Public record
Software health reportschema 0.16.0 · metrics 2.10.0 · 2026-07-21 02:46 UTC

sergey-dryabzhinsky / python-zstd

Simple python bindings to Yann Collet ZSTD compression library

C · PythonBSD-2-Clause★ 187 stars⑂ 33 forkssince Mar 2015View on GitHub ↗

sergey-dryabzhinsky/python-zstd holds a health index of 11 out of 100, placing it in the Critical band. It scores highest on Vitality (72/100) and lowest on Security (9/100). It was last updated 9 days ago. A single contributor accounts for most of its recent work.

11
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.

11
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 54 is calibrated to 56 on the published index scale (record calibration 2026-08-02). High-Risk Jurisdiction Policy applies a 20% multiplier to weighted overall health and gives it an At Risk ceiling of 34.

Ownership

Sergey DryabzhinskyPersonal account
127 followers28 public repossince Apr 2011Rusoft

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
PyPIzstd1.5.7.2-49392 days agozstdzstandardcompression

Metrics by category

Vitality

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

72Good · 21% of overall
How it's scored
28.8/36Push recencylast push 9 days ago
15.9/36Commit cadence23/52 weeks with commits
18/18Commit volume182 commits in the last year
10/10OpenSSF Scorecard: Maintained7 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year182
human_commit_share
days_since_last_push9
active_weeks_last_year23
How it's scored
27/27Ships releases70 releases published
16.2/36Release recencylatest release 193 days ago
27/27Release cadencea release every ~36.3 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count70
latest_release_tagv1.5.7.3
releases_from_tagsno
days_since_latest_release193
mean_days_between_releases36.3

Community & Adoption

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

52Moderate · 17% of overall
How it's scored
36.8/60Stars187 stars
12.5/25Forks33 forks
3.9/15Watchers6 watchers
Inputs used
forks33
stars187
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (BSD-2-Clause)
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?

60Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0.8/22.5Commit distributiontop contributor authored 96% of commits
13.5/13.5Contributor breadth14 contributors
10/10OpenSSF Scorecard: Contributorsproject has 5 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled14
top_contributor_share0.964
How it's scored
36.5/42Issue resolution87% of issues closed
27.7/30PR acceptance196/212 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-ReviewFound 1/7 approved changesets -- score normalized to 1
Inputs used
merged_prs196
open_issues14
closed_issues93
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.869
closed_unmerged_prs16
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
15.1/25Owner reach127 followers of sergey-dryabzhinsky
22.6/25Track record28 public repos, account ~15 yr old
Inputs used
followers127
owner_typeUser
is_verified
owner_loginsergey-dryabzhinsky
public_repos28
account_age_days5,582
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable1 package(s) on pypi
14/35Publish recencylatest publish 392 days ago
20/20Version history49 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packageszstd
ecosystemspypi
any_deprecatedno
min_days_since_publish392

Engineering Quality

Are baseline engineering and documentation practices in place?

71Good · 19% of overall
How it's scored
24/24CI workflows43 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests5 out of 5 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics5 topics
0/10Wiki
Inputs used
topicsc, python, zstd, zstandard, compression
has_wikino
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?

9Critical · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2.5/2.5CI-Tests5 out of 5 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
0.8/7.5Code-ReviewFound 1/7 approved changesets -- score normalized to 1
2.5/2.5Contributorsproject has 5 contributing companies or organizations
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
7.5/7.5Maintained7 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.4
high_risk_jurisdiction_cap34
high_risk_jurisdiction_multiplier20
security_posture_after_multiplier9
security_posture_before_jurisdiction44
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging. Remaining weights renormalized. High-Risk Jurisdiction Policy applies a 20% multiplier and gives Security posture an At risk ceiling of 34.

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.

37Weak · 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
0/11Lint / format config
11/11Static type checkingC (statically typed)
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_languageyes
bootstrap_files
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
45/45Type-checkable codeC (statically typed)
55/55Manageable file sizes0/18 source files over 60KB
Inputs used
primary_languageC
largest_source_bytes26,605
source_files_sampled18
oversized_source_files0

Key facts

187GitHub stars
14contributors
182commits, last 12 months
9days since last push
70releases
1bus factor
14open issues
PyPIpackage ecosystems

More detail

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 02:46 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
10CI-Tests5 out of 5 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/7 approved changesets -- score normalized to 1
10Contributorsproject has 5 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained7 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
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 not assessed

Advisory matching could not run for this report: No resolved dependencies to assess

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 v0.16.0 — full methodology · metrics wiki.

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