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Software-GesundheitsberichtSchema 0.11.0 · Metriken 1.13.0 · 2026-07-14 16:16 UTC

roboflow / inference

Turn any computer or edge device into a command center for your computer vision projects.

PythonEigene Lizenz★ 2.374 Sterne⑂ 286 Forksseit Juli 2023Auf GitHub ansehen ↗
#agents#classification#computer-vision#deployment#docker#inference#inference-api#inference-server#instance-segmentation#jetson#machine-learning#object-detection#onnx#python#tensorrt#vit#yolo11#yolov12#yolov5#yolov8

roboflow/inference erreicht einen Gesundheitsindex von 71 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (100/100) ab, am schwächsten bei Security (1/100). Zuletzt heute aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

71
gesamt / 100
Gut

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.

71
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.

Eigentümerschaft

RoboflowOrganisation
5.137 Follower168 öffentliche Reposseit Juli 2019

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlicht
PyPIinference-models0.31.087.648101vor 3 Tagen

Metriken nach Kategorie

Vitalität

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

100Exzellent · 22 % des Gesamtindex

Entwicklungsaktivität

100Exzellent
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 0 Tagen
36/36Commit-Rhythmus — 52/52 Wochen mit Commits
18/18Commit-Volumen — 3.484 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — keine Daten
Verwendete Eingangsdaten
commits_last_year3.484
human_commit_share—
days_since_last_push0
active_weeks_last_year52

Release-Disziplin

100Exzellent
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 100 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 3 Tagen
27/27Release-Rhythmus — ein Release etwa alle 5,5 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count100
latest_release_tagv1.3.5
releases_from_tagsnein
days_since_latest_release3
mean_days_between_releases5,5

Community & Verbreitung

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

79Gut · 18 % des Gesamtindex

Popularität & Verbreitung

83Gut
Wie die Bewertung erfolgt
54.8/60Stars — 2.374 Stars
20.5/25Forks — 286 Forks
8/15Watcher — 29 Watcher
Verwendete Eingangsdaten
forks286
stars2.374
watchers29
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community-Gesundheit

71Gut
Wie die Bewertung erfolgt
22.5/22.5README
16.9/22.5Lizenz — Lizenzdatei vorhanden, keine anerkannte Lizenz
18/18CONTRIBUTING-Leitfaden
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
6.3/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductnein
has_pull_request_templateja

Ökosystem-Verbreitung (Downloads)

82Gut
Wie die Bewertung erfolgt
65.9/80Downloads pro Monat — 87.648 Downloads/Monat über pypi
0/20Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen
Verwendete Eingangsdaten
packagesinference-models
dependents—
ecosystemspypi
total_downloads—
monthly_downloads87.648
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Abhängige in der Registry. Die verbleibenden Gewichte wurden renormalisiert.

Nachhaltigkeit & Governance

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

75Gut · 24 % des Gesamtindex

Maintainer-Resilienz (Bus-Faktor)

59Mittel
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
14.6/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 35 % der Commits
13.5/13.5Breite der Beitragenden — 100 Beitragende
0/10OpenSSF Scorecard: Contributors — keine Daten
Verwendete Eingangsdaten
bus_factor2
contributors_sampled100
top_contributor_share0,349

Reaktionsfähigkeit bei Issues & PRs

71Gut
Wie die Bewertung erfolgt
28.1/46.8Issue-Lösungsquote — 60 % der Issues geschlossen
32.2/38.3PR-Annahme — 1.999/2.371 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — keine Daten
Verwendete Eingangsdaten
merged_prs1.999
open_issues78
closed_issues117
issue_closed_ratio0,6
closed_unmerged_prs372

Eigentümerschaft & Trägerschaft

80Gut
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
25/25Reichweite des Inhabers — 5.137 Follower von roboflow
25/25Kontohistorie — 168 öffentliche Repos, Kontoalter ca. 6 Jahre
Verwendete Eingangsdaten
followers5.137
owner_typeOrganization
is_verified—
owner_loginroboflow
public_repos168
account_age_days2.551

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 3 Tagen
20/20Versionshistorie — 101 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesinference-models
ecosystemspypi
any_deprecatednein
min_days_since_publish3

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

84Gut · 20 % des Gesamtindex

Engineering-Praktiken

80Gut
Wie die Bewertung erfolgt
24/24CI-Workflows — 63 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration — .eslintrc.json
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

Dokumentation

90Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://inference.roboflow.com
10/10Repository-Beschreibung
10/10Topics — 20 Topics
0/10Wiki
Verwendete Eingangsdaten
topicsagents, classification, computer-vision, deployment, docker, inference, inference-api, inference-server, instance-segmentation, jetson, machine-learning, object-detection, onnx, python, tensorrt, vit, yolo11, yolov12, yolov5, yolov8
has_wikinein
homepagehttps://inference.roboflow.com
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

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

1Kritisch · 16 % des Gesamtindex

Sicherheitslage

1Kritisch
Wie die Bewertung erfolgt
0/30Sicherheitsrichtlinie (SECURITY.md)
0/25Dependabot-Konfiguration
0/25Lockfiles für Abhängigkeiten — veröffentlichte Bibliothek — Lockfiles sind Sache der Anwendung, hier nicht erwartet
0/20CodeQL-Workflow
Verwendete Eingangsdaten
sourcefile_signals
lockfilespackage-lock.json, uv.lock
manifestsinference_models/pyproject.toml, pyproject.toml, requirements/requirements.cli.txt, requirements/requirements.clip.txt, requirements/requirements.cloud_deploy.txt, requirements/requirements.cloud_storage.txt, requirements/requirements.code_analysis.txt, requirements/requirements.cpu.txt, requirements/requirements.docs.txt, requirements/requirements.doctr.txt, requirements/requirements.easyocr.txt, requirements/requirements.gpu.txt, requirements/requirements.groundingdino.txt, requirements/requirements.hosted.txt, requirements/requirements.http.txt, requirements/requirements.jetson.txt, requirements/requirements.modal.txt, requirements/requirements.pali.flash_attn.txt, requirements/requirements.parallel.txt, requirements/requirements.sam.txt, requirements/requirements.sam3.txt, requirements/requirements.sam3_3d.txt, requirements/requirements.sdk.http.txt, requirements/requirements.sdk.webrtc.txt, requirements/requirements.test.colab.txt, requirements/requirements.test.integration.txt, requirements/requirements.test.unit.txt, requirements/requirements.transformers.txt, requirements/requirements.vino.txt, requirements/requirements.yolo_world.txt, setup.py, theme_build/package.json
has_codeql_workflownein
has_security_policynein
has_dependabot_confignein
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lockfiles für Abhängigkeiten. Die verbleibenden Gewichte wurden renormalisiert.

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.

81Gut · 0 % des Gesamtindex

AI-Agentenkontext & Leitlinien

75Gut
Wie die Bewertung erfolgt
45/45Agentenanweisungen — .cursor/rules/cli-options.mdc, .cursor/rules/empty-lines.mdc, .cursor/rules/execution-engine-version-changelog.mdc, .cursor/rules/function-call.mdc, .cursor/rules/google-docstrings.mdc, .cursor/rules/pathlib.mdc, .cursor/rules/pr-description.mdc, .cursor/rules/pydantic-field-descriptions.mdc, .cursor/rules/return-values.mdc, .cursor/rules/uv-package-management.mdc, AGENTS.md
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share—
agent_instruction_files.cursor/rules/cli-options.mdc, .cursor/rules/empty-lines.mdc, .cursor/rules/execution-engine-version-changelog.mdc, .cursor/rules/function-call.mdc, .cursor/rules/google-docstrings.mdc, .cursor/rules/pathlib.mdc, .cursor/rules/pr-description.mdc, .cursor/rules/pydantic-field-descriptions.mdc, .cursor/rules/return-values.mdc, .cursor/rules/uv-package-management.mdc, AGENTS.md
agent_instruction_max_bytes4.044
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.

AI-Verify-Loop (Build / Test / Typprüfung)

100Exzellent
Wie die Bewertung erfolgt
18/18Bootstrap mit einem Befehl — Makefile
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration — .eslintrc.json
11/11Statische Typprüfung — inference/landing/tsconfig.json
10/10Reproduzierbare Umgebung — Dockerfile, lockfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — keine Daten
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfilespackage-lock.json, uv.lock
has_dockerfileja
typed_languagenein
bootstrap_filesMakefile
has_devcontainernein
has_linter_configja
typecheck_configsinference/landing/tsconfig.json
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share—
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis, Automatisierte Wartung. Die verbleibenden Gewichte wurden renormalisiert.

AI-Code-Lesbarkeit für Modelle

81Gut
Wie die Bewertung erfolgt
27/45Typprüfbarer Code — Python mit Typprüfungs-Konfiguration (inference/landing/tsconfig.json)
54.4/55Handhabbare Dateigrößen — 28/2.431 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes354.082
source_files_sampled2.431
oversized_source_files28

Maschinenlesbare AI-Schnittstellen

40Gefährdet
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
0/20MCP-Server
40/40Lauffähige Beispiele — examples, notebooks
Verwendete Eingangsdaten
example_dirsexamples, notebooks
has_mcp_signalnein
api_schema_files—

Eckdaten

2.374GitHub-Sterne
100Mitwirkende
3.484Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
2Bus-Faktor
78offene Issues
npm, PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • Could not fetch pypi package 'inference-development' from its registry
  • Could not fetch npm package 'inference-docs-theme' from its registry
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

Weitere Details

Direkte Abhängigkeiten 45
RegistryPaketVersionsvorgabeManifest
PyPInumpy—inference_models/pyproject.toml
PyPItorch>=2.0.0,<3.0.0inference_models/pyproject.toml
PyPItorchvision—inference_models/pyproject.toml
PyPIopencv-python>=4.8.1.78inference_models/pyproject.toml
PyPIrequests>=2.33.0,<3.0.0inference_models/pyproject.toml
PyPIsupervision>=0.29.0inference_models/pyproject.toml
PyPIbackoff~=2.2.0inference_models/pyproject.toml
PyPItransformers>=5.5.0,<=5.9.0inference_models/pyproject.toml
PyPItimm>=1.0.0,<2.0.0inference_models/pyproject.toml
PyPIaccelerate>=1.0.0,<2.0.0inference_models/pyproject.toml
PyPIeinops>=0.7.0,<1.0.0inference_models/pyproject.toml
PyPIpeft>=0.18.1inference_models/pyproject.toml
PyPInum2words~=0.5.14inference_models/pyproject.toml
PyPIbitsandbytes>=0.46.1,<0.48.0inference_models/pyproject.toml
PyPIpyvips>=2.2.3,<3.0.0inference_models/pyproject.toml
PyPIrf-clip==1.1inference_models/pyproject.toml
PyPIpython-doctr>=1.0.0inference_models/pyproject.toml
PyPIpackaging>=24.0.0inference_models/pyproject.toml
PyPIrich>=14.1.0,<15.0.0inference_models/pyproject.toml
PyPIpydantic>=2.11.7,<3.0.0inference_models/pyproject.toml
PyPIfilelock>=3.20.3,<4.0.0inference_models/pyproject.toml
PyPIsegmentation-models-pytorch>=0.5.0,<1.0.0inference_models/pyproject.toml
PyPIscikit-image>=0.24.0,<0.26.0inference_models/pyproject.toml
PyPIeasyocr~=1.7.2inference_models/pyproject.toml
PyPIsentencepiece>=0.2.1,<0.3.0inference_models/pyproject.toml
PyPIrf_groundingdino==0.3.0inference_models/pyproject.toml
PyPItldextract~=5.1.2inference_models/pyproject.toml
PyPIpybase64~=1.0.0inference_models/pyproject.toml
PyPIrf-segment-anything==1.0inference_models/pyproject.toml
PyPIrf-sam-2==1.0.3inference_models/pyproject.toml
PyPIpycocotools>=2.0.10,<2.1.0inference_models/pyproject.toml
PyPIPyYAML>=6.0.0,<7.0.0inference_models/pyproject.toml
PyPIpyclipper>=1.3.0,<2.0.0inference_models/pyproject.toml
PyPIshapely>=2.0.0,<3.0.0inference_models/pyproject.toml
PyPIurllib3>=2.7.0,<3.0.0inference_models/pyproject.toml
PyPIpillow>=12.2.0,<13.0.0inference_models/pyproject.toml
PyPIGitPython>=3.1.50,<4.0.0inference_models/pyproject.toml
PyPICairoSVG>=2.9.0,<3.0.0inference_models/pyproject.toml
PyPIfonttools>=4.60.2,<5.0.0inference_models/pyproject.toml
PyPIPygments>=2.20.0,<3.0.0inference_models/pyproject.toml
PyPIidna>=3.15inference_models/pyproject.toml
npm@rive-app/canvas^2.25.7theme_build/package.json
npm@tailwindcss/line-clamp^0.4.4theme_build/package.json
npmgsap^3.12.7theme_build/package.json
npmswiper^12.1.2theme_build/package.json
Alle Abhängigkeiten 1.040

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 87 direkte und 953 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
npm@rive-app/canvas2.25.7direkt
npm@tailwindcss/line-clamp0.4.4direkt
npmgsap3.12.7direkt
npmswiper12.1.2direkt
PyPIaccelerate—direkt
PyPIaccelerate1.7.0direkt
PyPIbackoff—direkt
PyPIbackoff2.2.1direkt
PyPIbitsandbytes—direkt
PyPIbitsandbytes0.47.0direkt
PyPIcairosvg2.9.0direkt
PyPIeasyocr1.7.2direkt
PyPIeinops—direkt
PyPIeinops0.8.1direkt
PyPIfilelock—direkt
PyPIfilelock3.29.0direkt
PyPIfonttools4.62.1direkt
PyPIgitpython3.1.50direkt
PyPIidna3.18direkt
PyPInum2words—direkt
PyPInum2words0.5.14direkt
PyPInumpy—direkt
PyPInumpy1.26.4direkt
PyPIopencv-python—direkt
PyPIopencv-python4.11.0.86direkt
PyPIpackaging—direkt
PyPIpackaging25.0direkt
PyPIpeft—direkt
PyPIpeft0.18.1direkt
PyPIpillow—direkt
PyPIpillow12.2.0direkt
PyPIpybase64—direkt
PyPIpybase641.0.2direkt
PyPIpyclipper1.3.0.post6direkt
PyPIpycocotools—direkt
PyPIpycocotools2.0.10direkt
PyPIpydantic—direkt
PyPIpydantic2.11.7direkt
PyPIpygments2.20.0direkt
PyPIpython-doctr—direkt
PyPIpython-doctr1.0.1direkt
PyPIpyvips—direkt
PyPIpyvips2.2.3direkt
PyPIpyyaml—direkt
PyPIpyyaml6.0.2direkt
PyPIrequests—direkt
PyPIrequests2.33.1direkt
PyPIrequests2.33.0direkt
PyPIrf-clip1.1direkt
PyPIrf-groundingdino—direkt
PyPIrf-groundingdino0.3.0direkt
PyPIrf-sam-21.0.3direkt
PyPIrf-segment-anything1.0direkt
PyPIrich—direkt
PyPIrich14.1.0direkt
PyPIscikit-image—direkt
PyPIscikit-image0.25.2direkt
PyPIsegmentation-models-pytorch0.5.0direkt
PyPIsentencepiece0.2.1direkt
PyPIshapely—direkt
PyPIshapely2.1.1direkt
PyPIsupervision—direkt
PyPIsupervision0.29.1direkt
PyPIsupervision0.13.0direkt
PyPItimm—direkt
PyPItimm1.0.15direkt
PyPItimm1.0.27direkt
PyPItldextract—direkt
PyPItldextract5.1.3direkt
PyPItorch—direkt
PyPItorch2.7.1direkt
PyPItorch2.7.1+cpudirekt
PyPItorch2.7.1+cu128direkt
PyPItorch2.8.0direkt
PyPItorch2.7.1+cu118direkt
PyPItorch2.6.0+cu124direkt
PyPItorchvision—direkt
PyPItorchvision0.22.1+cu118direkt
PyPItorchvision0.22.1+cu128direkt
PyPItorchvision0.21.0+cu124direkt
PyPItorchvision0.23.0direkt
PyPItorchvision0.22.1+cpudirekt
PyPItorchvision0.22.1direkt
PyPItransformers—direkt
PyPItransformers5.5.4direkt
PyPIurllib3—direkt
PyPIurllib32.7.0direkt
npm@alloc/quick-lru5.2.0indirekt
npm@babel/code-frame7.29.7indirekt
npm@babel/compat-data7.29.7indirekt
npm@babel/core7.29.7indirekt
npm@babel/generator7.29.7indirekt
npm@babel/helper-compilation-targets7.29.7indirekt
npm@babel/helper-globals7.29.7indirekt
npm@babel/helper-module-imports7.29.7indirekt
npm@babel/helper-module-transforms7.29.7indirekt
npm@babel/helper-string-parser7.29.7indirekt
npm@babel/helper-validator-identifier7.29.7indirekt
npm@babel/helper-validator-option7.29.7indirekt
npm@babel/helpers7.29.7indirekt
npm@babel/parser7.29.7indirekt
npm@babel/runtime7.26.10indirekt
npm@babel/runtime7.29.7indirekt
npm@babel/template7.29.7indirekt
npm@babel/traverse7.29.7indirekt
npm@babel/types7.29.7indirekt
npm@emnapi/core1.10.0indirekt
npm@emnapi/runtime1.10.0indirekt
npm@emnapi/runtime1.11.1indirekt
npm@emnapi/wasi-threads1.2.1indirekt
npm@esbuild/aix-ppc640.25.0indirekt
npm@esbuild/android-arm0.25.0indirekt
npm@esbuild/android-arm640.25.0indirekt
npm@esbuild/android-x640.25.0indirekt
npm@esbuild/darwin-arm640.25.0indirekt
npm@esbuild/darwin-x640.25.0indirekt
npm@esbuild/freebsd-arm640.25.0indirekt
npm@esbuild/freebsd-x640.25.0indirekt
npm@esbuild/linux-arm0.25.0indirekt
npm@esbuild/linux-arm640.25.0indirekt
npm@esbuild/linux-ia320.25.0indirekt
npm@esbuild/linux-loong640.25.0indirekt
npm@esbuild/linux-mips64el0.25.0indirekt
npm@esbuild/linux-ppc640.25.0indirekt
npm@esbuild/linux-riscv640.25.0indirekt
npm@esbuild/linux-s390x0.25.0indirekt
npm@esbuild/linux-x640.25.0indirekt
npm@esbuild/netbsd-arm640.25.0indirekt
npm@esbuild/netbsd-x640.25.0indirekt
npm@esbuild/openbsd-arm640.25.0indirekt
npm@esbuild/openbsd-x640.25.0indirekt
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JSON-Rohbericht maschinenlesbar
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    "Could not fetch npm package 'inference-docs-theme' from its registry",
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  "report_type": "repository",
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}

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Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.11.0 — vollständige Methodik · Metriken-Wiki.

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