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Software-GesundheitsberichtSchema 0.16.0 · Metriken 1.13.0 · 2026-07-21 01:24 UTC

deepspeedai / DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Python · C++Apache-2.0★ 42.747 Sterne⑂ 4.895 Forksseit Jan. 2020Auf GitHub ansehen ↗

deepspeedai/DeepSpeed erreicht einen Gesundheitsindex von 85 von 100 und liegt damit im Bereich Exzellent. Am stärksten schneidet es bei Vitality (100/100) ab, am schwächsten bei Security (52/100). Zuletzt heute aktualisiert. 6 Mitwirkende tragen den Großteil der jüngsten Arbeit.

85
gesamt / 100
Exzellent

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.

85
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

deepspeedaiOrganisation
399 Follower6 öffentliche Reposseit Nov. 2020

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlicht
PyPIdeepspeed0.19.21.253.553127vor 34 Tagen

Metriken nach Kategorie

Vitalität

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

100Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 0 Tagen
36/36Commit-Rhythmus — 52/52 Wochen mit Commits
18/18Commit-Volumen — 390 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — keine Daten
Verwendete Eingangsdaten
commits_last_year390
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 34 Tagen
27/27Release-Rhythmus — ein Release etwa alle 21 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count100
latest_release_tagv0.19.2
releases_from_tagsnein
days_since_latest_release34
mean_days_between_releases21

Community & Verbreitung

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

94Exzellent · 18 % des Gesamtindex
Wie die Bewertung erfolgt
60/60Stars — 42.747 Stars
25/25Forks — 4.895 Forks
14.2/15Watcher — 360 Watcher
Verwendete Eingangsdaten
forks4.895
stars42.747
watchers360
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)
18/18CONTRIBUTING-Leitfaden
13.5/13.5Verhaltenskodex
0/7.2Issue-Vorlage
0/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductja
has_pull_request_templatenein
Wie die Bewertung erfolgt
80/80Downloads pro Monat — 1.253.553 Downloads/Monat über pypi
0/20Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen
Verwendete Eingangsdaten
packagesdeepspeed
dependents
ecosystemspypi
total_downloads
monthly_downloads1.253.553
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?

82Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
48.6/54Bus-Faktor — 6 Beitragende decken die Hälfte aller Commits ab
18.7/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 17 % der Commits
13.5/13.5Breite der Beitragenden — 100 Beitragende
0/10OpenSSF Scorecard: Contributors — keine Daten
Verwendete Eingangsdaten
bus_factor6
contributors_sampled100
top_contributor_share0,169
Wie die Bewertung erfolgt
30.5/46.8Issue-Lösungsquote — 65 % der Issues geschlossen
31.9/38.3PR-Annahme — 3.101/3.716 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — keine Daten
Verwendete Eingangsdaten
merged_prs3.101
open_issues1.164
closed_issues2.188
issue_closed_ratio0,653
closed_unmerged_prs615
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
18.7/25Reichweite des Inhabers — 399 Follower von deepspeedai
17.6/25Kontohistorie — 6 öffentliche Repos, Kontoalter ca. 5 Jahre
Verwendete Eingangsdaten
followers399
owner_typeOrganization
is_verified
owner_logindeepspeedai
public_repos6
account_age_days2.082

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 34 Tagen
20/20Versionshistorie — 127 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesdeepspeed
ecosystemspypi
any_deprecatednein
min_days_since_publish34

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

91Exzellent · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 30 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration — .flake8, .pylintrc
9.6/9.6Pre-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_configja

Dokumentation

90Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://www.deepspeed.ai/
10/10Repository-Beschreibung
10/10Topics — 13 Topics
0/10Wiki
Verwendete Eingangsdaten
topicsdeep-learning, pytorch, gpu, machine-learning, billion-parameters, data-parallelism, model-parallelism, inference, pipeline-parallelism, compression, mixture-of-experts, trillion-parameters, zero
has_wikinein
homepagehttps://www.deepspeed.ai/
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

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

52Mittel · 16 % des Gesamtindex

Sicherheitslage

40Gefährdet
Wie die Bewertung erfolgt
30/30Sicherheitsrichtlinie (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
lockfiles
manifestsdocs/Gemfile, requirements/requirements-1bit-mpi.txt, requirements/requirements-autotuning-ml.txt, requirements/requirements-autotuning.txt, requirements/requirements-cpu.txt, requirements/requirements-deepcompile.txt, requirements/requirements-dev.txt, requirements/requirements-inf.txt, requirements/requirements-readthedocs.txt, requirements/requirements-sd.txt, requirements/requirements-sparse_attn.txt, requirements/requirements-sparse_pruning.txt, requirements/requirements-torchembed.txt, requirements/requirements-triton.txt, requirements/requirements.txt, setup.cfg, setup.py
has_codeql_workflownein
has_security_policyja
has_dependabot_confignein
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lockfiles für Abhängigkeiten. Die verbleibenden Gewichte wurden renormalisiert.
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_packages15
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:deepspeed@0.19.2 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 15 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.

71Gut · 0 % des Gesamtindex
Wie die Bewertung erfolgt
45/45Agentenanweisungen — AGENTS.md, CLAUDE.md
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes2.031
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
18/18Bootstrap mit einem Befehl — Makefile, docs/code-docs/Makefile
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration — .flake8, .pylintrc
0/11Statische Typprüfung
10/10Reproduzierbare Umgebung — Dockerfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — keine Daten
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfileja
typed_languagenein
bootstrap_filesMakefile, docs/code-docs/Makefile
has_devcontainernein
has_linter_configja
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 — 14/1.258 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes279.279
source_files_sampled1.258
oversized_source_files14
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
0/20MCP-Server
40/40Lauffähige Beispiele — examples
Verwendete Eingangsdaten
example_dirsexamples
has_mcp_signalnein
api_schema_files

Eckdaten

42.747GitHub-Sterne
100Mitwirkende
390Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
6Bus-Faktor
1.164offene Issues
PyPI, RubyGemsPaket-Ökosysteme

Warnungen zur Datenerhebung

  • OpenSSF Scorecard did not return a usable result (2026/07/21 01:23:49 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

Weitere Details

Alle Abhängigkeiten 58

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 58 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIaccelerateindirekt
PyPIautodoc-pydanticindirekt
PyPIclang-format18.1.3indirekt
PyPIcomet-mlindirekt
PyPIdeepspeed-kernelsindirekt
PyPIdiffusersindirekt
PyPIdocutilsindirekt
PyPIeinopsindirekt
PyPIfutureindirekt
PyPIgoogleindirekt
PyPIhjsonindirekt
PyPIimportlib-metadataindirekt
PyPIlm-eval0.3.0indirekt
PyPImpi4pyindirekt
PyPImsgpackindirekt
PyPImupindirekt
PyPIneural-compressor2.1.0indirekt
PyPIninjaindirekt
PyPInumpyindirekt
PyPIpackagingindirekt
PyPIpre-commitindirekt
PyPIprotobufindirekt
PyPIpsutilindirekt
PyPIpy-cpuinfoindirekt
PyPIpydanticindirekt
PyPIpytestindirekt
PyPIpytest-forkedindirekt
PyPIpytest-randomlyindirekt
PyPIpytest-xdistindirekt
PyPIqtorchindirekt
PyPIqtorch0.3.0indirekt
PyPIrecommonmarkindirekt
PyPIsafetensorsindirekt
PyPIscipyindirekt
PyPIsentencepieceindirekt
PyPIsphinxindirekt
PyPIsphinx-rtd-themeindirekt
PyPItabulateindirekt
PyPItensorboardindirekt
PyPItorchindirekt
PyPItorchembedindirekt
PyPItorchvisionindirekt
PyPItqdmindirekt
PyPItransformersindirekt
PyPItritonindirekt
PyPItriton1.0.0indirekt
PyPItriton2.1.0indirekt
PyPIwandbindirekt
PyPIxgboostindirekt
RubyGemsgithub-pagesindirekt
RubyGemsjekyll-feedindirekt
RubyGemsjekyll-include-cacheindirekt
RubyGemsjekyll-paginateindirekt
RubyGemsjekyll-remote-themeindirekt
RubyGemsminimal-mistakes-jekyllindirekt
RubyGemstzinfoindirekt
RubyGemstzinfo-dataindirekt
RubyGemswebrickindirekt
Abhängigkeits-Advisories 0

Die Installation von pypi:deepspeed@0.19.2 zieht 15 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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      "has_mcp_signal": false,
      "bootstrap_files": [
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      "api_schema_files": [],
      "has_devcontainer": false,
      "typecheck_configs": [],
      "largest_source_bytes": 279279,
      "source_files_sampled": 1258,
      "oversized_source_files": 14,
      "agent_instruction_files": [
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      "agent_instruction_max_bytes": 2031
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    "dependencies": {
      "manifests": [
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        "requirements/requirements-1bit-mpi.txt",
        "requirements/requirements-autotuning-ml.txt",
        "requirements/requirements-autotuning.txt",
        "requirements/requirements-cpu.txt",
        "requirements/requirements-deepcompile.txt",
        "requirements/requirements-dev.txt",
        "requirements/requirements-inf.txt",
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        "requirements/requirements-sd.txt",
        "requirements/requirements-sparse_attn.txt",
        "requirements/requirements-sparse_pruning.txt",
        "requirements/requirements-torchembed.txt",
        "requirements/requirements-triton.txt",
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      "dependencies": [],
      "all_dependencies": {
        "error": null,
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          {
            "name": "accelerate",
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          },
          {
            "name": "autodoc-pydantic",
            "direct": false,
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          {
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            "name": "future",
            "direct": false,
            "version": null,
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          },
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            "name": "google",
            "direct": false,
            "version": null,
            "ecosystem": "pypi"
          },
          {
            "name": "hjson",
            "direct": false,
            "version": null,
            "ecosystem": "pypi"
          },
          {
            "name": "importlib-metadata",
            "direct": false,
            "version": null,
            "ecosystem": "pypi"
          },
          {
            "name": "lm-eval",
            "direct": false,
            "version": "0.3.0",
            "ecosystem": "pypi"
          },
          {
            "name": "mpi4py",
            "direct": false,
            "version": null,
            "ecosystem": "pypi"
          },
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                "key": "pr_acceptance",
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                "status": "met",
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                "status": "missed",
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        ],
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        "name": "Security",
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        "weight": 0.16,
        "metrics": [
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                "code": "excluded_no_data",
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                "code": "weights_renormalized",
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                "key": "dependabot_config",
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                "points": 0,
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              },
              {
                "key": "dependency_lockfiles",
                "name": "Dependency lockfiles",
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                "status": "excluded",
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              {
                "key": "codeql_workflow",
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            ]
          },
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            "notes": [
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            "inputs": {
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              {
                "key": "no_advisories_left_outstanding",
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                "max_points": 40
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          },
          {
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            "notes": [],
            "value": 100,
            "inputs": {
              "source": "osv",
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              "red_flag": false,
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              "direct_malicious_packages": 0,
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                "key": "no_dependency_reported_as_a_malicious_package",
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                "details": [
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                    "code": "no_malicious_dependencies",
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                ],
                "max_points": 100
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            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
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            "name": "High-Risk Jurisdiction Exposure",
            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
            "notes": [
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                "code": "jurisdiction_evidence_limits",
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            "value": 100,
            "inputs": {
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            "components": [
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                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
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                "status": "met",
                "details": [
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                    "code": "jurisdiction_no_match",
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                "max_points": 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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        "weight": 0,
        "metrics": [
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            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
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              },
              {
                "code": "weights_renormalized",
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            ],
            "value": 75,
            "inputs": {
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                "key": "agent_instructions",
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                "max_points": 45
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              {
                "key": "machine_readable_docs_llms_txt",
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                ],
                "max_points": 40
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            ]
          },
          {
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            "notes": [
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                "code": "excluded_no_data",
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              },
              {
                "code": "weights_renormalized",
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                "key": "automated_tests",
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                "points": 22,
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              },
              {
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                ],
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              },
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                "key": "demonstrated_agent_practice",
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                "details": [
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                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
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                "details": [
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                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
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                "status": "excluded",
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                ],
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            ]
          },
          {
            "key": "ai_code_legibility",
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            "notes": [],
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                "status": "missed",
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                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
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                "status": "partial",
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                ],
                "max_points": 55
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            ]
          },
          {
            "key": "ai_interfaces",
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            "notes": [],
            "value": 40,
            "inputs": {
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              "has_mcp_signal": false,
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            "components": [
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                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "examples",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "examples"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "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."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "OpenSSF Scorecard did not return a usable result (2026/07/21 01:23:49 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-21T01:24:38.970063Z",
  "schema_version": "0.16.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/d/deepspeedai/DeepSpeed.svg",
  "full_name": "deepspeedai/DeepSpeed",
  "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 v0.16.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte StatistikenPyPI.