Public record
Software health reportschema 0.11.0 · metrics 2.10.0 · 2026-07-15 23:59 UTC

scikit-maad / scikit-maad

Open-source and modular toolbox for quantitative soundscape analysis in Python

PythonBSD-3-Clause★ 135 stars⑂ 25 forkssince Sep 2018View on GitHub ↗

scikit-maad/scikit-maad holds a health index of 69 out of 100, placing it in the Good band. It scores highest on Engineering Quality (75/100) and lowest on AI Readiness (33/100). It was last updated 55 days ago. 2 contributors account for most of its recent work.

69
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.

69
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 62 is calibrated to 69 on the published index scale (record calibration 2026-08-02).

Ownership

scikit-maadPersonal account
35 followers2 public repossince Sep 2018

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIscikit-maad1.5.2-14152 days agoecoacousticsbioacousticsecologysound-pressure-levelsignal-processing

Metrics by category

Vitality

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

50Moderate · 21% of overall
How it's scored
18/36Push recencylast push 55 days ago
3.5/36Commit cadence5/52 weeks with commits
11.7/18Commit volume19 commits in the last year
1/10OpenSSF Scorecard: Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
Inputs used
commits_last_year19
human_commit_share
days_since_last_push55
active_weeks_last_year5
How it's scored
27/27Ships releases13 releases published
27/36Release recencylatest release 152 days ago
12.6/27Release cadencea release every ~134.7 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count13
latest_release_tagv1.5.2
releases_from_tagsno
days_since_latest_release152
mean_days_between_releases134.7
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?

67Good · 17% of overall
How it's scored
34.5/60Stars135 stars
11.5/25Forks25 forks
5/15Watchers9 watchers
Inputs used
forks25
stars135
watchers9
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (BSD-3-Clause)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

69Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
11.9/22.5Commit distributiontop contributor authored 47% of commits
12.2/13.5Contributor breadth9 contributors
10/10OpenSSF Scorecard: Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor2
contributors_sampled9
top_contributor_share0.471
How it's scored
35.2/42Issue resolution84% of issues closed
23.4/30PR acceptance50/64 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
9/15OpenSSF Scorecard: Code-ReviewFound 3/5 approved changesets -- score normalized to 6
Inputs used
merged_prs50
open_issues8
closed_issues41
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.837
closed_unmerged_prs14
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
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
11.2/25Owner reach35 followers of scikit-maad
15.5/25Track record2 public repos, account ~7 yr old
Inputs used
followers35
owner_typeUser
is_verified
owner_loginscikit-maad
public_repos2
account_age_days2,865
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 152 days ago
20/20Version history14 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesscikit-maad
ecosystemspypi
any_deprecatedno
min_days_since_publish152

Engineering Quality

Are baseline engineering and documentation practices in place?

75Good · 19% of overall
How it's scored
24/24CI workflows2 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
10/20OpenSSF Scorecard: CI-Tests2 out of 4 merged PRs checked by a CI test -- score normalized to 5
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://scikit-maad.github.io/
10/10Repository description
10/10Topics6 topics
10/10Wiki
Inputs used
topicsecoacoustics, bioacoustics, signal-processing, sound-pressure-level, pattern-recognition, acoustic-indices
has_wikiyes
homepagehttps://scikit-maad.github.io/
docs_sitehttps://scikit-maad.github.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

52Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
1.2/2.5CI-Tests2 out of 4 merged PRs checked by a CI test -- score normalized to 5
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
4.5/7.5Code-ReviewFound 3/5 approved changesets -- score normalized to 6
2.5/2.5Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
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
0.8/7.5Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
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
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_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate5.2
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, 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.

33At Risk · 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 bootstrapdocs/Makefile
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
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_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configno
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
0/45Type-checkable codePython without a type-check config
53.1/55Manageable file sizes2/58 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes150,519
source_files_sampled58
oversized_source_files2

Key facts

135GitHub stars
9contributors
19commits, last 12 months
55days since last push
13releases
2bus factor
8open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 5.2 / 10
5.2aggregate

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 · 2026-07-15 23:59 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during GetBranch(master): error during branchesHandler.query: 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
5CI-Tests2 out of 4 merged PRs checked by a CI test -- score normalized to 5
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 3/5 approved changesets -- score normalized to 6
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
1Maintained2 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 1
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
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 6
RegistryPackageVersion constraintManifest
PyPInumpy>=1.21pyproject.toml
PyPIscipy>=1.8pyproject.toml
PyPIscikit-image>=0.23.1pyproject.toml
PyPIpandas>=1.5pyproject.toml
PyPImatplotlib>=3.6pyproject.toml
PyPIpywavelets>=1.4pyproject.toml
All dependencies 6

Full resolved dependency set from the GitHub dependency graph: 6 direct and 0 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImatplotlibdirect
PyPInumpydirect
PyPIpandasdirect
PyPIpywaveletsdirect
PyPIscikit-imagedirect
PyPIscipydirect
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 v0.11.0 — full methodology · metrics wiki.

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