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Software-GesundheitsberichtSchema 0.12.0 · Metriken 1.13.0 · 2026-07-17 18:05 UTC

databricks-industry-solutions / many-model-forecasting

Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.

Python · Jupyter NotebookEigene Lizenz★ 99 Sterne⑂ 42 Forksseit Okt. 2022Auf GitHub ansehen ↗

databricks-industry-solutions/many-model-forecasting erreicht einen Gesundheitsindex von 62 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Vitality (90/100) ab, am schwächsten bei Security (45/100). Zuletzt vor 5 Tagen aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.

62
gesamt / 100
Mittel

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.

62
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

797 Follower216 öffentliche Reposseit Mai 2022

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?

90Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 5 Tagen
18.7/36Commit-Rhythmus — 27/52 Wochen mit Commits
18/18Commit-Volumen — 302 Commits im letzten Jahr
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Verwendete Eingangsdaten
commits_last_year302
human_commit_share
days_since_last_push5
active_weeks_last_year27

Release-Disziplin

100Exzellent
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 9 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 6 Tagen
27/27Release-Rhythmus — ein Release etwa alle 18,6 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count9
latest_release_tagv0.1.7
releases_from_tagsnein
days_since_latest_release6
mean_days_between_releases18,6
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.

Community & Verbreitung

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

57Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
32.3/60Stars — 99 Stars
13.4/25Forks — 42 Forks
3.9/15Watcher — 6 Watcher
Verwendete Eingangsdaten
forks42
stars99
watchers6
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
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
0/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
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?

58Mittel · 24 % des Gesamtindex
Wie die Bewertung erfolgt
9/54Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab
5.8/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 74 % der Commits
10.8/13.5Breite der Beitragenden — 8 Beitragende
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Verwendete Eingangsdaten
bus_factor1
contributors_sampled8
top_contributor_share0,744
Wie die Bewertung erfolgt
37.4/46.8Issue-Lösungsquote — 80 % der Issues geschlossen
35.7/38.3PR-Annahme — 171/183 entschiedene PRs gemergt
1.5/15OpenSSF Scorecard: Code-Review — Found 1/7 approved changesets -- score normalized to 1
Verwendete Eingangsdaten
merged_prs171
open_issues2
closed_issues8
issue_closed_ratio0,8
closed_unmerged_prs12
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
20.9/25Reichweite des Inhabers — 797 Follower von databricks-industry-solutions
21.3/25Kontohistorie — 216 öffentliche Repos, Kontoalter ca. 4 Jahre
Verwendete Eingangsdaten
followers797
owner_typeOrganization
is_verified
owner_logindatabricks-industry-solutions
public_repos216
account_age_days1.509

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

53Mittel · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 5 Workflow(s)
24/24Tests vorhanden
0/16Linter-Konfiguration
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 7 merged PRs checked by a CI test -- score normalized to 0
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_confignein
has_precommit_confignein
Wie die Bewertung erfolgt
30/30README
0/25Dokumentationsverzeichnis
0/15Dokumentations-/Homepage-Site
10/10Repository-Beschreibung
10/10Topics — 2 Topics
10/10Wiki
Verwendete Eingangsdaten
topicsforecasting, xindustry
has_wikija
homepage
has_readmeja
has_docs_dirnein
has_descriptionja

Sicherheit

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

45Gefährdet · 16 % des Gesamtindex

Sicherheitslage

45Gefährdet
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — 0 out of 7 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 1/7 approved changesets -- score normalized to 1
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5Lizenz — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — keine Daten
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — keine Daten
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 10 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4,5
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): packaging, signed_releases. 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.

52Mittel · 0 % des Gesamtindex
Wie die Bewertung erfolgt
45/45Agentenanweisungen — CLAUDE.md
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes1.714
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
0/11Lint-/Format-Konfiguration
0/11Statische Typprüfung
10/10Reproduzierbare Umgebung — devcontainer, Dockerfile, lockfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfilesuv.lock
has_dockerfileja
typed_languagenein
bootstrap_files
has_devcontainerja
has_linter_confignein
typecheck_configs
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.
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
54.4/55Handhabbare Dateigrößen — 1/99 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes63.634
source_files_sampled99
oversized_source_files1
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

99GitHub-Sterne
8Mitwirkende
302Commits, letzte 12 Monate
5Tage seit letztem Push
9Releases
1Bus-Faktor
2offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • Could not fetch pypi package 'mmf_sa' from its registry

Weitere Details

OpenSSF Scorecard 4.5 / 10
4.5Gesamtwert

Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).Scorecard v5.5.0 · 2026-07-17 18:05 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
0CI-Tests0 out of 7 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/7 approved changesets -- score normalized to 1
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
k. A.Packagingpackaging 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
10Security-Policysecurity policy file detected
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities10 existing vulnerabilities detected
Direkte Abhängigkeiten 3
RegistryPaketVersionsvorgabeManifest
PyPIomegaconf==2.3.0pyproject.toml
PyPIsktime==0.40.1pyproject.toml
PyPImlflow>=3.1.4pyproject.toml
Alle Abhängigkeiten 183

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 4 direkte und 179 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPImlflowdirekt
PyPImlflow3.13.0direkt
PyPIomegaconf2.3.0direkt
PyPIsktime0.40.1direkt
PyPIaiohappyeyeballs2.6.2indirekt
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PyPIaiosignal1.4.0indirekt
PyPIalembic1.18.3indirekt
PyPIannotated-doc0.0.4indirekt
PyPIannotated-types0.7.0indirekt
PyPIanyio4.12.1indirekt
PyPIasync-timeout5.0.1indirekt
PyPIattrs26.1.0indirekt
PyPIazure-core1.38.0indirekt
PyPIazure-storage-blob12.28.0indirekt
PyPIazure-storage-file-datalake12.23.0indirekt
PyPIbackports-asyncio-runner1.2.0indirekt
PyPIblinker1.9.0indirekt
PyPIboto31.42.41indirekt
PyPIbotocore1.42.41indirekt
PyPIcachetools6.2.6indirekt
PyPIcertifi2026.1.4indirekt
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PyPIcharset-normalizer3.4.4indirekt
PyPIchronos-forecasting2.2.2indirekt
PyPIclick8.3.1indirekt
PyPIcloudpickle3.1.2indirekt
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PyPIcycler0.12.1indirekt
PyPIdash-bootstrap-componentsindirekt
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PyPIflask3.1.3indirekt
PyPIflask-cors6.0.2indirekt
PyPIfonttools4.61.1indirekt
PyPIfrozenlist1.8.0indirekt
PyPIgepa0.1.0indirekt
PyPIgitdb4.0.12indirekt
PyPIgitpython3.1.50indirekt
PyPIgoogle-api-core2.29.0indirekt
PyPIgoogle-auth2.48.0indirekt
PyPIgoogle-cloud-core2.5.0indirekt
PyPIgoogle-cloud-storage3.9.0indirekt
PyPIgoogle-crc32c1.8.0indirekt
PyPIgoogle-resumable-media2.8.0indirekt
PyPIgoogleapis-common-protos1.72.0indirekt
PyPIgraphene3.4.3indirekt
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PyPIjinja23.1.6indirekt
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JSON-Rohbericht maschinenlesbar
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  "license_state": "custom",
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}

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 v0.12.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken.