Public record
Software health reportschema 0.17.0 · metrics 2.10.0 · 2026-07-21 03:45 UTC

arviz-devs / arviz

Exploratory analysis of Bayesian models with Python

TeX · PythonApache-2.0★ 1,836 stars⑂ 502 forkssince Jul 2015View on GitHub ↗

arviz-devs/arviz holds a health index of 97 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (96/100) and lowest on AI Readiness (43/100). It was last updated today. 3 contributors account for most of its recent work.

97
overall / 100
Exceptional

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.

97
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 85 is calibrated to 97 on the published index scale (record calibration 2026-08-02).

Ownership

ArviZOrganization
94 followers32 public repossince Jul 2015

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIarviz1.2.0-4938 days ago

Metrics by category

Vitality

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

84Excellent · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
18.7/36Commit cadence27/52 weeks with commits
15.6/18Commit volume53 commits in the last year
9/10OpenSSF Scorecard: Maintained9 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 9
Inputs used
commits_last_year53
human_commit_share
days_since_last_push0
active_weeks_last_year27
How it's scored
27/27Ships releases48 releases published
36/36Release recencylatest release 38 days ago
19.8/27Release cadencea release every ~51.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count48
latest_release_tagv1.2.0
releases_from_tagsno
days_since_latest_release38
mean_days_between_releases51.5
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?

88Excellent · 17% of overall
How it's scored
52.9/60Stars1,836 stars
22.5/25Forks502 forks
9.4/15Watchers50 watchers
Inputs used
forks502
stars1,836
watchers50
growth_stateorganic
growth_factor_pct100

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code 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_conductyes
readme_badge_services
has_pull_request_templateyes

Sustainability & Governance

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

85Excellent · 23% of overall
How it's scored
36/54Bus factor3 contributor(s) cover half of all commits
18.4/22.5Commit distributiontop contributor authored 18% of commits
13.5/13.5Contributor breadth100 contributors
10/10OpenSSF Scorecard: Contributorsproject has 36 contributing companies or organizations
Inputs used
bus_factor3
contributors_sampled100
top_contributor_share0.181
How it's scored
37.4/42Issue resolution89% of issues closed
27.1/30PR acceptance1,474/1,632 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
9/15OpenSSF Scorecard: Code-ReviewFound 18/27 approved changesets -- score normalized to 6
Inputs used
merged_prs1,474
open_issues100
closed_issues818
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.891
closed_unmerged_prs158
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
14.2/25Owner reach94 followers of arviz-devs
23.1/25Track record32 public repos, account ~10 yr old
Inputs used
followers94
owner_typeOrganization
is_verified
owner_loginarviz-devs
public_repos32
account_age_days4,009
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 38 days ago
20/20Version history49 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesarviz
ecosystemspypi
any_deprecatedno
min_days_since_publish38

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows2 workflow(s)
24/24Tests present
16/16Linter configtox.ini
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests27 out of 27 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://python.arviz.org
10/10Repository description
10/10Topics3 topics
10/10Wiki
Inputs used
topicspython, bayesian, closember
has_wikiyes
homepagehttps://python.arviz.org
docs_sitehttps://python.arviz.org
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

82Excellent · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
3.8/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.5/2.5CI-Tests27 out of 27 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
4.5/7.5Code-ReviewFound 18/27 approved changesets -- score normalized to 6
2.5/2.5Contributorsproject has 36 contributing companies or organizations
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
6.8/7.5Maintained9 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 9
5/5Packagingpackaging workflow detected
2.5/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
1/5SASTSAST tool is not run on all commits -- score normalized to 2
4.5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
7.5/7.5Token-PermissionsGitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate7.7
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages13
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:arviz@1.2.0 runtime dependency closure — what installing the published package pulls in — 13 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.

43Weak · 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
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configtox.ini
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
5/8Automated maintenancedependency automation configured, none observed in the sampled commits
5/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.
How it's scored
0/45Type-checkable codeTeX without a type-check config
55/55Manageable file sizes0/6 source files over 60KB
Inputs used
primary_languageTeX
largest_source_bytes9,870
source_files_sampled6
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 examplesexamples, notebooks
Inputs used
example_dirsexamples, notebooks
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

1,836GitHub stars
100contributors
53commits, last 12 months
0days since last push
48releases
3bus factor
100open issues
PyPIpackage ecosystems

More detail

Star and fork history 1,836 ★ / 0 ⇿
1,836Stars

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

5001,0001,5002,0001,836152020-102023-082026-07

Each point covers 6 days.

OpenSSF Scorecard 7.7 / 10
7.7aggregate

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 · 2026-07-21 03:44 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests27 out of 27 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 18/27 approved changesets -- score normalized to 6
10Contributorsproject has 36 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
9Maintained9 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 9
10Packagingpackaging workflow detected
5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
2SASTSAST tool is not run on all commits -- score normalized to 2
9Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPIarviz_base>=1.2.0,<1.3.0pyproject.toml
PyPIarviz_stats>=1.2.0,<1.3.0pyproject.toml
PyPIarviz_plots>=1.2.0,<1.3.0pyproject.toml
All dependencies 6

Full resolved dependency set from the GitHub dependency graph: 3 direct and 3 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIarviz-basedirect
PyPIarviz-plotsdirect
PyPIarviz-statsdirect
PyPIflit-coreindirect
PyPIpydata-sphinx-themeindirect
PyPIsphinxindirect
Dependency advisories 0

Installing pypi:arviz@1.2.0 pulls in 13 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.

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 v0.17.0 — full methodology · metrics wiki.

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