Public record
Software health reportschema 0.17.0 · metrics 2.10.0 · 2026-07-21 04:05 UTC

Azure / msrestazure-for-python

The runtime library "msrestazure" for AutoRest generated Python clients.

PythonMIT★ 19 stars⑂ 37 forkssince Oct 2016archivedView on GitHub ↗

Azure/msrestazure-for-python holds a health index of 19 out of 100, placing it in the Critical band. It scores highest on Sustainability & Governance (71/100) and lowest on Vitality (21/100). The repository is archived, so no further maintenance is expected.

19
overall / 100
Critical

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.

19
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

Microsoft AzureOrganization
15,051 followers2,723 public repossince Mar 2014

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPImsrestazure0.6.4.post1-52831 days ago

Metrics by category

Vitality

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

21At Risk · 21% of overall
How it's scored
0/36Push recencylast push 875 days ago
0/36Commit cadence0/52 weeks with commits
0/18Commit volume0 commits in the last year
0/10OpenSSF Scorecard: Maintainedproject is archived
Inputs used
commits_last_year0
human_commit_share
days_since_last_push875
active_weeks_last_year0
How it's scored
27/27Ships releases37 releases published
0/36Release recencylatest release 2,212 days ago
19.8/27Release cadencea release every ~83 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count37
latest_release_tagv0.6.4
releases_from_tagsno
days_since_latest_release2,212
mean_days_between_releases83
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?

55Moderate · 17% of overall
How it's scored
20.4/60Stars19 stars
13/25Forks37 forks
12.9/15Watchers206 watchers
Inputs used
forks37
stars19
watchers206
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonbelow_threshold
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
0/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges
has_contributingno
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?

71Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
3.8/22.5Commit distributiontop contributor authored 83% of commits
13.5/13.5Contributor breadth17 contributors
10/10OpenSSF Scorecard: Contributorsproject has 5 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled17
top_contributor_share0.831
How it's scored
42/42Issue resolution100% of issues closed
26.7/30PR acceptance82/92 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
4.5/15OpenSSF Scorecard: Code-ReviewFound 9/29 approved changesets -- score normalized to 3
Inputs used
merged_prs82
open_issues0
closed_issues78
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio1
closed_unmerged_prs10
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
25/25Owner reach15,051 followers of Azure
25/25Track record2,723 public repos, account ~12 yr old
Inputs used
followers15,051
owner_typeOrganization
is_verified
owner_loginAzure
public_repos2,723
account_age_days4,522
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
4/35Publish recencylatest publish 831 days ago
20/20Version history52 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesmsrestazure
ecosystemspypi
any_deprecatedno
min_days_since_publish831

Engineering Quality

Are baseline engineering and documentation practices in place?

54Moderate · 19% of overall
How it's scored
0/24CI workflows
24/24Tests present
16/16Linter configtox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests0 out of 17 merged PRs checked by a CI test -- score normalized to 0
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics
has_wikiyes
homepage
docs_site
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

55Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
0/2.5CI-Tests0 out of 17 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2.2/7.5Code-ReviewFound 9/29 approved changesets -- score normalized to 3
2.5/2.5Contributorsproject has 5 contributing companies or organizations
0/10Dangerous-Workflowno data
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintainedproject is archived
0/5Packagingno data
0/5Pinned-Dependenciesno data
0/5SASTSAST tool is not run on all commits -- score normalized to 0
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsno data
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated12
scorecard_versionv5.5.0
checks_inconclusive6
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): Branch-Protection, Dangerous-Workflow, Packaging, Pinned-Dependencies, Signed-Releases, Token-Permissions. 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_packages18
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:msrestazure@0.6.4.post1 runtime dependency closure — what installing the published package pulls in — 18 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.

32At 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
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configtox.ini
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
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_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance, OpenSSF Scorecard: Pinned-Dependencies. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/23 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes29,612
source_files_sampled23
oversized_source_files0

Key facts

19GitHub stars
17contributors
0commits, last 12 months
875days since last push
37releases
1bus factor
0open issues
PyPIpackage ecosystems

More detail

Star and fork history 19 ★ / 0 ⇿
19Stars

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.

0481216201922016-102020-072024-03

Each point covers 7 days.

OpenSSF Scorecard 4.4 / 10
4.4aggregate

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 04:05 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
0CI-Tests0 out of 17 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3Code-ReviewFound 9/29 approved changesets -- score normalized to 3
10Contributorsproject has 5 contributing companies or organizations
n/aDangerous-Workflowno workflows found
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintainedproject is archived
n/aPackagingpackaging workflow not detected
n/aPinned-Dependenciesno dependencies found
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
n/aToken-PermissionsNo tokens found
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 0

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

RegistryPackageVersionRelation
Dependency advisories 0

Installing pypi:msrestazure@0.6.4.post1 pulls in 18 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.