Public record
Software health reportschema 0.15.0 · metrics 2.10.0 · 2026-07-19 11:58 UTC

DGtal-team / DGtal

Digital Geometry Tools and Algorithm Library

C++LGPL-3.0★ 397 stars⑂ 123 forkssince May 2011View on GitHub ↗

DGtal-team/DGtal holds a health index of 87 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (96/100) and lowest on Security (48/100). It was last updated 27 days ago. 2 contributors account for most of its recent work.

87
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.

87
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 73 is calibrated to 87 on the published index scale (record calibration 2026-08-02).

Ownership

22 followers14 public repossince Aug 2011

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIDGtal2.1.0-9217 days ago

Metrics by category

Vitality

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

69Good · 21% of overall
How it's scored
28.8/36Push recencylast push 27 days ago
21.5/36Commit cadence31/52 weeks with commits
18/18Commit volume436 commits in the last year
6/10OpenSSF Scorecard: Maintained7 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 6
Inputs used
commits_last_year436
human_commit_share
days_since_last_push27
active_weeks_last_year31
How it's scored
27/27Ships releases16 releases published
16.2/36Release recencylatest release 219 days ago
12.6/27Release cadencea release every ~313.9 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count16
latest_release_tagv2.1
releases_from_tagsno
days_since_latest_release219
mean_days_between_releases313.9
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

72Good · 17% of overall
How it's scored
42.1/60Stars397 stars
17.4/25Forks123 forks
8.4/15Watchers34 watchers
Inputs used
forks123
stars397
watchers34
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (LGPL-3.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

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

76Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
12.6/22.5Commit distributiontop contributor authored 44% of commits
13.5/13.5Contributor breadth34 contributors
10/10OpenSSF Scorecard: Contributorsproject has 23 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled34
top_contributor_share0.438
How it's scored
39.3/42Issue resolution94% of issues closed
27.6/30PR acceptance1,195/1,301 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
3/15OpenSSF Scorecard: Code-ReviewFound 2/10 approved changesets -- score normalized to 2
Inputs used
merged_prs1,195
open_issues34
closed_issues497
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.936
closed_unmerged_prs106
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
9.8/25Owner reach22 followers of DGtal-team
20.6/25Track record14 public repos, account ~14 yr old
Inputs used
followers22
owner_typeOrganization
is_verified
owner_loginDGtal-team
public_repos14
account_age_days5,437
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
26/35Publish recencylatest publish 217 days ago
20/20Version history9 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesDGtal
ecosystemspypi
any_deprecatedno
min_days_since_publish217

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests4 out of 4 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 sitehttps://dgtal.org
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicsdigital-geometry, geometry-processing, topology, computational-geometry, discrete-mathematics
has_wikiyes
homepagehttps://dgtal.org
docs_sitehttps://dgtal.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

48Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2.5/2.5CI-Tests4 out of 4 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
1.5/7.5Code-ReviewFound 2/10 approved changesets -- score normalized to 2
2.5/2.5Contributorsproject has 23 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
4.5/7.5Maintained7 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 6
5/5Packagingpackaging workflow detected
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
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.8
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. 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.

66Good · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
18/18One-command bootstrapdoc/doxygen-awesome-css-main/Makefile
22/22Automated tests
11/11Lint / format config
11/11Static type checkingC++ (statically typed)
10/10Reproducible environmentDockerfile
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageyes
bootstrap_filesdoc/doxygen-awesome-css-main/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
45/45Type-checkable codeC++ (statically typed)
54.6/55Manageable file sizes8/1,164 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes279,342
source_files_sampled1,164
oversized_source_files8
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 examplesexamples, samples
Inputs used
example_dirsexamples, samples
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

397GitHub stars
34contributors
436commits, last 12 months
27days since last push
16releases
2bus factor
34open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 4.8 / 10
4.8aggregate

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 · 2026-07-19 11:57 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-Tests4 out of 4 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 2/10 approved changesets -- score normalized to 2
10Contributorsproject has 23 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
6Maintained7 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 6
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
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 2

Full resolved dependency set from the GitHub dependency graph: 0 direct and 2 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpyindirect
PyPIpytestindirect
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 v0.15.0 — full methodology · metrics wiki.

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