Public record
Software health reportschema 0.17.0 · metrics 2.10.0 · 2026-07-21 03:29 UTC

vsergeev / u-msgpack-python

A portable, lightweight MessagePack serializer and deserializer written in pure Python, compatible with Python 2, Python 3, CPython, PyPy / msgpack.org[Python]

PythonMIT★ 268 stars⑂ 42 forkssince Sep 2013View on GitHub ↗

vsergeev/u-msgpack-python holds a health index of 50 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (78/100) and lowest on Security (31/100). It was last updated 12 days ago. A single contributor accounts 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

Vanya SergeevPersonal account
394 followers85 public repossince Dec 2010

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
PyPIu-msgpack-python2.8.0-181159 days agomsgpackserializationdeserialization

Metrics by category

Vitality

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

35Weak · 21% of overall
How it's scored
28.8/36Push recencylast push 12 days ago
0/36Commit cadence0/52 weeks with commits
0/18Commit volume0 commits in the last year
0/10OpenSSF Scorecard: Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Inputs used
commits_last_year0
human_commit_share
days_since_last_push12
active_weeks_last_year0
How it's scored
27/27Ships releases13 releases published
0/36Release recencylatest release 1,159 days ago
12.6/27Release cadencea release every ~246.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count13
latest_release_tagv2.8.0
releases_from_tagsno
days_since_latest_release1,159
mean_days_between_releases246.5
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?

55Moderate · 17% of overall
How it's scored
39.4/60Stars268 stars
13.4/25Forks42 forks
6.4/15Watchers15 watchers
Inputs used
forks42
stars268
watchers15
growth_stateorganic
growth_factor_pct100
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
2/22.5Commit distributiontop contributor authored 91% of commits
13.5/13.5Contributor breadth10 contributors
0/10OpenSSF Scorecard: Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled10
top_contributor_share0.911
How it's scored
42/42Issue resolution100% of issues closed
1.6/30PR acceptance1/19 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs1
open_issues0
closed_issues30
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio1
closed_unmerged_prs18
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
18.7/25Owner reach394 followers of vsergeev
25/25Track record85 public repos, account ~15 yr old
Inputs used
followers394
owner_typeUser
is_verified
owner_loginvsergeev
public_repos85
account_age_days5,696
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 1,159 days ago
20/20Version history18 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesu-msgpack-python
ecosystemspypi
any_deprecatedno
min_days_since_publish1,159

Engineering Quality

Are baseline engineering and documentation practices in place?

78Good · 19% of overall
How it's scored
24/24CI workflows1 workflow(s)
24/24Tests present
16/16Linter config.flake8, tox.ini
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.
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?

31At Risk · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionbranch protection not enabled on development/release branches
0/2.5CI-Testsno data
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/30 approved changesets -- score normalized to 0
0/2.5Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
0/7.5Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTno SAST tool detected
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate3.1
Excluded from scoring (no data or not applicable): CI-Tests, 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.

39Weak · 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 bootstrapdocs/Makefile
22/22Automated tests
11/11Lint / format config.flake8, tox.ini
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile
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. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/4 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes43,617
source_files_sampled4
oversized_source_files0

Key facts

268GitHub stars
10contributors
0commits, last 12 months
12days since last push
13releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • No resolved dependencies carried a version and a supported ecosystem

More detail

Star and fork history 268 ★ / 0 ⇿
268Stars

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.

05010015020025030026872013-102020-022026-07

Each point covers 12 days.

OpenSSF Scorecard 3.1 / 10
3.1aggregate

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 03:29 UTC

10Binary-Artifactsno binaries found in the repo
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
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 2

Full resolved dependency set from the GitHub dependency graph: 0 direct and 2 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPImyst-parserindirect
PyPIsphinx-rtd-themeindirect
Dependency advisories not assessed

Advisory matching could not run for this report: No resolved dependencies carried a version and a supported ecosystem

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.