Public record
Software health reportschema 0.11.0 · metrics 2.10.0 · 2026-07-16 12:57 UTC

hmandela / WASS2S

PythonCustom license★ 0 stars⑂ 3 forkssince Oct 2024View on GitHub ↗
KindLibraryhow this is determined

hmandela/WASS2S holds a health index of 28 out of 100, placing it in the At Risk band. It scores highest on Vitality (48/100) and lowest on AI Readiness (18/100). It was last updated today. A single contributor accounts for most of its recent work.

28
overall / 100
At Risk

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.

28
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 34 is calibrated to 28 on the published index scale (record calibration 2026-08-02).

Ownership

Mandela HOUNGNIBOPersonal account
9 followers24 public repossince Nov 2017@AGRHYMET

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
PyPIwass2s0.4.9.6-820 days agoclimateseasonal-forecastingsubseasonal-forecastingmachine-learningdata-downloadingdownscaling

Metrics by category

Vitality

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

48Weak · 21% of overall

Development activity

80Excellent
How it's scored
36/36Push recency — last push 0 days ago
15.9/36Commit cadence — 23/52 weeks with commits
18/18Commit volume — 106 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_year106
human_commit_share—
days_since_last_push0
active_weeks_last_year23

Release discipline

1Critical
How it's scored
0/27Ships releases — no releases published
0/36Release recency — no releases
0/27Release cadence — no releases
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count0
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?

22At Risk · 17% of overall

Popularity & adoption

2Critical
How it's scored
0/60Stars — 0 stars
2.5/25Forks — 3 forks
0/15Watchers — 1 watchers
Inputs used
forks3
stars0
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

44Weak
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges—
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

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

36Weak · 23% of overall

Maintainer resilience (bus factor)

13Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1

Issue & PR responsiveness

1Critical
How it's scored
0/42Issue resolution — 0% of issues closed
0/30PR acceptance — no decided pull requests or no data
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs0
open_issues1
closed_issues0
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio0
closed_unmerged_prs0
first_time_authors_30d—
first_time_prs_merged_30d—
first_time_prs_decided_30d—
Excluded from scoring (no data or not applicable): PR acceptance, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

49Weak
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
7.2/25Owner reach — 9 followers of hmandela
22.2/25Track record — 24 public repos, account ~8 yr old
Inputs used
followers9
owner_typeUser
is_verified—
owner_loginhmandela
public_repos24
account_age_days3,158
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

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 — 82 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageswass2s
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

34At Risk · 19% of overall

Engineering practices

30At Risk
How it's scored
0/24CI workflows
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

40Weak
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
0/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionno

Security

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

27At Risk · 16% of overall

Security posture

27At Risk
How it's scored
0/7.5Binary-Artifacts — binaries present in source code
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
0/10Dangerous-Workflow — no data
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 1 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — no data
0/5SAST — no SAST tool detected
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — no data
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated12
scorecard_versionv5.5.0
checks_inconclusive6
scorecard_aggregate2.7
Excluded from scoring (no data or not applicable): CI-Tests, Dangerous-Workflow, Packaging, Pinned-Dependencies, Signed-Releases, Token-Permissions. 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.

18Critical · 4% of overall

Agent context & guidance

1Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no 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.

Verify loop (build / test / typecheck)

31At Risk
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no data
0/8Automated maintenance — no data
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
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, OpenSSF Scorecard: Pinned-Dependencies. Remaining weights renormalized.

Code legibility for models

17Critical
How it's scored
0/45Type-checkable code — Python without a type-check config
17.1/55Manageable file sizes — 20/29 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes561,615
source_files_sampled29
oversized_source_files20

Key facts

0GitHub stars
1contributors
106commits, last 12 months
0days since last push
0releases
1bus factor
1open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 2.7 / 10
2.7aggregate

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 · 2026-07-16 12:57 UTC

0Binary-Artifactsbinaries present in source code
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
n/aDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
n/aPinned-Dependenciesno dependencies found
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
n/aToken-PermissionsNo tokens found
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 19
RegistryPackageVersion constraintManifest
PyPIstatsmodels—pyproject.toml
PyPIscipy==1.11.3pyproject.toml
PyPImatplotlib==3.7.3pyproject.toml
PyPIxeofs==3.0.4pyproject.toml
PyPIcdsapi==0.7.4pyproject.toml
PyPIcartopy==0.25.*pyproject.toml
PyPIrasterio—pyproject.toml
PyPIgeopandas—pyproject.toml
PyPIshapely—pyproject.toml
PyPIrioxarray—pyproject.toml
PyPISkillMetrics—pyproject.toml
PyPIoptuna—pyproject.toml
PyPIearthkit—pyproject.toml
PyPIipywidgets—pyproject.toml
PyPIregionmask—pyproject.toml
PyPIpycountry—pyproject.toml
PyPIcca-zoo—pyproject.toml
PyPIeccodes—pyproject.toml
PyPIcfgrib—pyproject.toml
All dependencies 6

Full resolved dependency set from the GitHub dependency graph: 5 direct and 1 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcartopy—direct
PyPIcdsapi0.7.4direct
PyPImatplotlib3.7.3direct
PyPIscipy1.11.3direct
PyPIxeofs3.0.4direct
PyPIsetuptools—indirect
Raw JSON report machine-readable

Feedback

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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 v0.11.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.