Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 03:42 UTC

qurator-spk / eynollah

Document Layout Analysis

PythonApache-2.0★ 414 stars⑂ 36 forkssince Nov 2020View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

qurator-spk/eynollah holds a health index of 71 out of 100, placing it in the Good band. It scores highest on Engineering Quality (82/100) and lowest on Security (21/100). It was last updated 50 days ago. 2 contributors account for most of its recent work.

71
overall / 100
Good

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.

71
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 63 is calibrated to 71 on the published index scale (record calibration 2026-08-02).

Ownership

IDM 4 Data ScienceOrganization
47 followers44 public repossince Jul 2019

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIeynollah0.9.23052350 days agodocument-layout-analysisimage-segmentationbinarizationoptical-character-recognition

Metrics by category

Vitality

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

79Good · 21% of overall
How it's scored
18/36Push recency — last push 50 days ago
27/36Commit cadence — 39/52 weeks with commits
18/18Commit volume — 708 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year708
human_commit_share1
days_since_last_push50
active_weeks_last_year39
How it's scored
27/27Ships releases — 25 releases published
36/36Release recency — latest release 50 days ago
19.8/27Release cadence — a release every ~53 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count25
latest_release_tagv0.9.2
releases_from_tagsno
days_since_latest_release50
mean_days_between_releases53

Community & Adoption

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

53Moderate · 17% of overall
How it's scored
42.4/60Stars — 414 stars
12.9/25Forks — 36 forks
7/15Watchers — 19 watchers
Inputs used
forks36
stars414
watchers19
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
33.1/80Monthly downloads — 305 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageseynollah
dependents—
ecosystemspypi
total_downloads—
monthly_downloads305
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?

73Good · 23% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
12.5/22.5Commit distribution — top contributor authored 44% of commits
12.2/13.5Contributor breadth — 9 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled9
top_contributor_share0.443
How it's scored
32.2/42Issue resolution — 77% of issues closed
24.8/30PR acceptance — 90/109 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs90
open_issues26
closed_issues85
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.766
closed_unmerged_prs19
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
12.1/25Owner reach — 47 followers of qurator-spk
24/25Track record — 44 public repos, account ~7 yr old
Inputs used
followers47
owner_typeOrganization
is_verifiedno
owner_loginqurator-spk
public_repos44
account_age_days2,604

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 50 days ago
20/20Version history — 23 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageseynollah
ecosystemspypi
any_deprecatedno
min_days_since_publish50

Engineering Quality

Are baseline engineering and documentation practices in place?

82Excellent · 19% of overall
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 5 topics
10/10Wiki
Inputs used
topicssegmentation, document-layout-analysis, ocr, textline-detection, binarization
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?

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml, requirements-ocr.txt, requirements-plotting.txt, requirements-test.txt, requirements-training.txt, requirements.txt, train/requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages100
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:eynollah@0.9.2 runtime dependency closure — what installing the published package pulls in — 100 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.

48Weak · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
17.6/40Legible commit history — 33 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.33
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — Makefile, models/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile, models/Makefile
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
51.9/55Manageable file sizes — 4/70 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes113,858
source_files_sampled70
oversized_source_files4

Key facts

414GitHub stars
9contributors
708commits, last 12 months
50days since last push
25releases
2bus factor
26open 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 ★ / 36 ⇿
0Stars
36Forks
25Releases

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.

01325383622020-122023-102026-08
Major 0Minor 9Patch 14

Each point covers 6 days.

All dependencies 24

Full resolved dependency set from the GitHub dependency graph: 0 direct and 24 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIblack—indirect
PyPIcoverage—indirect
PyPIimutils—indirect
PyPImatplotlib—indirect
PyPInumba—indirect
PyPInumpy—indirect
PyPIocrd—indirect
PyPIocrd-fork-sacred—indirect
PyPIprotobuf—indirect
PyPIpytest—indirect
PyPIpytest-isolate—indirect
PyPIscikit-image—indirect
PyPIscikit-learn—indirect
PyPIscipy—indirect
PyPIseaborn—indirect
PyPIsetuptools—indirect
PyPItabulate—indirect
PyPItensorflow—indirect
PyPItensorflow-addons—indirect
PyPItf-data—indirect
PyPItf-keras—indirect
PyPItorch—indirect
PyPItqdm—indirect
PyPItransformers—indirect
Dependency advisories 0

Installing pypi:eynollah@0.9.2 pulls in 100 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 v0.34.0 — full methodology · metrics wiki.

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