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Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-28 14:40 UTC

computationalmodelling / nbval

A py.test plugin to validate Jupyter notebooks

Python · Jupyter NotebookEigene Lizenz★ 453 Sterne⑂ 54 Forksseit Apr. 2015Auf GitHub ansehen ↗

computationalmodelling/nbval erreicht einen Gesundheitsindex von 56 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Engineering Quality (68/100) ab, am schwächsten bei Vitality (22/100). Zuletzt vor 314 Tagen aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.

56
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.

56
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

22 Follower7 öffentliche Reposseit Apr. 2015

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlicht
PyPInbval0.11.0585.75419vor 876 Tagen

Metriken nach Kategorie

Vitalität

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

22Kritisch · 22 % des Gesamtindex
Wie die Bewertung erfolgt
3.6/36Push-Aktualität — letzter Push vor 314 Tagen
2.1/36Commit-Rhythmus — 3/52 Wochen mit Commits
9.4/18Commit-Volumen — 10 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_year10
human_commit_share0,99
days_since_last_push314
active_weeks_last_year3

Release-Disziplin

32Gefährdet
Wie die Bewertung erfolgt
16.2/27Liefert Releases aus — 12 Versions-Tags (keine GitHub-Releases)
0/36Release-Aktualität — letztes Release vor 876 Tagen
12.6/27Release-Rhythmus — ein Release etwa alle 299,3 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count12
latest_release_tag0.11.0
releases_from_tagsja
days_since_latest_release876
mean_days_between_releases299,3
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?

65Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
43.1/60Stars — 453 Stars
14.4/25Forks — 54 Forks
5.3/15Watcher — 10 Watcher
Verwendete Eingangsdaten
forks54
stars453
watchers10
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
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
Wie die Bewertung erfolgt
76.9/80Downloads pro Monat — 585.754 Downloads/Monat über pypi
0/20Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen
Verwendete Eingangsdaten
packagesnbval
dependents
ecosystemspypi
total_downloads
monthly_downloads585.754
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?

66Mittel · 24 % des Gesamtindex
Wie die Bewertung erfolgt
25.2/54Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab
14.6/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 35 % der Commits
13.5/13.5Breite der Beitragenden — 32 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 69 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor2
contributors_sampled32
top_contributor_share0,349
Wie die Bewertung erfolgt
28.2/46.8Issue-Lösungsquote — 60 % der Issues geschlossen
33.6/38.3PR-Annahme — 93/106 entschiedene PRs gemergt
12/15OpenSSF Scorecard: Code-Review — Found 13/15 approved changesets -- score normalized to 8
Verwendete Eingangsdaten
merged_prs93
open_issues46
closed_issues70
issue_closed_ratio0,603
closed_unmerged_prs13
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
9.8/25Reichweite des Inhabers — 22 Follower von computationalmodelling
18.6/25Kontohistorie — 7 öffentliche Repos, Kontoalter ca. 11 Jahre
Verwendete Eingangsdaten
followers22
owner_typeOrganization
is_verified
owner_logincomputationalmodelling
public_repos7
account_age_days4.128

Paketpflege

69Mittel
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
4/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 876 Tagen
20/20Versionshistorie — 19 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesnbval
ecosystemspypi
any_deprecatednein
min_days_since_publish876

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

68Mittel · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 2 Workflow(s)
24/24Tests vorhanden
0/16Linter-Konfiguration
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
8/20OpenSSF Scorecard: CI-Tests — 6 out of 15 merged PRs checked by a CI test -- score normalized to 4
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_confignein
has_precommit_confignein

Dokumentation

85Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
0/15Dokumentations-/Homepage-Site
10/10Repository-Beschreibung
10/10Topics — 6 Topics
10/10Wiki
Verwendete Eingangsdaten
topicsipython-notebook, jupyter-notebook, python, testing, pytest-plugin, pytest
has_wikija
homepage
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

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

60Mittel · 16 % des Gesamtindex
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
1/2.5CI-Tests — 6 out of 15 merged PRs checked by a CI test -- score normalized to 4
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6/7.5Code-Review — Found 13/15 approved changesets -- score normalized to 8
2.5/2.5Contributors — project has 69 contributing companies or organizations
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
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
0/7.5Signed-Releases — keine Daten
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5,1
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): signed_releases. 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_packages41
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:nbval@0.11.0 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 41 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.

41Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
24.8/40Lesbare Commit-Historie — 46 von 99 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share0,465
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
18/18Bootstrap mit einem Befehl — docs/Makefile, issues/67/Makefile, tests/Makefile
22/22Automatisierte Tests
0/11Lint-/Format-Konfiguration
0/11Statische Typprüfung
0/10Reproduzierbare Umgebung
0/10Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100
8/8Automatisierte Wartung — 1 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates
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_filesdocs/Makefile, issues/67/Makefile, tests/Makefile
has_devcontainernein
has_linter_confignein
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0,01
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
55/55Handhabbare Dateigrößen — 0/23 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes36.043
source_files_sampled23
oversized_source_files0
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
0/20MCP-Server
40/40Lauffähige Beispiele — notebooks
Verwendete Eingangsdaten
example_dirsnotebooks
has_mcp_signalnein
api_schema_files

Eckdaten

453GitHub-Sterne
32Mitwirkende
10Commits, letzte 12 Monate
314Tage seit letztem Push
12Releases
2Bus-Faktor
46offene 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 ★ / 54 ⇿
0Sterne
54Forks
12Releases

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.

01020304050605122015-042020-102026-04
Major 0Minor 3Patch 9

Jeder Punkt umfasst 11 Tage.

OpenSSF Scorecard 5.1 / 10
5.1Gesamtwert

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 · 2026-07-28 14:39 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
4CI-Tests6 out of 15 merged PRs checked by a CI test -- score normalized to 4
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 13/15 approved changesets -- score normalized to 8
10Contributorsproject has 69 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense 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
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Alle Abhängigkeiten 5

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 5 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIcoverageindirekt
PyPIipykernelindirekt
PyPIjupyter-clientindirekt
PyPInbformatindirekt
PyPIpytestindirekt
Abhängigkeits-Advisories 0

Die Installation von pypi:nbval@0.11.0 zieht 41 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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                "key": "token_permissions",
                "name": "Token-Permissions",
                "detail": "detected GitHub workflow tokens with excessive permissions",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "vulnerabilities",
                "name": "Vulnerabilities",
                "detail": "0 existing vulnerabilities detected",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              }
            ]
          },
          {
            "key": "dependency_advisories",
            "band": "excellent",
            "name": "Dependency advisories",
            "note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:nbval@0.11.0 runtime dependency closure — what installing the published package pulls in — 41 packages. Reachability is not analyzed.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
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              {
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              {
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                  "package": "pypi:nbval@0.11.0",
                  "assessed": 41
                }
              },
              {
                "code": "advisories_reachability",
                "params": {}
              }
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            "inputs": {
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              "assessed_packages": 41,
              "unassessed_packages": 0,
              "affected_by_severity": "none",
              "direct_affected_packages": 0
            },
            "components": [
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                "status": "met",
                "details": [
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                "key": "indirect_dependencies_free_of_known_advisories",
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                  }
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                "key": "no_advisories_left_outstanding",
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                  }
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            ]
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          {
            "key": "malicious_dependencies",
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            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "source": "osv",
              "meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
              "packages": [],
              "red_flag": false,
              "assessed_packages": 41,
              "malicious_packages": 0,
              "direct_malicious_packages": 0,
              "withdrawn_malicious_packages": 0,
              "installable_malicious_packages": 0
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            "components": [
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                "key": "no_dependency_reported_as_a_malicious_package",
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                "status": "met",
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          {
            "key": "high_risk_jurisdiction_exposure",
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            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
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              }
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                "Iran",
                "North Korea"
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              "assessed_self_published_locations": 24
            },
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                "status": "met",
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                    "params": {}
                  }
                ],
                "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?"
      },
      {
        "key": "ai_readiness",
        "band": "at_risk",
        "name": "AI Readiness",
        "value": 41,
        "weight": 0,
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            "name": "Agent context & guidance",
            "note": null,
            "notes": [],
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                    "code": "no_agent_instructions",
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                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
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                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
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                "points": 24.8,
                "status": "partial",
                "details": [
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                    "code": "legible_history",
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                      "sampled": 99
                    }
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                "max_points": 40
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            "key": "ai_verify_loop",
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            "note": null,
            "notes": [],
            "value": 48,
            "inputs": {
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                "key": "one_command_bootstrap",
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                "status": "met",
                "details": [
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                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
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                "key": "lint_format_config",
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                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
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                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
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                "key": "reproducible_environment",
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                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "no agent-authored commits among the last 100",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_authored_commits",
                    "params": {
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "1 of the last 100 commits are automated dependency updates",
                "points": 8,
                "status": "met",
                "details": [
                  {
                    "code": "dependency_bot_commits",
                    "params": {
                      "count": 1,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "moderate",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 55,
            "inputs": {
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              "largest_source_bytes": 36043,
              "source_files_sampled": 23,
              "oversized_source_files": 0
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                "key": "type_checkable_code",
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                "points": 0,
                "status": "missed",
                "details": [
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                    "code": "no_typecheck_config_language",
                    "params": {
                      "language": "Python"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "0/23 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
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                    "code": "oversized_source_files",
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                      "sampled": 23,
                      "oversized": 0
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                "max_points": 55
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          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
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              ],
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                "detail": null,
                "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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                "points": 40,
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                "details": [
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                      "files": "notebooks"
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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": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-28T14:40:23.533935Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/c/computationalmodelling/nbval.svg",
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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 v0.27.0 — vollständige Methodik · Metriken-Wiki.

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