Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-05 17:53 UTC

lava-nc / lava-dl

Deep Learning library for Lava

Jupyter NotebookBSD-3-Clause★ 183 stars⑂ 80 forkssince Sep 2021archivedView on GitHub ↗

lava-nc/lava-dl holds a health index of 19 out of 100, placing it in the Critical band. It scores highest on Engineering Quality (68/100) and lowest on Vitality (31/100). The repository is archived, so no further maintenance is expected.

19
overall / 100
Critical

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.

19
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 52 is calibrated to 53 on the published index scale (record calibration 2026-08-02).

Ownership

LavaOrganization
238 followers9 public repossince Jun 2021

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIlava-dl0.6.0933758 days agoneuromorphicaiartificial-intelligenceneural-modelsspiking-neural-networksdeep-learning

Metrics by category

Vitality

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

31At Risk · 21% of overall
How it's scored
9.9/36Push recencylast push 114 days ago
2.1/36Commit cadence3/52 weeks with commits
8.1/18Commit volume7 commits in the last year
0/10OpenSSF Scorecard: Maintainedproject is archived
Inputs used
commits_last_year7
human_commit_share0.58
days_since_last_push114
active_weeks_last_year3
How it's scored
27/27Ships releases10 releases published
0/36Release recencylatest release 758 days ago
19.8/27Release cadencea release every ~111 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count10
latest_release_tagv0.6.0
releases_from_tagsno
days_since_latest_release758
mean_days_between_releases111

Community & Adoption

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

54Moderate · 17% of overall
How it's scored
36.7/60Stars183 stars
15.8/25Forks80 forks
6/15Watchers13 watchers
Inputs used
forks80
stars183
watchers13
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
7.2/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingno
has_issue_templateyes
has_code_of_conductno
readme_badge_services
has_pull_request_templateyes
How it's scored
26.3/80Monthly downloads93 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packageslava-dl
dependents
ecosystemspypi
total_downloads
monthly_downloads93
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?

61Moderate · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
13.7/22.5Commit distributiontop contributor authored 39% of commits
13.5/13.5Contributor breadth22 contributors
3/10OpenSSF Scorecard: Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor2
contributors_sampled22
top_contributor_share0.393
How it's scored
32/42Issue resolution76% of issues closed
25.2/30PR acceptance142/169 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/14 approved changesets -- score normalized to 0
Inputs used
merged_prs142
open_issues28
closed_issues90
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.763
closed_unmerged_prs27
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.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
17.1/25Owner reach238 followers of lava-nc
17.7/25Track record9 public repos, account ~5 yr old
Inputs used
followers238
owner_typeOrganization
is_verifiedno
owner_loginlava-nc
public_repos9
account_age_days1,900
How it's scored
25/25Published & resolvable1 package(s) on pypi
4/35Publish recencylatest publish 758 days ago
12/20Version history3 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packageslava-dl
ecosystemspypi
any_deprecatedno
min_days_since_publish758

Engineering Quality

Are baseline engineering and documentation practices in place?

68Good · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.black])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests1 out of 19 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage sitehttps://lava-nc.org
10/10Repository description
10/10Topics6 topics
10/10Wiki
Inputs used
topicsdeep-learning, neural-networks, neuromorphic, neuromorphic-computing, python, pytorch
has_wikiyes
homepagehttps://lava-nc.org
docs_sitehttps://lava-nc.org
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

42Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
0/2.5CI-Tests1 out of 19 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/14 approved changesets -- score normalized to 0
0.8/2.5Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
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
0/7.5Maintainedproject is archived
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
5/5Security-Policysecurity policy file detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
7.5/7.5Token-PermissionsGitHub workflow tokens follow principle of least privilege
0/7.5Vulnerabilities101 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging. Remaining weights renormalized.
How it's scored
5.4/35Direct dependencies free of known advisories3 affected: pillow 10.4.0 (critical 9.1), torch 2.3.1 (critical 9.8), pytest 7.4.4 (moderate 6.8)
8.2/25Indirect dependencies free of known advisories1 affected: asteval 0.9.33 (high 8.4)
21.6/40No advisories left outstanding4 advisory-carrying package(s) unaddressed past 90 days; oldest published 675 days ago
Inputs used
sourceosv
advisories64
affected_packages4
assessed_packages51
unassessed_packages0
affected_by_severitycritical 2, high 1, moderate 1
direct_affected_packages3
Matched the pypi:lava-dl@0.6.0 runtime dependency closure — what installing the published package pulls in — 51 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.

56Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history46 of 58 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.793
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.black])
0/11Static type checking
10/10Reproducible environmentlockfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
8/8Automated maintenance42 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilespoetry.lock
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0.42
How it's scored
0/45Type-checkable codeJupyter Notebook without a type-check config
55/55Manageable file sizes0/135 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes52,899
source_files_sampled135
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesnotebooks
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

183GitHub stars
22contributors
7commits, last 12 months
114days since last push
10releases
2bus factor
28open 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 ★ / 80 ⇿
0Stars
80Forks
10Releases

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.

0204060807932021-102024-022026-05
Major 0Minor 6Patch 4

Each point covers 5 days.

OpenSSF Scorecard 4.4 / 10
4.4aggregate

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 · 2026-09-05 17:53 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-Tests1 out of 19 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 0/14 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintainedproject is archived
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
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
0Vulnerabilities101 existing vulnerabilities detected
Direct dependencies 12
RegistryPackageVersion constraintManifest
PyPIlava-ncpyproject.toml
PyPItorchvision^0.24.0pyproject.toml
PyPIh5py^3.7.0pyproject.toml
PyPIninja^1.10.2.3pyproject.toml
PyPImatplotlib^3.5.2pyproject.toml
PyPInumpy^1.24.4pyproject.toml
PyPIscipy^1.8.1pyproject.toml
PyPIpillow>=10.0.1,<13.0pyproject.toml
PyPIpytest^7.2.0pyproject.toml
PyPIunittest2^1.1.0pyproject.toml
PyPItorch^2.9.0pyproject.toml
PyPIopencv-python-headless^4.8.1.78pyproject.toml
All dependencies 0

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

RegistryPackageVersionRelation
Dependency advisories 4

Installing pypi:lava-dl@0.6.0 pulls in 51 packages, direct and transitive: 4 carry known advisories, of which 3 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
pillow10.4.0directcritical3412.3.0
torch2.3.1directcritical232.13.0
asteval0.9.33indirecthigh51.0.9
pytest7.4.4directmoderate29.0.3

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 statisticsPyPI.