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Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-28 14:46 UTC

NaturalHistoryMuseum / pyzbar

Read one-dimensional barcodes and QR codes from Python 2 and 3.

PythonMIT★ 827 Sterne⑂ 196 Forksseit Nov. 2016Auf GitHub ansehen ↗

NaturalHistoryMuseum/pyzbar erreicht einen Gesundheitsindex von 25 von 100 und liegt damit im Bereich Kritisch. Am stärksten schneidet es bei Engineering Quality (68/100) ab, am schwächsten bei Vitality (13/100). Zuletzt vor 1018 Tagen aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.

25
gesamt / 100
Kritisch

Software-Gesundheitsindex

Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.

25
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
VitalitätCommunity &VerbreitungNachhaltigkeit &GovernanceEngineering-QualitätSicherheitAI Readiness

Bewertungsprofil

Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.

Abandonment Policy applies a 60% multiplier to weighted overall health and gives it a ceiling of 49.

Eigentümerschaft

92 Follower190 öffentliche Reposseit Aug. 2013

Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.

Metriken nach Kategorie

Vitalität

Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?

13Kritisch · 22 % des Gesamtindex
Wie die Bewertung erfolgt
0/36Push-Aktualität — letzter Push vor 1.018 Tagen
0/36Commit-Rhythmus — 0/52 Wochen mit Commits
0/18Commit-Volumen — 0 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — keine Daten
Verwendete Eingangsdaten
commits_last_year0
human_commit_share1
days_since_last_push1.018
active_weeks_last_year0

Release-Disziplin

32Gefährdet
Wie die Bewertung erfolgt
16.2/27Liefert Releases aus — 10 Versions-Tags (keine GitHub-Releases)
0/36Release-Aktualität — letztes Release vor 1.737 Tagen
12.6/27Release-Rhythmus — ein Release etwa alle 201,3 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count10
latest_release_tagv0.1.9
releases_from_tagsja
days_since_latest_release1.737
mean_days_between_releases201,3

Community & Verbreitung

Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?

63Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
47.3/60Stars — 827 Stars
19.1/25Forks — 196 Forks
7.5/15Watcher — 23 Watcher
Verwendete Eingangsdaten
forks196
stars827
watchers23
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (MIT)
0/18CONTRIBUTING-Leitfaden
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
0/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingnein
has_issue_templatenein
has_code_of_conductnein
has_pull_request_templatenein

Nachhaltigkeit & Governance

Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?

45Gefährdet · 24 % des Gesamtindex
Wie die Bewertung erfolgt
9/54Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab
2/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 91 % der Commits
6.8/13.5Breite der Beitragenden — 5 Beitragende
0/10OpenSSF Scorecard: Contributors — keine Daten
Verwendete Eingangsdaten
bus_factor1
contributors_sampled5
top_contributor_share0,912
Wie die Bewertung erfolgt
16.2/46.8Issue-Lösungsquote — 35 % der Issues geschlossen
27.5/38.3PR-Annahme — 23/32 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — keine Daten
Verwendete Eingangsdaten
merged_prs23
open_issues83
closed_issues44
issue_closed_ratio0,346
closed_unmerged_prs9
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
14.2/25Reichweite des Inhabers — 92 Follower von NaturalHistoryMuseum
25/25Kontohistorie — 190 öffentliche Repos, Kontoalter ca. 12 Jahre
Verwendete Eingangsdaten
followers92
owner_typeOrganization
is_verified
owner_loginNaturalHistoryMuseum
public_repos190
account_age_days4.726

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

68Mittel · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 1 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration — tox.ini
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — keine Daten
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_confignein
Wie die Bewertung erfolgt
30/30README
0/25Dokumentationsverzeichnis
0/15Dokumentations-/Homepage-Site
10/10Repository-Beschreibung
0/10Topics
10/10Wiki
Verwendete Eingangsdaten
topics
has_wikija
homepage
has_readmeja
has_docs_dirnein
has_descriptionja

Sicherheit

Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?

21Kritisch · 16 % des Gesamtindex
Wie die Bewertung erfolgt
0/30Sicherheitsrichtlinie (SECURITY.md)
0/25Dependabot-Konfiguration
0/25Lockfiles für Abhängigkeiten
0/20CodeQL-Workflow
Verwendete Eingangsdaten
sourcefile_signals
lockfiles
manifestsrequirements-test.txt, requirements.txt, setup.cfg, setup.py
has_codeql_workflownein
has_security_policynein
has_dependabot_confignein
Wie die Bewertung erfolgt
35/35Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory
0/25Indirekte Abhängigkeiten ohne bekannte Advisories — transitive Menge in diesem Bereich nicht von Entwicklungs- und Test-Abhängigkeiten trennbar
0/40Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum
Verwendete Eingangsdaten
sourceosv
advisories0
affected_packages0
assessed_packages1
unassessed_packages12
affected_by_severitynone
direct_affected_packages0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Indirekte Abhängigkeiten ohne bekannte Advisories, Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. 1 aufgelöste Abhängigkeiten wurden mit OSV abgeglichen. 12 konnten nicht bewertet werden — keine aufgelöste Version, ein nicht unterstütztes Ökosystem oder außerhalb der ausgewiesenen Paketliste. Dieses Repository veröffentlicht kein Paket, das der Index auflöst; bewertet wurde daher der Abhängigkeitsgraph des Repositorys. Dieser Graph vermischt Entwicklungs- und Test-Pins mit ausgelieferten Abhängigkeiten, daher werden nur die deklarierten Laufzeit-Abhängigkeiten bewertet; transitive Befunde werden als Kontext ausgewiesen und fließen nicht in die Bewertung ein. Erreichbarkeit wird nicht analysiert.

AI Readiness

Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.

41Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
40/40Lesbare Commit-Historie — 85 von 100 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share0,85
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration — tox.ini
0/11Statische Typprüfung
0/10Reproduzierbare Umgebung
0/10Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100
0/8Automatisierte Wartung — keine automatisierten Abhängigkeits-Updates beobachtet
0/10OpenSSF Scorecard: Pinned-Dependencies — keine Daten
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfilenein
typed_languagenein
bootstrap_files
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
55/55Handhabbare Dateigrößen — 0/15 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes8.822
source_files_sampled15
oversized_source_files0

Eckdaten

827GitHub-Sterne
5Mitwirkende
0Commits, letzte 12 Monate
1.018Tage seit letztem Push
10Releases
1Bus-Faktor
83offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

Weitere Details

Stern- und Fork-Verlauf 0 ★ / 196 ⇿
0Sterne
196Forks
5Releases

Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.

0408012016015832017-092022-012026-06
Major 0Minor 0Patch 5

Jeder Punkt umfasst 8 Tage.

Alle Abhängigkeiten 13

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 13 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIcoverallsindirekt
PyPIenum341.1.6indirekt
PyPIimageioindirekt
PyPImockindirekt
PyPInoseindirekt
PyPInumpyindirekt
PyPIpathlibindirekt
PyPIpillowindirekt
PyPIpytestindirekt
PyPIpytest-covindirekt
PyPItoxindirekt
PyPItwineindirekt
PyPIwheelindirekt
Abhängigkeits-Advisories 0

Dieses Repository veröffentlicht kein vom Index auflösbares Paket, daher wurde sein eigener Abhängigkeitsgraph bewertet – 1 Pakete, darunter auch Entwicklungs- und Test-Pins, die nie ausgeliefert werden: 0 tragen bekannte Advisories, davon 0 direkte. 12 konnten nicht bewertet werden – keine aufgelöste Version, ein nicht unterstütztes Ökosystem, oder außerhalb der ausgewiesenen Paketliste.

Keine bekannten Advisories betreffen die bewerteten Abhängigkeiten.

Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.

JSON-Rohbericht maschinenlesbar
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              {
                "key": "linter_config",
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                "key": "pre_commit_hooks",
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              {
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                "points": 0,
                "status": "missed",
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              {
                "key": "openssf_scorecard_ci_tests",
                "name": "OpenSSF Scorecard: CI-Tests",
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                "points": 0,
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                ],
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                "key": "documentation_directory",
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                "key": "documentation_homepage_site",
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                "status": "missed",
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                "key": "repository_description",
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                "key": "wiki",
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        ],
        "description": "Are baseline engineering and documentation practices in place?"
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      {
        "key": "security",
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        "weight": 0.16,
        "metrics": [
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              "source": "file_signals",
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            "components": [
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                "key": "security_policy_security_md",
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                "status": "missed",
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              {
                "key": "dependabot_config",
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                "key": "dependency_lockfiles",
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                "key": "codeql_workflow",
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            "key": "dependency_advisories",
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                "code": "advisories_scope_repository",
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              {
                "code": "advisories_unassessed",
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                "code": "advisories_repo_graph_caveat",
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                "code": "advisories_reachability",
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            "value": 100,
            "inputs": {
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            "components": [
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                "key": "direct_dependencies_free_of_known_advisories",
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                "status": "met",
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                "key": "indirect_dependencies_free_of_known_advisories",
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                "points": 0,
                "status": "excluded",
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                "max_points": 25
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              {
                "key": "no_advisories_left_outstanding",
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                "detail": "no advisory carries a publication date",
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                "status": "excluded",
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                "max_points": 40
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          },
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            "key": "malicious_dependencies",
            "band": "excellent",
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            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
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            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
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            "notes": [
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                "code": "jurisdiction_evidence_limits",
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            "value": 100,
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                "status": "met",
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        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
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                "key": "demonstrated_agent_practice",
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                "key": "automated_maintenance",
                "name": "Automated maintenance",
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                "points": 0,
                "status": "missed",
                "details": [
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                    "code": "no_dependency_automation",
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                "max_points": 8
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              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "OpenSSF Scorecard unavailable",
                "points": 0,
                "status": "excluded",
                "details": [
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          },
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            "key": "ai_code_legibility",
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            "notes": [],
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            "components": [
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                "points": 0,
                "status": "missed",
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                    "code": "no_typecheck_config_language",
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                "max_points": 45
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                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
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                "points": 55,
                "status": "met",
                "details": [
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  },
  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "OpenSSF Scorecard timed out after 240s; skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-28T14:46:02.866307Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/n/NaturalHistoryMuseum/pyzbar.svg",
  "full_name": "NaturalHistoryMuseum/pyzbar",
  "license_state": "standard",
  "license_spdx": "MIT"
}

Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.

Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken.