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Software-GesundheitsberichtSchema 0.27.0 · Metriken 1.13.0 · 2026-07-23 07:49 UTC

HugoBlox / academic-file-converter

📚 Import Bibtex publications and Jupyter Notebook blog posts into your Markdown website or book. 将Bibtex转换为Markdown网站

Python · Jupyter NotebookMIT★ 419 Sterne⑂ 106 Forksseit Okt. 2018Auf GitHub ansehen ↗

HugoBlox/academic-file-converter erreicht einen Gesundheitsindex von 45 von 100 und liegt damit im Bereich Gefährdet. Am stärksten schneidet es bei Engineering Quality (68/100) ab, am schwächsten bei Vitality (41/100). Zuletzt vor 1 Tag aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.

45
gesamt / 100
Gefährdet

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.

45
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.

Abandonment Policy applies a 85% multiplier to weighted overall health.

Eigentümerschaft

HugoBloxOrganisation
232 Follower20 öffentliche Reposseit Okt. 2023

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlichtTags
PyPIacademicverweist auf ein anderes Repo — nicht bewertet0.11.2-29vor 969 Tagenresearchjupyterbibtexmarkdownlatexpublicationreferenceacademicconverter

Metriken nach Kategorie

Vitalität

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

41Gefährdet · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 1 Tagen
0.7/36Commit-Rhythmus — 1/52 Wochen mit Commits
2.7/18Commit-Volumen — 1 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Verwendete Eingangsdaten
commits_last_year1
human_commit_share0,97
days_since_last_push1
active_weeks_last_year1

Release-Disziplin

44Gefährdet
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 22 Releases veröffentlicht
0/36Release-Aktualität — letztes Release vor 962 Tagen
12.6/27Release-Rhythmus — ein Release etwa alle 166,1 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count22
latest_release_tagv0.11.2
releases_from_tagsnein
days_since_latest_release962
mean_days_between_releases166,1
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?

58Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
42.5/60Stars — 419 Stars
16.8/25Forks — 106 Forks
5.3/15Watcher — 10 Watcher
Verwendete Eingangsdaten
forks106
stars419
watchers10
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (MIT)
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?

50Mittel · 24 % des Gesamtindex
Wie die Bewertung erfolgt
9/54Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab
3.5/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 84 % der Commits
13.5/13.5Breite der Beitragenden — 17 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Verwendete Eingangsdaten
bus_factor1
contributors_sampled17
top_contributor_share0,844
Wie die Bewertung erfolgt
45.4/46.8Issue-Lösungsquote — 97 % der Issues geschlossen
10.4/38.3PR-Annahme — 28/103 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — Found 0/29 approved changesets -- score normalized to 0
Verwendete Eingangsdaten
merged_prs28
open_issues2
closed_issues68
issue_closed_ratio0,971
closed_unmerged_prs75
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
17/25Reichweite des Inhabers — 232 Follower von HugoBlox
15.1/25Kontohistorie — 20 öffentliche Repos, Kontoalter ca. 2 Jahre
Verwendete Eingangsdaten
followers232
owner_typeOrganization
is_verified
owner_loginHugoBlox
public_repos20
account_age_days996

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

68Mittel · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 3 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration — .flake8
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
6/20OpenSSF Scorecard: CI-Tests — 1 out of 3 merged PRs checked by a CI test -- score normalized to 3
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_confignein
Wie die Bewertung erfolgt
30/30README
0/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://docs.hugoblox.com/reference/content-types/#automatically-import-publications-from-bibtex
10/10Repository-Beschreibung
10/10Topics — 19 Topics
0/10Wiki
Verwendete Eingangsdaten
topicsacademic, hugo, cli, bibtex, gatsby, nextjs, jekyll, biblatex, latex, markdown, markdown-converter, static-site-generator, wowchemy, astrojs, hugo-theme, bibliography, citations, csl-json, reference-manager
has_wikinein
homepagehttps://docs.hugoblox.com/reference/content-types/#automatically-import-publications-from-bibtex
has_readmeja
has_docs_dirnein
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 — branch protection not enabled on development/release branches
0.8/2.5CI-Tests — 1 out of 3 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
0/7.5Code-Review — Found 0/29 approved changesets -- score normalized to 0
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Lizenz — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packaging — keine Daten
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
3.5/5SAST — SAST tool detected but not run on all commits
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
0/7.5Vulnerabilities — 81 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate3,7
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): 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_packages34
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:academic@0.11.2 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 34 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.

54Mittel · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
40/40Lesbare Commit-Historie — 88 von 97 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text)
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share0,907
agent_instruction_files
agent_instruction_max_bytes
Wie die Bewertung erfolgt
18/18Bootstrap mit einem Befehl — Makefile
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration — .flake8
0/11Statische Typprüfung
10/10Reproduzierbare Umgebung — lockfile
0/10Belegte Agentenpraxis — keine von Agenten verfassten Commits unter den letzten 100
8/8Automatisierte Wartung — 3 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
lockfilespoetry.lock
has_dockerfilenein
typed_languagenein
bootstrap_filesMakefile
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0,03
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
55/55Handhabbare Dateigrößen — 0/9 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes9.084
source_files_sampled9
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

419GitHub-Sterne
17Mitwirkende
1Commits, letzte 12 Monate
1Tage seit letztem Push
22Releases
1Bus-Faktor
2offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'academic' points at a different repository (https://github.com/GetRD/academic-file-converter); excluded from ecosystem scoring

Weitere Details

Stern- und Fork-Verlauf 0 ★ / 106 ⇿
0Sterne
106Forks
21Releases

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.

02040608010012010532018-102022-072026-03
Major 0Minor 7Patch 14

Jeder Punkt umfasst 7 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 · 2026-07-23 07:48 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
3CI-Tests1 out of 3 merged PRs checked by a CI test -- score normalized to 3
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/29 approved changesets -- score normalized to 0
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
k. A.Packagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
7SASTSAST tool detected but not run on all commits
0Security-Policysecurity policy file not detected
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities81 existing vulnerabilities detected
Direkte Abhängigkeiten 4
RegistryPaketVersionsvorgabeManifest
PyPIbibtexparser~1.4pyproject.toml
PyPIruamel.yaml~0.17pyproject.toml
PyPInbconvert^7.10.0pyproject.toml
PyPIpyyaml^6.0.1pyproject.toml
Alle Abhängigkeiten 117

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 4 direkte und 113 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIbibtexparser1.4.1direkt
PyPInbconvert7.10.0direkt
PyPIpyyaml6.0.1direkt
PyPIruamel-yaml0.17.40direkt
PyPIanyio4.0.0indirekt
PyPIappnope0.1.3indirekt
PyPIargon2-cffi23.1.0indirekt
PyPIargon2-cffi-bindings21.2.0indirekt
PyPIarrow1.3.0indirekt
PyPIasttokens2.4.1indirekt
PyPIasync-lru2.0.4indirekt
PyPIattrs23.1.0indirekt
PyPIbabel2.13.1indirekt
PyPIbeautifulsoup44.12.2indirekt
PyPIblack23.10.1indirekt
PyPIbleach6.1.0indirekt
PyPIcertifi2023.7.22indirekt
PyPIcffi1.16.0indirekt
PyPIcharset-normalizer3.3.2indirekt
PyPIclick8.1.7indirekt
PyPIcolorama0.4.6indirekt
PyPIcomm0.1.4indirekt
PyPIdebugpy1.8.0indirekt
PyPIdecorator5.1.1indirekt
PyPIdefusedxml0.7.1indirekt
PyPIexecuting2.0.1indirekt
PyPIfastjsonschema2.18.1indirekt
PyPIflake86.1.0indirekt
PyPIfqdn1.4.0indirekt
PyPIidna3.4indirekt
PyPIiniconfig2.0.0indirekt
PyPIipykernel6.26.0indirekt
PyPIipython8.17.2indirekt
PyPIipython-genutils0.2.0indirekt
PyPIipywidgets8.1.1indirekt
PyPIisoduration20.11.0indirekt
PyPIisort5.12.0indirekt
PyPIjedi0.19.1indirekt
PyPIjinja23.1.2indirekt
PyPIjson50.9.14indirekt
PyPIjsonpointer2.4indirekt
PyPIjsonschema4.19.2indirekt
PyPIjsonschema-specifications2023.7.1indirekt
PyPIjupyter1.0.0indirekt
PyPIjupyter-client8.5.0indirekt
PyPIjupyter-console6.6.3indirekt
PyPIjupyter-core5.5.0indirekt
PyPIjupyter-events0.8.0indirekt
PyPIjupyter-lsp2.2.0indirekt
PyPIjupyter-server2.9.1indirekt
PyPIjupyter-server-terminals0.4.4indirekt
PyPIjupyterlab4.0.8indirekt
PyPIjupyterlab-pygments0.2.2indirekt
PyPIjupyterlab-server2.25.0indirekt
PyPIjupyterlab-widgets3.0.9indirekt
PyPImarkupsafe2.1.3indirekt
PyPImatplotlib-inline0.1.6indirekt
PyPImccabe0.7.0indirekt
PyPImistune3.0.2indirekt
PyPImypy-extensions1.0.0indirekt
PyPInbclient0.8.0indirekt
PyPInbformat5.9.2indirekt
PyPInest-asyncio1.5.8indirekt
PyPInodeenv1.8.0indirekt
PyPInotebook7.0.6indirekt
PyPInotebook-shim0.2.3indirekt
PyPIoverrides7.4.0indirekt
PyPIpackaging23.2indirekt
PyPIpandocfilters1.5.0indirekt
PyPIparso0.8.3indirekt
PyPIpathspec0.11.2indirekt
PyPIpexpect4.8.0indirekt
PyPIplatformdirs3.11.0indirekt
PyPIpluggy1.3.0indirekt
PyPIprometheus-client0.18.0indirekt
PyPIprompt-toolkit3.0.39indirekt
PyPIpsutil5.9.6indirekt
PyPIptyprocess0.7.0indirekt
PyPIpure-eval0.2.2indirekt
PyPIpycodestyle2.11.1indirekt
PyPIpycparser2.21indirekt
PyPIpyflakes3.1.0indirekt
PyPIpygments2.16.1indirekt
PyPIpyparsing3.1.1indirekt
PyPIpyright1.1.334indirekt
PyPIpytest7.4.3indirekt
PyPIpython-dateutil2.8.2indirekt
PyPIpython-json-logger2.0.7indirekt
PyPIpywin32306indirekt
PyPIpywinpty2.0.12indirekt
PyPIpyzmq25.1.1indirekt
PyPIqtconsole5.4.4indirekt
PyPIqtpy2.4.1indirekt
PyPIreferencing0.30.2indirekt
PyPIrequests2.31.0indirekt
PyPIrfc3339-validator0.1.4indirekt
PyPIrfc3986-validator0.1.1indirekt
PyPIrpds-py0.12.0indirekt
PyPIruamel-yaml-clib0.2.8indirekt
PyPIsend2trash1.8.2indirekt
PyPIsetuptools68.2.2indirekt
PyPIsix1.16.0indirekt
PyPIsniffio1.3.0indirekt
PyPIsoupsieve2.5indirekt
PyPIstack-data0.6.3indirekt
PyPIterminado0.17.1indirekt
PyPItinycss21.2.1indirekt
PyPItornado6.3.3indirekt
PyPItraitlets5.13.0indirekt
PyPItypes-python-dateutil2.8.19.14indirekt
PyPIuri-template1.3.0indirekt
PyPIurllib32.6.3indirekt
PyPIwcwidth0.2.9indirekt
PyPIwebcolors1.13indirekt
PyPIwebencodings0.5.1indirekt
PyPIwebsocket-client1.6.4indirekt
PyPIwidgetsnbextension4.0.9indirekt
Abhängigkeits-Advisories 0

Die Installation von pypi:academic@0.11.2 zieht 34 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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          "body": "Path().glob(\"docker-compose.yml\") returns a generator that is always a\r\ntrue value in if conditions (even if the list that it returns is emtpy).\r\nFix this by using `Path(\"docker-compose.yml\").exists()`.",
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          "headline": "fix: check for docker-compose (#71)",
          "author_name": "Alexander Richardson",
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          "headline": "Create FUNDING.yml",
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          "headline": "fix: self vars undeclared during FM init",
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          "body": "May help prevent reports of further issues similar to #54",
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          "headline": "chore: explicitly set required version of bibtexparser",
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          "headline": "test: add a simple unit test for Bibtex import",
          "author_name": "George Cushen",
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          "oid": "a9c25b01859b0e00b9af57e0474df4f6225c5317",
          "body": "Also:\n- Refactor Import Assets code to its own module\n- Raise AcademicError on error to fail tests\n- Add Dry Run CLI option for testing\n- Fix deprecation warning for regex for consecutive hyphens\n\nFix #53",
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          "headline": "fix: logger initialisation",
          "author_name": "George Cushen",
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                "key": "fuzzing",
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                "key": "vulnerabilities",
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            ]
          },
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            "notes": [
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                "code": "advisories_scope_published",
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            "value": 100,
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            "components": [
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                "key": "direct_dependencies_free_of_known_advisories",
                "name": "Direct dependencies free of known advisories",
                "detail": "no direct dependency carries a known advisory",
                "points": 35,
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                "details": [
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                    "params": {}
                  }
                ],
                "max_points": 35
              },
              {
                "key": "indirect_dependencies_free_of_known_advisories",
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              {
                "key": "no_advisories_left_outstanding",
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          },
          {
            "key": "malicious_dependencies",
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              "packages": [],
              "red_flag": false,
              "assessed_packages": 34,
              "malicious_packages": 0,
              "direct_malicious_packages": 0,
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              "installable_malicious_packages": 0
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                "name": "No dependency reported as a malicious package",
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                "max_points": 100
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            ]
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          {
            "key": "high_risk_jurisdiction_exposure",
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              {
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              "review_only_matches": 0,
              "assessed_self_published_locations": 18
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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",
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        "value": 54,
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                "details": [],
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              },
              {
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                "status": "met",
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                      "sampled": 97
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          {
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            "notes": [],
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            "inputs": {
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              "has_tests": true,
              "lockfiles": [
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              "typed_language": false,
              "bootstrap_files": [
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              "agent_commit_share": 0,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0.03
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                "key": "one_command_bootstrap",
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              {
                "key": "automated_tests",
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              },
              {
                "key": "static_type_checking",
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                "detail": null,
                "points": 0,
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                "details": [],
                "max_points": 11
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                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
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                "detail": "no agent-authored commits among the last 100",
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                "status": "missed",
                "details": [
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                    "code": "no_agent_authored_commits",
                    "params": {
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                  }
                ],
                "max_points": 10
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              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
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                "points": 8,
                "status": "met",
                "details": [
                  {
                    "code": "dependency_bot_commits",
                    "params": {
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                      "sampled": 100
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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": [],
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              "largest_source_bytes": 9084,
              "source_files_sampled": 9,
              "oversized_source_files": 0
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "Python without a type-check config",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_typecheck_config_language",
                    "params": {
                      "language": "Python"
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                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "0/9 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
                  {
                    "code": "oversized_source_files",
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                      "oversized": 0
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
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                "notebooks"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "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": "notebooks",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "notebooks"
                    }
                  }
                ],
                "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": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "pypi package 'academic' points at a different repository (https://github.com/GetRD/academic-file-converter); excluded from ecosystem scoring"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-23T07:49:02.609296Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/h/HugoBlox/academic-file-converter.svg",
  "full_name": "HugoBlox/academic-file-converter",
  "license_state": "standard",
  "license_spdx": "MIT"
}

Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.

Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte StatistikenPyPI.