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Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-25 17:27 UTC

ibis-project / ibis-ml

IbisML is a library for building scalable ML pipelines using Ibis.

PythonApache-2.0★ 120 Sterne⑂ 15 Forksseit Jan. 2023Auf GitHub ansehen ↗

ibis-project/ibis-ml erreicht einen Gesundheitsindex von 59 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Engineering Quality (82/100) ab, am schwächsten bei Vitality (33/100). Zuletzt vor 363 Tagen aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

59
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.

59
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

Ibis ProjectOrganisation
306 Follower24 öffentliche Reposseit Apr. 2017

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlicht
PyPIibis-ml0.1.4-5vor 373 Tagen

Metriken nach Kategorie

Vitalität

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

33Gefährdet · 22 % des Gesamtindex
Wie die Bewertung erfolgt
3.6/36Push-Aktualität — letzter Push vor 363 Tagen
0.7/36Commit-Rhythmus — 1/52 Wochen mit Commits
2.7/18Commit-Volumen — 1 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Verwendete Eingangsdaten
commits_last_year1
human_commit_share1
days_since_last_push363
active_weeks_last_year1
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 5 Releases veröffentlicht
16.2/36Release-Aktualität — letztes Release vor 363 Tagen
19.8/27Release-Rhythmus — ein Release etwa alle 107,1 Tage
8/10OpenSSF Scorecard: Signed-Releases — 5 out of the last 5 releases have a total of 5 signed artifacts.
Verwendete Eingangsdaten
releases_count5
latest_release_tagv0.1.4
releases_from_tagsnein
days_since_latest_release363
mean_days_between_releases107,1

Community & Verbreitung

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

49Gefährdet · 18 % des Gesamtindex
Wie die Bewertung erfolgt
33.7/60Stars — 120 Stars
9.6/25Forks — 15 Forks
5/15Watcher — 9 Watcher
Verwendete Eingangsdaten
forks15
stars120
watchers9
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (Apache-2.0)
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?

70Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
11.7/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 48 % der Commits
12.2/13.5Breite der Beitragenden — 9 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 15 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor2
contributors_sampled9
top_contributor_share0,478
Wie die Bewertung erfolgt
29.6/46.8Issue-Lösungsquote — 63 % der Issues geschlossen
35.7/38.3PR-Annahme — 98/105 entschiedene PRs gemergt
10.5/15OpenSSF Scorecard: Code-Review — Found 16/22 approved changesets -- score normalized to 7
Verwendete Eingangsdaten
merged_prs98
open_issues29
closed_issues50
issue_closed_ratio0,633
closed_unmerged_prs7
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
17.9/25Reichweite des Inhabers — 306 Follower von ibis-project
22.2/25Kontohistorie — 24 öffentliche Repos, Kontoalter ca. 9 Jahre
Verwendete Eingangsdaten
followers306
owner_typeOrganization
is_verified
owner_loginibis-project
public_repos24
account_age_days3.389
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
14/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 373 Tagen
20/20Versionshistorie — 5 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesibis-ml
ecosystemspypi
any_deprecatednein
min_days_since_publish373

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

82Gut · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 3 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration
9.6/9.6Pre-Commit-Hooks
0/6.4.editorconfig
2/20OpenSSF Scorecard: CI-Tests — 2 out of 18 merged PRs checked by a CI test -- score normalized to 1
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_configja

Dokumentation

90Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://ibis-project.github.io/ibis-ml/
10/10Repository-Beschreibung
10/10Topics — 4 Topics
0/10Wiki
Verwendete Eingangsdaten
topicsfeature-engineering, machine-learning, sql, ibis
has_wikinein
homepagehttps://ibis-project.github.io/ibis-ml/
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

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

58Mittel · 16 % des Gesamtindex

Sicherheitslage

48Gefährdet
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
0.2/2.5CI-Tests — 2 out of 18 merged PRs checked by a CI test -- score normalized to 1
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
5.2/7.5Code-Review — Found 16/22 approved changesets -- score normalized to 7
2.5/2.5Contributors — project has 15 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Lizenz — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
5/5Packaging — packaging workflow detected
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
0/5Security-Policy — security policy file not detected
6/7.5Signed-Releases — 5 out of the last 5 releases have a total of 5 signed artifacts.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate4,8
Wie die Bewertung erfolgt
35/35Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory
25/25Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory
0/40Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum
Verwendete Eingangsdaten
sourceosv
advisories0
affected_packages0
assessed_packages9
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:ibis-ml@0.1.4 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 9 Paketen. 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.

39Gefährdet · 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 — 100 von 100 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share1
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration
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 — dependency not pinned by hash detected -- score normalized to 0
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 — 0/34 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes29.891
source_files_sampled34
oversized_source_files0
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

120GitHub-Sterne
9Mitwirkende
1Commits, letzte 12 Monate
363Tage seit letztem Push
5Releases
2Bus-Faktor
29offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

Weitere Details

Stern- und Fork-Verlauf 0 ★ / 15 ⇿
0Sterne
15Forks
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.

03581013151512023-082024-102025-12
Major 0Minor 1Patch 4

Jeder Punkt umfasst 3 Tage.

OpenSSF Scorecard 4.8 / 10
4.8Gesamtwert

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 · 2026-07-25 17:27 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-Protectionbranch protection is not maximal on development and all release branches
1CI-Tests2 out of 18 merged PRs checked by a CI test -- score normalized to 1
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7Code-ReviewFound 16/22 approved changesets -- score normalized to 7
10Contributorsproject has 15 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
8Signed-Releases5 out of the last 5 releases have a total of 5 signed artifacts.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direkte Abhängigkeiten 1
RegistryPaketVersionsvorgabeManifest
PyPIibis-frameworkpyproject.toml
Alle Abhängigkeiten 1

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 1 direkte und 0 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIibis-frameworkdirekt
Abhängigkeits-Advisories 0

Die Installation von pypi:ibis-ml@0.1.4 zieht 9 Pakete nach sich, direkt und transitiv: 0 tragen bekannte Advisories, davon 0 direkte Abhängigkeiten.

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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                "name": "Verified domain",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "owner_reach",
                "name": "Owner reach",
                "detail": "306 followers of ibis-project",
                "points": 17.9,
                "status": "partial",
                "details": [
                  {
                    "code": "owner_followers",
                    "params": {
                      "count": 306,
                      "login": "ibis-project"
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                  }
                ],
                "max_points": 25
              },
              {
                "key": "track_record",
                "name": "Track record",
                "detail": "24 public repos, account ~9 yr old",
                "points": 22.2,
                "status": "partial",
                "details": [
                  {
                    "code": "public_repos",
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                  {
                    "code": "account_age_years",
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                      "years": 9
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                "max_points": 25
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            ]
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          {
            "key": "package_maintenance",
            "band": "good",
            "name": "Package maintenance",
            "note": null,
            "notes": [],
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            "inputs": {
              "packages": [
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              "ecosystems": "pypi",
              "any_deprecated": false,
              "min_days_since_publish": 373
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            "components": [
              {
                "key": "published_resolvable",
                "name": "Published & resolvable",
                "detail": "1 package(s) on pypi",
                "points": 25,
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                "details": [
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                  }
                ],
                "max_points": 25
              },
              {
                "key": "publish_recency",
                "name": "Publish recency",
                "detail": "latest publish 373 days ago",
                "points": 14,
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                "details": [
                  {
                    "code": "publish_recency",
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                "max_points": 35
              },
              {
                "key": "version_history",
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                "detail": "5 published versions",
                "points": 20,
                "status": "met",
                "details": [
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                      "count": 5
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                ],
                "max_points": 20
              },
              {
                "key": "not_deprecated",
                "name": "Not deprecated",
                "detail": "active, not deprecated or yanked",
                "points": 20,
                "status": "met",
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            ]
          }
        ],
        "description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
      },
      {
        "key": "engineering",
        "band": "good",
        "name": "Engineering Quality",
        "value": 82,
        "weight": 0.2,
        "metrics": [
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            "key": "engineering_practices",
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            "notes": [],
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                "points": 24,
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              {
                "key": "linter_config",
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                "points": 16,
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                "points": 9.6,
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              },
              {
                "key": "openssf_scorecard_ci_tests",
                "name": "OpenSSF Scorecard: CI-Tests",
                "detail": "2 out of 18 merged PRs checked by a CI test -- score normalized to 1",
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            ]
          },
          {
            "key": "documentation",
            "band": "excellent",
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            "note": null,
            "notes": [],
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              "has_readme": true,
              "has_docs_dir": true,
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            "components": [
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                "key": "documentation_directory",
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                "detail": null,
                "points": 25,
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              {
                "key": "documentation_homepage_site",
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                "points": 15,
                "status": "met",
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                "max_points": 15
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                "detail": null,
                "points": 10,
                "status": "met",
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              },
              {
                "key": "topics",
                "name": "Topics",
                "detail": "4 topics",
                "points": 10,
                "status": "met",
                "details": [
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                "key": "wiki",
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                "detail": null,
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            ]
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        ],
        "description": "Are baseline engineering and documentation practices in place?"
      },
      {
        "key": "security",
        "band": "moderate",
        "name": "Security",
        "value": 58,
        "weight": 0.16,
        "metrics": [
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            "note": null,
            "notes": [],
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                "points": 10,
                "status": "met",
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              },
              {
                "key": "dependency_update_tool",
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                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "fuzzing",
                "name": "Fuzzing",
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                "points": 0,
                "status": "missed",
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                "status": "met",
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                "key": "maintained",
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                "points": 0,
                "status": "missed",
                "details": [],
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                "max_points": 7.5
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                "max_points": 7.5
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                "key": "vulnerabilities",
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            "note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:ibis-ml@0.1.4 runtime dependency closure — what installing the published package pulls in — 9 packages. Reachability is not analyzed.",
            "notes": [
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            "notes": [
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            "value": 100,
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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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            "components": [
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              },
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                "points": 0,
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                "status": "missed",
                "details": [
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                "max_points": 8
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                "key": "openssf_scorecard_pinned_dependencies",
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          },
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            "key": "ai_code_legibility",
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            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 55,
            "inputs": {
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              "largest_source_bytes": 29891,
              "source_files_sampled": 34,
              "oversized_source_files": 0
            },
            "components": [
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                "points": 0,
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                ],
                "max_points": 45
              },
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                "key": "manageable_file_sizes",
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                "points": 55,
                "status": "met",
                "details": [
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          {
            "key": "ai_interfaces",
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            "notes": [],
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            "inputs": {
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            "components": [
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                "key": "api_schema_openapi_graphql_proto",
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                "points": 0,
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                "details": [],
                "max_points": 40
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              {
                "key": "mcp_server",
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                "points": 0,
                "status": "missed",
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              },
              {
                "key": "runnable_examples",
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                "details": [
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        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
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  "report_type": "repository",
  "generated_at": "2026-07-25T17:27:23.449142Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/i/ibis-project/ibis-ml.svg",
  "full_name": "ibis-project/ibis-ml",
  "license_state": "standard",
  "license_spdx": "Apache-2.0"
}

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 StatistikenPyPI.