Öffentliches Register
Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-26 07:41 UTC

debbiemarkslab / EVcouplings

Evolutionary couplings from protein and RNA sequence alignments

Jupyter Notebook · PythonEigene Lizenz★ 313 Sterne⑂ 83 Forksseit Juli 2016Auf GitHub ansehen ↗

debbiemarkslab/EVcouplings erreicht einen Gesundheitsindex von 54 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Engineering Quality (72/100) ab, am schwächsten bei Vitality (31/100). Zuletzt vor 96 Tagen aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.

54
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.

54
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

Debora Marks LabOrganisation
135 Follower33 öffentliche Reposseit Sept. 2014

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlichtTags
PyPIevcouplings0.2.1-7vor 627 Tagenanalysiscouplingsevolutionary

Metriken nach Kategorie

Vitalität

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

31Gefährdet · 22 % des Gesamtindex
Wie die Bewertung erfolgt
9.9/36Push-Aktualität — letzter Push vor 96 Tagen
2.1/36Commit-Rhythmus — 3/52 Wochen mit Commits
9.7/18Commit-Volumen — 11 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_year11
human_commit_share1
days_since_last_push96
active_weeks_last_year3

Release-Disziplin

44Gefährdet
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 7 Releases veröffentlicht
7.2/36Release-Aktualität — letztes Release vor 627 Tagen
5.4/27Release-Rhythmus — ein Release etwa alle 436,7 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count7
latest_release_tagv0.2.1
releases_from_tagsnein
days_since_latest_release627
mean_days_between_releases436,7
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?

55Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
40.5/60Stars — 313 Stars
16/25Forks — 83 Forks
7.7/15Watcher — 25 Watcher
Verwendete Eingangsdaten
forks83
stars313
watchers25
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

Nachhaltigkeit & Governance

Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?

61Mittel · 24 % des Gesamtindex
Wie die Bewertung erfolgt
9/54Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab
8.2/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 63 % der Commits
12.2/13.5Breite der Beitragenden — 9 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 10 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor1
contributors_sampled9
top_contributor_share0,634
Wie die Bewertung erfolgt
32.9/46.8Issue-Lösungsquote — 70 % der Issues geschlossen
31.3/38.3PR-Annahme — 108/132 entschiedene PRs gemergt
1.5/15OpenSSF Scorecard: Code-Review — Found 1/6 approved changesets -- score normalized to 1
Verwendete Eingangsdaten
merged_prs108
open_issues59
closed_issues140
issue_closed_ratio0,704
closed_unmerged_prs24
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
15.3/25Reichweite des Inhabers — 135 Follower von debbiemarkslab
23.2/25Kontohistorie — 33 öffentliche Repos, Kontoalter ca. 11 Jahre
Verwendete Eingangsdaten
followers135
owner_typeOrganization
is_verified
owner_logindebbiemarkslab
public_repos33
account_age_days4.316
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
14/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 627 Tagen
20/20Versionshistorie — 7 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesevcouplings
ecosystemspypi
any_deprecatednein
min_days_since_publish627

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

72Gut · 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
6/20OpenSSF Scorecard: CI-Tests — 2 out of 6 merged PRs checked by a CI test -- score normalized to 3
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_confignein
has_precommit_confignein

Dokumentation

100Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — http://evcouplings.org
10/10Repository-Beschreibung
10/10Topics — 4 Topics
10/10Wiki
Verwendete Eingangsdaten
topicsstructure-prediction, 3d-structure, protein, protein-complexes
has_wikija
homepagehttp://evcouplings.org
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

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

50Mittel · 16 % des Gesamtindex

Sicherheitslage

37Gefährdet
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — keine Daten
0.8/2.5CI-Tests — 2 out of 6 merged PRs checked by a CI test -- score normalized to 3
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 1/6 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 10 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
2.2/2.5Lizenz — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packaging — keine Daten
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
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_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate3,7
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, packaging, 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_packages39
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:evcouplings@0.2.1 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 39 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.

34Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
13.3/40Lesbare Commit-Historie — 25 von 100 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share0,25
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
18/18Bootstrap mit einem Befehl — docs/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
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_filesdocs/Makefile
has_devcontainernein
has_linter_confignein
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Jupyter Notebook ohne Typprüfungs-Konfiguration
53.6/55Handhabbare Dateigrößen — 2/81 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languageJupyter Notebook
largest_source_bytes263.767
source_files_sampled81
oversized_source_files2
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
0/20MCP-Server
40/40Lauffähige Beispiele — example, notebooks
Verwendete Eingangsdaten
example_dirsexample, notebooks
has_mcp_signalnein
api_schema_files

Eckdaten

313GitHub-Sterne
9Mitwirkende
11Commits, letzte 12 Monate
96Tage seit letztem Push
7Releases
1Bus-Faktor
59offene 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 ★ / 83 ⇿
0Sterne
83Forks
6Releases

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.

0204060807732017-092022-022026-07
Major 0Minor 0Patch 6

Jeder Punkt umfasst 9 Tage.

OpenSSF Scorecard 3.7 / 10
3.7Gesamtwert

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 · 2026-07-26 07:41 UTC

10Binary-Artifactsno binaries found in the repo
k. A.Branch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
3CI-Tests2 out of 6 merged PRs checked by a CI test -- score normalized to 3
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/6 approved changesets -- score normalized to 1
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update 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
k. A.Packagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direkte Abhängigkeiten 18
RegistryPaketVersionsvorgabeManifest
PyPIbilliardpyproject.toml
PyPIbiopython>=1.84pyproject.toml
PyPIbokehpyproject.toml
PyPIclickpyproject.toml
PyPIfilelockpyproject.toml
PyPIjinja2pyproject.toml
PyPImatplotlibpyproject.toml
PyPImsgpackpyproject.toml
PyPInumbapyproject.toml
PyPInumpypyproject.toml
PyPIpandaspyproject.toml
PyPIpsutilpyproject.toml
PyPIrequestspyproject.toml
PyPIruamel.yaml<0.18pyproject.toml
PyPIscikit-learnpyproject.toml
PyPIscipypyproject.toml
PyPIseabornpyproject.toml
PyPIsetuptools>=18.2pyproject.toml
Alle Abhängigkeiten 18

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 18 direkte und 0 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIbilliarddirekt
PyPIbiopythondirekt
PyPIbokehdirekt
PyPIclickdirekt
PyPIfilelockdirekt
PyPIjinja2direkt
PyPImatplotlibdirekt
PyPImsgpackdirekt
PyPInumbadirekt
PyPInumpydirekt
PyPIpandasdirekt
PyPIpsutildirekt
PyPIrequestsdirekt
PyPIruamel-yamldirekt
PyPIscikit-learndirekt
PyPIscipydirekt
PyPIseaborndirekt
PyPIsetuptoolsdirekt
Abhängigkeits-Advisories 0

Die Installation von pypi:evcouplings@0.2.1 zieht 39 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": "openssf_scorecard_contributors",
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                "key": "pr_acceptance",
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                "key": "owner_reach",
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            "inputs": {
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              "ecosystems": "pypi",
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        ],
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                "key": "fuzzing",
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                "status": "missed",
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                "max_points": 5
              },
              {
                "key": "sast",
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                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "security_policy",
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                "detail": "security policy file not detected",
                "points": 0,
                "status": "missed",
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                "max_points": 5
              },
              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "no releases found",
                "points": 0,
                "status": "excluded",
                "details": [
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                    "code": "no_data",
                    "params": {}
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                ],
                "max_points": 7.5
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                "key": "token_permissions",
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                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "vulnerabilities",
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                "points": 7.5,
                "status": "met",
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                "max_points": 7.5
              }
            ]
          },
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            "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:evcouplings@0.2.1 runtime dependency closure — what installing the published package pulls in — 39 packages. Reachability is not analyzed.",
            "notes": [
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                "code": "excluded_no_data",
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              },
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                }
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              {
                "code": "advisories_reachability",
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              "assessed_packages": 39,
              "unassessed_packages": 0,
              "affected_by_severity": "none",
              "direct_affected_packages": 0
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            "key": "malicious_dependencies",
            "band": "excellent",
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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": 39,
              "malicious_packages": 0,
              "direct_malicious_packages": 0,
              "withdrawn_malicious_packages": 0,
              "installable_malicious_packages": 0
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          {
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            "notes": [
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                "Iran",
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              "assessed_self_published_locations": 11
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                  }
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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": 34,
        "weight": 0,
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            "name": "Agent context & guidance",
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                "key": "machine_readable_docs_llms_txt",
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                "points": 0,
                "status": "missed",
                "details": [],
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              },
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                "key": "legible_commit_history",
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                "points": 13.3,
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                "details": [
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              {
                "key": "automated_tests",
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                "points": 22,
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              },
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                "key": "demonstrated_agent_practice",
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                "points": 0,
                "status": "missed",
                "details": [
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                "key": "automated_maintenance",
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                "detail": "no automated dependency updates observed",
                "points": 0,
                "status": "missed",
                "details": [
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                    "code": "no_dependency_automation",
                    "params": {}
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                "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": 54,
            "inputs": {
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              "largest_source_bytes": 263767,
              "source_files_sampled": 81,
              "oversized_source_files": 2
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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": "Jupyter Notebook"
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                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "2/81 source files over 60KB",
                "points": 53.6,
                "status": "partial",
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                      "oversized": 2
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                "max_points": 55
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          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
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            "note": null,
            "notes": [],
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                "detail": null,
                "points": 0,
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              },
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                "status": "missed",
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              },
              {
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                    "params": {
                      "files": "example, 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": "2026-07-26T07:41:44.719075Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/d/debbiemarkslab/EVcouplings.svg",
  "full_name": "debbiemarkslab/EVcouplings",
  "license_state": "custom",
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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 v0.27.0 — vollständige Methodik · Metriken-Wiki.

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