Public record
Software health reportschema 0.14.0 · metrics 2.10.0 · 2026-07-19 05:08 UTC

marcuxyz / mvc-flask

You can use the mvc-flask extension to turn on MVC pattern in your applications.

HTML · PythonMIT★ 55 stars⑂ 6 forkssince Sep 2021View on GitHub ↗
KindNetwork serviceCommand-line toolhow this is determined

marcuxyz/mvc-flask holds a health index of 60 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (74/100) and lowest on AI Readiness (41/100). It was last updated 5 days ago. A single contributor accounts for most of its recent work.

60
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.

60
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 57 is calibrated to 60 on the published index scale (record calibration 2026-08-02).

Ownership

Marcus ViníciusPersonal account
256 followers36 public repossince Nov 2014

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
PyPIflask-mvc2points to another repo — not scored0.4.074175 days agoarchitectureclicrudflaskflask-extensiongeneratormvcrest-apiscaffoldweb-framework

Metrics by category

Vitality

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

70Good · 21% of overall
How it's scored
36/36Push recencylast push 5 days ago
2.1/36Commit cadence3/52 weeks with commits
9/18Commit volume9 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_year9
human_commit_share
days_since_last_push5
active_weeks_last_year3
How it's scored
27/27Ships releases8 releases published
36/36Release recencylatest release 5 days ago
19.8/27Release cadencea release every ~52.3 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count8
latest_release_tagv0.4.0
releases_from_tagsno
days_since_latest_release5
mean_days_between_releases52.3
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?

43Weak · 17% of overall
How it's scored
28.1/60Stars55 stars
5.8/25Forks6 forks
2.7/15Watchers4 watchers
Inputs used
forks6
stars55
watchers4
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
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?

50Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
2.7/22.5Commit distributiontop contributor authored 88% of commits
2.7/13.5Contributor breadth2 contributors
6/10OpenSSF Scorecard: Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.882
How it's scored
42/42Issue resolution100% of issues closed
20.3/30PR acceptance46/68 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/15 approved changesets -- score normalized to 0
Inputs used
merged_prs46
open_issues0
closed_issues24
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio1
closed_unmerged_prs22
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
17.3/25Owner reach256 followers of marcuxyz
23.4/25Track record36 public repos, account ~11 yr old
Inputs used
followers256
owner_typeUser
is_verified
owner_loginmarcuxyz
public_repos36
account_age_days4,277
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall
How it's scored
24/24CI workflows1 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests12 out of 15 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://marcuxyz.github.io/flask-mvc2/
10/10Repository description
10/10Topics6 topics
0/10Wiki
Inputs used
topicsflask, flask-extensions, flask-mvc, flask-mvc-structure, flask-mvc-template, flask-template
has_wikino
homepagehttps://marcuxyz.github.io/flask-mvc2/
docs_sitehttps://marcuxyz.github.io/flask-mvc2/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

49Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2/2.5CI-Tests12 out of 15 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/15 approved changesets -- score normalized to 0
1.5/2.5Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
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
0/5Packagingno data
2.5/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
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
1.5/7.5Vulnerabilities8 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4.9
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.

41Weak · 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 bootstrapmakefile
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environmentlockfile
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
5/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesmakefile
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 codeHTML without a type-check config
53.5/55Manageable file sizes2/75 source files over 60KB
Inputs used
primary_languageHTML
largest_source_bytes677,463
source_files_sampled75
oversized_source_files2

Key facts

55GitHub stars
2contributors
9commits, last 12 months
5days since last push
8releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • pypi package 'flask-mvc2' points at a different repository (https://github.com/marcuxyz/flask-mvc); excluded from ecosystem scoring

More detail

OpenSSF Scorecard 4.9 / 10
4.9aggregate

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 · 2026-07-19 05:08 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
8CI-Tests12 out of 15 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/15 approved changesets -- score normalized to 0
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
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
n/aPackagingpackaging workflow not detected
5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 5
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
2Vulnerabilities8 existing vulnerabilities detected
Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPIFlask>=3.0.0,<4.0.0pyproject.toml
PyPIclick>=8.0.0,<9.0.0pyproject.toml
PyPIJinja2>=3.1.0,<4.0.0pyproject.toml
PyPImethod-override>=0.3.0,<0.4.0pyproject.toml
All dependencies 92

Full resolved dependency set from the GitHub dependency graph: 4 direct and 88 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIclick8.4.2direct
PyPIflask3.1.3direct
PyPIjinja23.1.6direct
PyPImethod-override0.3.0direct
PyPIasttokens3.0.1indirect
PyPIbabel2.18.0indirect
PyPIbackrefs7.0indirect
PyPIbandit1.9.4indirect
PyPIbeautifulsoup44.15.0indirect
PyPIblack25.12.0indirect
PyPIblinker1.9.0indirect
PyPIcertifi2026.6.17indirect
PyPIcfgv3.5.0indirect
PyPIcharset-normalizer3.4.9indirect
PyPIcolorama0.4.6indirect
PyPIcoverage7.15.0indirect
PyPIcssselect1.4.0indirect
PyPIdecorator5.3.1indirect
PyPIdistlib0.4.3indirect
PyPIeditorconfig0.17.1indirect
PyPIexecuting2.2.1indirect
PyPIfilelock3.29.7indirect
PyPIflake86.1.0indirect
PyPIflask-sqlalchemy3.1.1indirect
PyPIflaskmvc0.2.0indirect
PyPIghp-import2.1.0indirect
PyPIgreenlet3.5.3indirect
PyPIidentify2.6.19indirect
PyPIidna3.18indirect
PyPIiniconfig2.3.0indirect
PyPIipython8.39.0indirect
PyPIisort5.13.2indirect
PyPIitsdangerous2.2.0indirect
PyPIjedi0.20.0indirect
PyPIjsbeautifier2.0.3indirect
PyPIlibrt0.13.0indirect
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PyPImkdocs-get-deps0.2.2indirect
PyPImkdocs-material9.7.6indirect
PyPImkdocs-material-extensions1.3.1indirect
PyPImkdocs-mermaid2-plugin1.2.3indirect
PyPImypy1.20.2indirect
PyPImypy-extensions1.1.0indirect
PyPInodeenv1.10.0indirect
PyPIpackaging26.2indirect
PyPIpaginate0.5.7indirect
PyPIparso0.8.7indirect
PyPIpathspec1.1.1indirect
PyPIpexpect4.9.0indirect
PyPIplatformdirs4.10.0indirect
PyPIpluggy1.6.0indirect
PyPIpre-commit3.8.0indirect
PyPIprompt-toolkit3.0.52indirect
PyPIptyprocess0.7.0indirect
PyPIpure-eval0.2.3indirect
PyPIpycodestyle2.11.1indirect
PyPIpyflakes3.1.0indirect
PyPIpygments2.20.0indirect
PyPIpymdown-extensions11.0.1indirect
PyPIpytest8.4.2indirect
PyPIpytest-cov4.1.0indirect
PyPIpytest-mock3.15.1indirect
PyPIpython-dateutil2.9.0.post0indirect
PyPIpython-discovery1.4.4indirect
PyPIpytokens0.4.1indirect
PyPIpyyaml6.0.3indirect
PyPIpyyaml-env-tag1.1indirect
PyPIrequests2.34.2indirect
PyPIrich13.9.4indirect
PyPIsetuptools83.0.0indirect
PyPIsix1.17.0indirect
PyPIsoupsieve2.8.4indirect
PyPIsplinter0.19.0indirect
PyPIsqlalchemy2.0.51indirect
PyPIstack-data0.6.3indirect
PyPIstevedore5.9.0indirect
PyPItraitlets5.15.1indirect
PyPItyping-extensions4.16.0indirect
PyPIurllib31.26.20indirect
PyPIvirtualenv21.6.0indirect
PyPIwatchdog6.0.0indirect
PyPIwcwidth0.8.2indirect
PyPIwerkzeug3.1.8indirect
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 v0.14.0 — full methodology · metrics wiki.

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