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_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://nvidia.github.io/cccl/
10/10Repository description
10/10Topics — 19 topics
10/10Wiki
Inputs used
topicsaccelerated-computing, cpp, cpp-programming, cuda, cuda-cpp, cuda-kernels, cuda-library, cuda-programming, gpu, gpu-acceleration, gpu-computing, gpu-programming, hpc, nvidia, nvidia-gpu, parallel-algorithm, parallel-computing, parallel-programming, modern-cpp
has_wikiyes
homepagehttps://nvidia.github.io/cccl/
docs_sitehttps://nvidia.github.io/cccl/
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
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
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
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 24 contributing companies or organizations
0/10Dangerous-Workflow — dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
1/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
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
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.9
Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages4
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:cuda-cccl@1.2.1 runtime dependency closure — what installing the published package pulls in — 4 packages. 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.

86Excellent · 4% of overall
How it's scored
45/45Agent instructions — .cursor/rules/tuning-analysis.mdc, .github/copilot-instructions.md, AGENTS.md, CLAUDE.md, python/cuda_cccl/benchmarks/compute/AGENTS.md, python/cuda_coop/AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.nvidia.com/llms.txt)
40/40Legible commit history — 85 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.nvidia.com/llms.txt
legible_history_share0.859
agent_instruction_files.cursor/rules/tuning-analysis.mdc, .github/copilot-instructions.md, AGENTS.md, CLAUDE.md, python/cuda_cccl/benchmarks/compute/AGENTS.md, python/cuda_coop/AGENTS.md
agent_instruction_max_bytes15,805
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff]), python/cuda_cccl/pyproject.toml ([tool.ruff]), python/cuda_coop/pyproject.toml ([tool.ruff]), python/cuda_stf/pyproject.toml ([tool.ruff])
11/11Static type checking — python/cuda_cccl/cuda/cccl/py.typed, python/cuda_coop/cuda/coop/numba_mlir/py.typed, python/cuda_coop/cuda/coop/py.typed, python/cuda_stf/cuda/stf/_experimental/py.typed
10/10Reproducible environment — devcontainer
10/10Demonstrated agent practice — 17 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
2/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageyes
bootstrap_files—
has_devcontaineryes
has_linter_configyes
typecheck_configspython/cuda_cccl/cuda/cccl/py.typed, python/cuda_coop/cuda/coop/numba_mlir/py.typed, python/cuda_coop/cuda/coop/py.typed, python/cuda_stf/cuda/stf/_experimental/py.typed
agent_commit_share0.17
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
45/45Type-checkable code — C++ (statically typed)
54.7/55Manageable file sizes — 34/6,982 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes372,082
source_files_sampled6,982
oversized_source_files34
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
40/40Runnable examples — examples, notebooks, sample
Inputs used
example_dirsexamples, notebooks, sample
has_mcp_signalyes
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

2,544GitHub stars
97contributors
2,986commits, last 12 months
0days since last push
58releases
6bus factor
1,482open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 15 authors (cap 12)
  • Could not fetch pypi package 'cuda-stf' from its registry
  • Could not fetch pypi package 'cuda-coop' from its registry

More detail

Star and fork history 0 ★ / 529 ⇿
0Stars
529Forks
58Releases

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.

0100200300400500600518102020-102023-102026-10
Major 1Minor 12Patch 23

Each point covers 6 days.

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 · 2026-10-22 21:23 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
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
10Code-Reviewall changesets reviewed
10Contributorsproject has 24 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
2Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 2
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
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
PyPInumpy—python/cuda_cccl/pyproject.toml
PyPIcuda-pathfinder>=1.2.3python/cuda_cccl/pyproject.toml
PyPIcuda-core—python/cuda_cccl/pyproject.toml
PyPItyping_extensions—python/cuda_cccl/pyproject.toml
PyPInumpy—python/cuda_stf/pyproject.toml
PyPIcuda-pathfinder>=1.2.3python/cuda_stf/pyproject.toml
PyPIcuda-core—python/cuda_stf/pyproject.toml
PyPItyping_extensions—python/cuda_stf/pyproject.toml
All dependencies 23

Full resolved dependency set from the GitHub dependency graph: 4 direct and 19 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcuda-core—direct
PyPIcuda-pathfinder—direct
PyPInumpy—direct
PyPItyping-extensions—direct
PyPIbreathe—indirect
PyPIcmake—indirect
PyPIcuda-bindings—indirect
PyPIcuda-toolkit—indirect
PyPIcython—indirect
PyPIexhale—indirect
PyPImyst-parser—indirect
PyPInbsphinx—indirect
PyPInumba—indirect
PyPInumba-cuda—indirect
PyPInumba-cuda-mlir—indirect
PyPInumpydoc—indirect
PyPInvidia-nvjitlink-cu12—indirect
PyPInvidia-sphinx-theme—indirect
PyPIpyright1.1.414indirect
PyPIscikit-build-core—indirect
PyPIsphinx—indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-design—indirect
Dependency advisories 0

Installing pypi:cuda-cccl@1.2.1 pulls in 4 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

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.