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Software-GesundheitsberichtSchema 0.11.0 · Metriken 1.13.0 · 2026-07-16 23:50 UTC

anthropics / anthropic-sdk-python

PythonMIT★ 3.749 Sterne⑂ 775 Forksseit Jan. 2023Auf GitHub ansehen ↗

anthropics/anthropic-sdk-python erreicht einen Gesundheitsindex von 75 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (98/100) ab, am schwächsten bei AI Readiness (46/100). Zuletzt heute aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

75
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.

75
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

AnthropicOrganisation
76.910 Follower94 öffentliche Reposseit Dez. 2020

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlicht
PyPIanthropic0.117.0-200vor 0 Tagen

Metriken nach Kategorie

Vitalität

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

98Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 0 Tagen
32.5/36Commit-Rhythmus — 47/52 Wochen mit Commits
18/18Commit-Volumen — 367 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_year367
human_commit_share
days_since_last_push0
active_weeks_last_year47

Release-Disziplin

100Exzellent
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 100 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 0 Tagen
27/27Release-Rhythmus — ein Release etwa alle 3,5 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count100
latest_release_tagv0.117.0
releases_from_tagsnein
days_since_latest_release0
mean_days_between_releases3,5
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?

83Gut · 18 % des Gesamtindex
Wie die Bewertung erfolgt
58/60Stars — 3.749 Stars
24.1/25Forks — 775 Forks
12.6/15Watcher — 183 Watcher
Verwendete Eingangsdaten
forks775
stars3.749
watchers183
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (MIT)
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?

76Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
11.4/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 49 % der Commits
13.5/13.5Breite der Beitragenden — 60 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 8 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor2
contributors_sampled60
top_contributor_share0,494
Wie die Bewertung erfolgt
32.2/46.8Issue-Lösungsquote — 69 % der Issues geschlossen
30.6/38.3PR-Annahme — 792/989 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — keine Daten
Verwendete Eingangsdaten
merged_prs792
open_issues140
closed_issues310
issue_closed_ratio0,689
closed_unmerged_prs197
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Code-Review. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
25/25Reichweite des Inhabers — 76.910 Follower von anthropics
24.1/25Kontohistorie — 94 öffentliche Repos, Kontoalter ca. 5 Jahre
Verwendete Eingangsdaten
followers76.910
owner_typeOrganization
is_verified
owner_loginanthropics
public_repos94
account_age_days2.035

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 0 Tagen
20/20Versionshistorie — 200 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesanthropic
ecosystemspypi
any_deprecatednein
min_days_since_publish0

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

53Mittel · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 4 Workflow(s)
24/24Tests vorhanden
0/16Linter-Konfiguration
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 7 out of 7 merged PRs checked by a CI test -- score normalized to 10
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_confignein
has_precommit_confignein

Dokumentation

30Gefährdet
Wie die Bewertung erfolgt
30/30README
0/25Dokumentationsverzeichnis
0/15Dokumentations-/Homepage-Site
0/10Repository-Beschreibung
0/10Topics
0/10Wiki
Verwendete Eingangsdaten
topics
has_wikinein
homepage
has_readmeja
has_docs_dirnein
has_descriptionnein

Sicherheit

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

59Mittel · 16 % des Gesamtindex
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
4.5/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 7 out of 7 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — keine Daten
2.5/2.5Contributors — project has 8 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
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
3.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
5/5SAST — SAST tool is run on all commits
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 — 55 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate5,9
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): code_review, 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.

46Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
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
11/11Statische Typprüfung — src/anthropic/py.typed
10/10Reproduzierbare Umgebung — devcontainer, Dockerfile, lockfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
7/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfilesuv.lock
has_dockerfileja
typed_languagenein
bootstrap_files
has_devcontainerja
has_linter_confignein
typecheck_configssrc/anthropic/py.typed
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
27/45Typprüfbarer Code — Python mit Typprüfungs-Konfiguration (src/anthropic/py.typed)
54.6/55Handhabbare Dateigrößen — 9/1.192 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes200.386
source_files_sampled1.192
oversized_source_files9
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
20/20MCP-Server
40/40Lauffähige Beispiele — examples
Verwendete Eingangsdaten
example_dirsexamples
has_mcp_signalja
api_schema_files

Eckdaten

3.749GitHub-Sterne
60Mitwirkende
367Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
2Bus-Faktor
140offene Issues
PyPIPaket-Ökosysteme

Weitere Details

OpenSSF Scorecard 5.9 / 10
5.9Gesamtwert

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 · 2026-07-16 23:50 UTC

10Binary-Artifactsno binaries found in the repo
6Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests7 out of 7 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
k. A.Code-ReviewFound no human activity in the last 7 changesets
10Contributorsproject has 8 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense 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
7Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 7
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities55 existing vulnerabilities detected
Direkte Abhängigkeiten 8
RegistryPaketVersionsvorgabeManifest
PyPIhttpx>=0.25.0, <1pyproject.toml
PyPIpydantic>=1.9.0, <3pyproject.toml
PyPItyping-extensions>=4.14, <5pyproject.toml
PyPIanyio>=3.5.0, <5pyproject.toml
PyPIdistro>=1.7.0, <2pyproject.toml
PyPIsniffiopyproject.toml
PyPIjiter>=0.4.0, <1pyproject.toml
PyPIdocstring-parser>=0.15,<1pyproject.toml
Alle Abhängigkeiten 110

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 9 direkte und 101 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIanyio4.12.1direkt
PyPIdistro1.9.0direkt
PyPIdocstring-parser0.17.0direkt
PyPIhttpx0.28.1direkt
PyPIjiter0.13.0direkt
PyPIpydantic1.10.26direkt
PyPIpydantic2.12.5direkt
PyPIsniffio1.3.1direkt
PyPItyping-extensions4.15.0direkt
PyPIaiohappyeyeballs2.6.1indirekt
PyPIaiohttp3.13.3indirekt
PyPIaiosignal1.4.0indirekt
PyPIannotated-types0.7.0indirekt
PyPIanthropic0.102.0indirekt
PyPIasttokens3.0.1indirekt
PyPIasync-timeout5.0.1indirekt
PyPIattrs25.4.0indirekt
PyPIbackports-asyncio-runner1.2.0indirekt
PyPIboto31.42.69indirekt
PyPIboto3-stubs1.42.69indirekt
PyPIbotocore1.42.69indirekt
PyPIbotocore-stubs1.42.41indirekt
PyPIcertifi2026.2.25indirekt
PyPIcffi2.0.0indirekt
PyPIcharset-normalizer3.4.6indirekt
PyPIclick8.3.1indirekt
PyPIcolorama0.4.6indirekt
PyPIcryptography46.0.5indirekt
PyPIdeprecated1.3.1indirekt
PyPIdirty-equals0.11indirekt
PyPIexceptiongroup1.3.1indirekt
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PyPIgriffelib2.0.0indirekt
PyPIh110.16.0indirekt
PyPIhttp-snapshot0.1.8indirekt
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PyPIimportlib-metadata8.7.1indirekt
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PyPIjsonschema-specifications2025.9.1indirekt
PyPImarkdown-it-py3.0.0indirekt
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PyPImypy1.17.0indirekt
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PyPInodeenv1.10.0indirekt
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PyPIpluggy1.6.0indirekt
PyPIpropcache0.4.1indirekt
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PyPIpytest8.4.2indirekt
PyPIpytest9.0.2indirekt
PyPIpytest-asyncio1.2.0indirekt
PyPIpytest-asyncio1.3.0indirekt
PyPIpytest-xdist3.8.0indirekt
PyPIpython-dateutil2.9.0.post0indirekt
PyPIpython-dotenv1.2.2indirekt
PyPIpython-multipart0.0.22indirekt
PyPIpywin32311indirekt
PyPIreferencing0.37.0indirekt
PyPIrequests2.32.5indirekt
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PyPIrich14.3.3indirekt
PyPIrpds-py0.30.0indirekt
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PyPIzipp3.23.0indirekt
JSON-Rohbericht maschinenlesbar
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      "pushed_at": "2026-07-16T19:36:18Z",
      "created_at": "2023-01-17T20:57:10Z",
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      "followers": 76910,
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          "versions_count": 200,
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          "dependents_count": null,
          "deprecation_note": null,
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          "first_published_at": "2023-02-09T06:19:01.733591Z",
          "latest_published_at": "2026-07-16T19:36:13.070306Z",
          "latest_version_yanked": null,
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      "stars": 3749,
      "watchers": 183,
      "open_issues_and_prs": 346
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      "largest_source_bytes": 200386,
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      "oversized_source_files": 9,
      "agent_instruction_files": [],
      "agent_instruction_max_bytes": null
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                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              },
              {
                "key": "topics",
                "name": "Topics",
                "detail": null,
                "points": 0,
                "status": "missed",
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              {
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                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
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          }
        ],
        "description": "Are baseline engineering and documentation practices in place?"
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      {
        "key": "security",
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        "value": 59,
        "weight": 0.16,
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            "key": "security_posture",
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              {
                "code": "excluded_no_data",
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                  "components": [
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              },
              {
                "code": "weights_renormalized",
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            "value": 59,
            "inputs": {
              "source": "openssf_scorecard",
              "checks_evaluated": 15,
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              "checks_inconclusive": 3,
              "scorecard_aggregate": 5.9
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            "components": [
              {
                "key": "binary_artifacts",
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                "detail": "no binaries found in the repo",
                "points": 7.5,
                "status": "met",
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              {
                "key": "branch_protection",
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                "detail": "branch protection is not maximal on development and all release branches",
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                "details": [],
                "max_points": 7.5
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                "key": "ci_tests",
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                "detail": "7 out of 7 merged PRs checked by a CI test -- score normalized to 10",
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                "details": [],
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                "key": "cii_best_practices",
                "name": "CII-Best-Practices",
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                "points": 0,
                "status": "missed",
                "details": [],
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              },
              {
                "key": "code_review",
                "name": "Code-Review",
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                "key": "contributors",
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              {
                "key": "dangerous_workflow",
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                "key": "dependency_update_tool",
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                "status": "missed",
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              {
                "key": "fuzzing",
                "name": "Fuzzing",
                "detail": "project is not fuzzed",
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                "status": "missed",
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                "max_points": 5
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              {
                "key": "license",
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                "detail": "license file detected",
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                "key": "maintained",
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                "status": "met",
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                "status": "excluded",
                "details": [
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                "max_points": 5
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                "key": "pinned_dependencies",
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                "detail": "dependency not pinned by hash detected -- score normalized to 7",
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                "status": "partial",
                "details": [],
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                "key": "sast",
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                "key": "security_policy",
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                "points": 5,
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                "key": "signed_releases",
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                "key": "token_permissions",
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                "status": "missed",
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              {
                "key": "vulnerabilities",
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                "points": 0,
                "status": "missed",
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          },
          {
            "key": "high_risk_jurisdiction_exposure",
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                "Iran",
                "North Korea"
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              "review_only_matches": 0,
              "assessed_self_published_locations": 6
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                "key": "policy_exposure_multiplier",
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                "points": 100,
                "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": "ai_readiness",
        "band": "at_risk",
        "name": "AI Readiness",
        "value": 46,
        "weight": 0,
        "metrics": [
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            "name": "Agent context & guidance",
            "note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
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                "params": {
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                "details": [
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                    "code": "no_agent_instructions",
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                "max_points": 45
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                "key": "machine_readable_docs_llms_txt",
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                "points": 0,
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                "details": [],
                "max_points": 15
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                "status": "excluded",
                "details": [
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                    "code": "no_data",
                    "params": {}
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                "max_points": 40
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          },
          {
            "key": "ai_verify_loop",
            "band": "moderate",
            "name": "Verify loop (build / test / typecheck)",
            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
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                "code": "weights_renormalized",
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            "value": 61,
            "inputs": {
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            "components": [
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                "key": "one_command_bootstrap",
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                "points": 0,
                "status": "missed",
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              {
                "key": "automated_tests",
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                "points": 22,
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                "points": 0,
                "status": "missed",
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                "status": "met",
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              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
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                "points": 0,
                "status": "excluded",
                "details": [
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                    "code": "no_data",
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                "max_points": 10
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                "key": "automated_maintenance",
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                "details": [
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                "max_points": 8
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              {
                "key": "openssf_scorecard_pinned_dependencies",
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                "status": "partial",
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                "max_points": 10
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            "key": "ai_code_legibility",
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            "notes": [],
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            "inputs": {
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              "largest_source_bytes": 200386,
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              "oversized_source_files": 9
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            "notes": [],
            "value": 60,
            "inputs": {
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            },
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                "points": 0,
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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."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [],
  "report_type": "repository",
  "generated_at": "2026-07-16T23:50:56.456697Z",
  "schema_version": "0.11.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/a/anthropics/anthropic-sdk-python.svg",
  "full_name": "anthropics/anthropic-sdk-python",
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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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