Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-05 18:19 UTC

wwPDB / py-wwpdb_utils_config

Configuration utilities for OneDep system

PythonCustom license★ 0 stars⑂ 0 forkssince Oct 2018View on GitHub ↗
KindCommand-line toolhow this is determined

wwPDB/py-wwpdb_utils_config holds a health index of 50 out of 100, placing it in the Moderate band. It scores highest on Sustainability & Governance (72/100) and lowest on Security (21/100). It was last updated 47 days ago. 2 contributors account for most of its recent work.

50
overall / 100
Moderate

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.

50
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 50 is calibrated to 50 on the published index scale (record calibration 2026-08-02).

Ownership

wwPDBOrganization
24 followers71 public repossince Sep 2018

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIwwpdb.utils.configpoints to another repo — not scored1.0.11,81610547 days ago

Metrics by category

Vitality

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

57Moderate · 21% of overall

Development activity

41Weak
How it's scored
18/36Push recency — last push 47 days ago
6.9/36Commit cadence — 10/52 weeks with commits
11.9/18Commit volume — 20 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year20
human_commit_share1
days_since_last_push47
active_weeks_last_year10

Release discipline

80Excellent
How it's scored
16.2/27Ships releases — 37 version tags (no GitHub releases)
36/36Release recency — latest release 47 days ago
19.8/27Release cadence — a release every ~53.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count37
latest_release_tagv1.0.1
releases_from_tagsyes
days_since_latest_release47
mean_days_between_releases53.9

Community & Adoption

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

24At Risk · 17% of overall

Popularity & adoption

6Critical
How it's scored
0/60Stars — 0 stars
0/25Forks — 0 forks
6.5/15Watchers — 16 watchers
Inputs used
forks0
stars0
watchers16
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_badges1
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesdev.azure.com
has_pull_request_templateno

Sustainability & Governance

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

72Good · 23% of overall

Maintainer resilience (bus factor)

60Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.2/22.5Commit distribution — top contributor authored 32% of commits
13.5/13.5Contributor breadth — 10 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled10
top_contributor_share0.324

Issue & PR responsiveness

94Exceptional
How it's scored
0/42Issue resolution — no issues or no data
28.3/30PR acceptance — 33/35 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs33
open_issues0
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio—
closed_unmerged_prs2
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Issue resolution, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

65Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
10.1/25Owner reach — 24 followers of wwPDB
25/25Track record — 71 public repos, account ~7 yr old
Inputs used
followers24
owner_typeOrganization
is_verifiedno
owner_loginwwPDB
public_repos71
account_age_days2,909

Engineering Quality

Are baseline engineering and documentation practices in place?

68Good · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 1 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff]), tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

21At Risk · 16% of overall

Security posture

1Critical
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml, requirements-test.txt, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

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_packages24
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:wwpdb.utils.config@1.0.1 runtime dependency closure — what installing the published package pulls in — 24 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.

40Weak · 4% of overall

Agent context & guidance

8Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
8/40Legible commit history — 15 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.15
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

49Weak
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff]), tox.ini
11/11Static type checking — wwpdb/utils/config/py.typed
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configswwpdb/utils/config/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (wwpdb/utils/config/py.typed)
55/55Manageable file sizes — 0/25 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes48,824
source_files_sampled25
oversized_source_files0

Key facts

0GitHub stars
10contributors
20commits, last 12 months
47days since last push
37releases
2bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • pypi package 'wwpdb.utils.config' points at a different repository (https://github.com/rcsb/py-wwpdb_utils_config); excluded from ecosystem scoring
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPIoslo.concurrency—pyproject.toml
PyPIpython-dateutil—pyproject.toml
All dependencies 4

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

RegistryPackageVersionRelation
PyPIoslo-concurrency—direct
PyPIpython-dateutil—direct
PyPItox—indirect
PyPIwwpdb-utils-testing—indirect
Dependency advisories 0

Installing pypi:wwpdb.utils.config@1.0.1 pulls in 24 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.

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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 v0.34.0 — full methodology · metrics wiki.

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