Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-10 08:48 UTC

yang-zhang / product_category

Product cateogry prediction

PythonNo license detected★ 21 stars⑂ 13 forkssince Mar 2021View on GitHub ↗

yang-zhang/product_category holds a health index of 16 out of 100, placing it in the Critical band. It scores highest on Engineering Quality (39/100) and lowest on Security (10/100). It was last updated 1993 days ago. A single contributor accounts for most of its recent work.

16
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.

16
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 25 is calibrated to 19 on the published index scale (record calibration 2026-08-02).

Ownership

Yang ZhangPersonal account
58 followers64 public repossince Dec 2012

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 publish
PyPIproduct_category0.0.21122009 days ago

Metrics by category

Vitality

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

18Critical · 21% of overall
How it's scored
0/36Push recency — last push 1,993 days ago
0/36Commit cadence — 0/52 weeks with commits
0/18Commit volume — 0 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year0
human_commit_share1
days_since_last_push1,993
active_weeks_last_year0
How it's scored
27/27Ships releases — 1 releases published
0/36Release recency — latest release 2,009 days ago
12.6/27Release cadence — cadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count1
latest_release_tagv0.0.1
releases_from_tagsno
days_since_latest_release2,009
mean_days_between_releases—

Community & Adoption

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

25At Risk · 17% of overall
How it's scored
21.1/60Stars — 21 stars
9/25Forks — 13 forks
0/15Watchers — 1 watchers
Inputs used
forks13
stars21
watchers1
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
0/22.5License — no license file detected
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseno
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno
How it's scored
14.4/80Monthly downloads — 11 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesproduct_category
dependents—
ecosystemspypi
total_downloads—
monthly_downloads11
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

31At Risk · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1
How it's scored
0/42Issue resolution — 0% of issues closed
0/30PR acceptance — no decided pull requests or no data
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs0
open_issues5
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): PR acceptance, Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
12.7/25Owner reach — 58 followers of yang-zhang
25/25Track record — 64 public repos, account ~13 yr old
Inputs used
followers58
owner_typeUser
is_verified—
owner_loginyang-zhang
public_repos64
account_age_days5,010
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
4/35Publish recency — latest publish 2,009 days ago
12/20Version history — 2 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesproduct_category
ecosystemspypi
any_deprecatedno
min_days_since_publish2,009

Engineering Quality

Are baseline engineering and documentation practices in place?

39Weak · 19% of overall
How it's scored
0/24CI workflows
0/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_cino
has_testsno
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

50Moderate
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
10/10Wiki
Inputs used
topics—
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

10Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsrequirements.txt, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
Excluded from scoring (no data or not applicable): Dependency lockfiles. Remaining weights renormalized.
How it's scored
3.9/35Direct dependencies free of known advisories — 5 affected: black 20.8.0b1 (critical 9.8), pytorch-lightning 1.2.1 (critical 9.8), torch 1.7.1 (critical 9.8), +2 more
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
18.5/40No advisories left outstanding — 5 advisory-carrying package(s) unaddressed past 90 days; oldest published 1,721 days ago
Inputs used
sourceosv
advisories98
affected_packages5
assessed_packages53
unassessed_packages0
affected_by_severitycritical 4, high 1
direct_affected_packages5
Matched the pypi:product_category@0.0.2 runtime dependency closure — what installing the published package pulls in — 53 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.

16Critical · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — 0 of 24 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
0/22Automated tests
11/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no agent-authored commits among the last 24
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsno
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/5 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes6,578
source_files_sampled5
oversized_source_files0

Key facts

21GitHub stars
1contributors
0commits, last 12 months
1,993days since last push
1releases
1bus factor
5open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 13 ⇿
0Stars
13Forks

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.

03581013151322021-032023-022025-01

Each point covers 4 days.

All dependencies 6

Full resolved dependency set from the GitHub dependency graph: 0 direct and 6 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIblack20.8b1indirect
PyPIpre-commit2.10.1indirect
PyPIpytorch-lightning1.2.1indirect
PyPIscikit-learn0.24.1indirect
PyPItorch1.7.1indirect
PyPItransformers4.3.3indirect
Dependency advisories 5

Installing pypi:product_category@0.0.2 pulls in 53 packages, direct and transitive: 5 carry known advisories, of which 5 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
black20.8.0b1directcritical426.3.1
pytorch-lightning1.2.1directcritical102.4.0
torch1.7.1directcritical302.13.0
transformers4.3.3directcritical515.10.0
scikit-learn0.24.1directhigh31.5.0

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 v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.