Öffentliches RegisterGut
Software-GesundheitsberichtSchema 0.17.0 · Metriken 1.13.0 · 2026-07-21 04:34 UTC
Unstructured-IO / unstructured-inference
Unstructured-IO/unstructured-inference erreicht einen Gesundheitsindex von 72 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (84/100) ab, am schwächsten bei Security (46/100). Zuletzt heute aktualisiert. 4 Mitwirkende tragen den Großteil der jüngsten Arbeit.
72
gesamt / 100Software-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.
72
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)
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
UnstructuredOrganisation
1.497 Follower45 öffentliche Reposseit Juni 2022
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht | Tags |
|---|---|---|---|---|---|---|
| PyPI | unstructured_inference | 1.6.13 | - | 124 | vor 39 Tagen | cvhtmlnlppdfxmlparsingpreprocessing |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
84
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 0 Tagen |
| 14.5/36 | Commit-Rhythmus — 21/52 Wochen mit Commits |
| 14.8/18 | Commit-Volumen — 44 Commits im letzten Jahr |
| 0/10 | OpenSSF Scorecard: Maintained — keine Daten |
Verwendete Eingangsdaten
| commits_last_year | 44 |
| human_commit_share | — |
| days_since_last_push | 0 |
| active_weeks_last_year | 21 |
Release-Disziplin
100Exzellent
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 100 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 39 Tagen |
| 27/27 | Release-Rhythmus — ein Release etwa alle 7,7 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 100 |
| latest_release_tag | v1.6.13 |
| releases_from_tags | nein |
| days_since_latest_release | 39 |
| mean_days_between_releases | 7,7 |
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
72
Popularität & Verbreitung
60Mittel
Wie die Bewertung erfolgt
| 37.6/60 | Stars — 209 Stars |
| 15.5/25 | Forks — 73 Forks |
| 6.7/15 | Watcher — 17 Watcher |
Verwendete Eingangsdaten
| forks | 73 |
| stars | 209 |
| watchers | 17 |
| growth_state | organic |
| growth_factor_pct | 100 |
Community-Gesundheit
85Exzellent
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (Apache-2.0) |
| 18/18 | CONTRIBUTING-Leitfaden |
| 13.5/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 0/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | ja |
| has_pull_request_template | nein |
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
80
Wie die Bewertung erfolgt
| 43.2/54 | Bus-Faktor — 4 Beitragende decken die Hälfte aller Commits ab |
| 16.9/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 25 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 39 Beitragende |
| 0/10 | OpenSSF Scorecard: Contributors — keine Daten |
Verwendete Eingangsdaten
| bus_factor | 4 |
| contributors_sampled | 39 |
| top_contributor_share | 0,247 |
Wie die Bewertung erfolgt
| 30.5/46.8 | Issue-Lösungsquote — 65 % der Issues geschlossen |
| 28/38.3 | PR-Annahme — 308/420 entschiedene PRs gemergt |
| 0/15 | OpenSSF Scorecard: Code-Review — keine Daten |
Verwendete Eingangsdaten
| merged_prs | 308 |
| open_issues | 25 |
| closed_issues | 47 |
| issue_closed_ratio | 0,653 |
| closed_unmerged_prs | 112 |
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 22.8/25 | Reichweite des Inhabers — 1.497 Follower von Unstructured-IO |
| 20.2/25 | Kontohistorie — 45 öffentliche Repos, Kontoalter ca. 4 Jahre |
Verwendete Eingangsdaten
| followers | 1.497 |
| owner_type | Organization |
| is_verified | — |
| owner_login | Unstructured-IO |
| public_repos | 45 |
| account_age_days | 1.483 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 39 Tagen |
| 20/20 | Versionshistorie — 124 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | unstructured_inference |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 39 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
71
Engineering-Praktiken
92Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 5 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 0/20 | OpenSSF Scorecard: CI-Tests — keine Daten |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
40Gefährdet
Wie die Bewertung erfolgt
| 30/30 | README |
| 0/25 | Dokumentationsverzeichnis |
| 0/15 | Dokumentations-/Homepage-Site |
| 0/10 | Repository-Beschreibung |
| 0/10 | Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | — |
| has_wiki | ja |
| homepage | — |
| has_readme | ja |
| has_docs_dir | nein |
| has_description | nein |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
46
Sicherheitslage
33Gefährdet
Wie die Bewertung erfolgt
| 0/30 | Sicherheitsrichtlinie (SECURITY.md) |
| 25/25 | Dependabot-Konfiguration |
| 0/25 | Lockfiles für Abhängigkeiten — veröffentlichte Bibliothek — Lockfiles sind Sache der Anwendung, hier nicht erwartet |
| 0/20 | CodeQL-Workflow |
Verwendete Eingangsdaten
| source | file_signals |
| lockfiles | uv.lock |
| manifests | pyproject.toml |
| has_codeql_workflow | nein |
| has_security_policy | nein |
| has_dependabot_config | ja |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lockfiles für Abhängigkeiten. Die verbleibenden Gewichte wurden renormalisiert.
Abhängigkeits-Advisories
100Exzellent
Wie die Bewertung erfolgt
| 35/35 | Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory |
| 25/25 | Indirekte Abhängigkeiten ohne bekannte Advisories — keine indirekte Abhängigkeit trägt ein bekanntes Advisory |
| 0/40 | Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum |
Verwendete Eingangsdaten
| source | osv |
| advisories | 0 |
| affected_packages | 0 |
| assessed_packages | 55 |
| unassessed_packages | 0 |
| affected_by_severity | none |
| direct_affected_packages | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. Abgeglichen wurde die Laufzeit-Abhängigkeitshülle von pypi:unstructured_inference@1.6.13 — das, was die Installation des veröffentlichten Pakets nach sich zieht — mit 55 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.
47
AI-Agentenkontext & Leitlinien
1Kritisch
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 0/40 | Lesbare Commit-Historie — keine Daten |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| 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
| 18/18 | Bootstrap mit einem Befehl — Makefile |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 10/10 | Reproduzierbare Umgebung — Dockerfile, lockfile |
| 0/10 | Belegte Agentenpraxis — keine Daten |
| 5/8 | Automatisierte Wartung — Abhängigkeits-Automatisierung konfiguriert, in den erfassten Commits nicht beobachtet |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — keine Daten |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | uv.lock |
| has_dockerfile | ja |
| typed_language | nein |
| bootstrap_files | Makefile |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis. Die verbleibenden Gewichte wurden renormalisiert.
AI-Code-Lesbarkeit für Modelle
55Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 55/55 | Handhabbare Dateigrößen — 0/41 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 59.969 |
| source_files_sampled | 41 |
| oversized_source_files | 0 |
Maschinenlesbare AI-Schnittstellen
40Gefährdet
Wie die Bewertung erfolgt
| 0/40 | API-Schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP-Server |
| 40/40 | Lauffähige Beispiele — examples |
Verwendete Eingangsdaten
| example_dirs | examples |
| has_mcp_signal | nein |
| api_schema_files | — |
Eckdaten
209GitHub-Sterne
39Mitwirkende
44Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
4Bus-Faktor
25offene Issues
PyPIPaket-Ökosysteme
Warnungen zur Datenerhebung
- OpenSSF Scorecard did not return a usable result (2026/07/21 04:33:47 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks
Weitere Details
Stern- und Fork-Verlauf 209 ★ / 0 ⇿
209Sterne
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.
Jeder Punkt umfasst 4 Tage.
Direkte Abhängigkeiten 15
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | huggingface-hub | >=0.22.0 | pyproject.toml |
| PyPI | numpy | >=1.26.0 | pyproject.toml |
| PyPI | opencv-python | >=4.13.0.90 | pyproject.toml |
| PyPI | onnx | >=1.20.1 | pyproject.toml |
| PyPI | onnxruntime | >=1.25.0 | pyproject.toml |
| PyPI | matplotlib | >=3.10.8 | pyproject.toml |
| PyPI | torch | >=2.10.0 | pyproject.toml |
| PyPI | timm | >=1.0.24 | pyproject.toml |
| PyPI | transformers | >=4.25.1 | pyproject.toml |
| PyPI | accelerate | >=1.12.0 | pyproject.toml |
| PyPI | rapidfuzz | >=3.14.3 | pyproject.toml |
| PyPI | pandas | >=1.5.0 | pyproject.toml |
| PyPI | scipy | >=1.17.0 | pyproject.toml |
| PyPI | pypdfium2 | >=5.0.0 | pyproject.toml |
| PyPI | pdfminer.six | >=20231228 | pyproject.toml |
Alle Abhängigkeiten 194
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 15 direkte und 179 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | accelerate | 1.13.0 | direkt |
| PyPI | huggingface-hub | 1.10.1 | direkt |
| PyPI | matplotlib | 3.10.8 | direkt |
| PyPI | numpy | 2.4.4 | direkt |
| PyPI | onnx | 1.21.0 | direkt |
| PyPI | onnxruntime | 1.25.0 | direkt |
| PyPI | opencv-python | 4.13.0.92 | direkt |
| PyPI | pandas | 3.0.2 | direkt |
| PyPI | pdfminer-six | 20260107 | direkt |
| PyPI | pypdfium2 | 5.7.0 | direkt |
| PyPI | rapidfuzz | 3.14.5 | direkt |
| PyPI | scipy | 1.17.1 | direkt |
| PyPI | timm | 1.0.26 | direkt |
| PyPI | torch | 2.11.0 | direkt |
| PyPI | transformers | 5.5.3 | direkt |
| PyPI | annotated-doc | 0.0.4 | indirekt |
| PyPI | anyio | 4.13.0 | indirekt |
| PyPI | appnope | 0.1.4 | indirekt |
| PyPI | argon2-cffi | 25.1.0 | indirekt |
| PyPI | argon2-cffi-bindings | 25.1.0 | indirekt |
| PyPI | arrow | 1.4.0 | indirekt |
| PyPI | asttokens | 3.0.1 | indirekt |
| PyPI | async-lru | 2.3.0 | indirekt |
| PyPI | attrs | 26.1.0 | indirekt |
| PyPI | babel | 2.18.0 | indirekt |
| PyPI | backports-tarfile | 1.2.0 | indirekt |
| PyPI | beautifulsoup4 | 4.14.3 | indirekt |
| PyPI | bleach | 6.3.0 | indirekt |
| PyPI | certifi | 2026.2.25 | indirekt |
| PyPI | cffi | 2.0.0 | indirekt |
| PyPI | charset-normalizer | 3.4.7 | indirekt |
| PyPI | click | 8.3.2 | indirekt |
| PyPI | colorama | 0.4.6 | indirekt |
| PyPI | comm | 0.2.3 | indirekt |
| PyPI | contourpy | 1.3.3 | indirekt |
| PyPI | coverage | 7.13.5 | indirekt |
| PyPI | cryptography | 46.0.7 | indirekt |
| PyPI | cuda-bindings | 13.2.0 | indirekt |
| PyPI | cuda-pathfinder | 1.5.2 | indirekt |
| PyPI | cuda-toolkit | 13.0.2 | indirekt |
| PyPI | cycler | 0.12.1 | indirekt |
| PyPI | debugpy | 1.8.20 | indirekt |
| PyPI | decorator | 5.2.1 | indirekt |
| PyPI | defusedxml | 0.7.1 | indirekt |
| PyPI | docutils | 0.22.4 | indirekt |
| PyPI | execnet | 2.1.2 | indirekt |
| PyPI | executing | 2.2.1 | indirekt |
| PyPI | fastjsonschema | 2.21.2 | indirekt |
| PyPI | filelock | 3.25.2 | indirekt |
| PyPI | flatbuffers | 25.12.19 | indirekt |
| PyPI | fonttools | 4.62.1 | indirekt |
| PyPI | fqdn | 1.5.1 | indirekt |
| PyPI | fsspec | 2026.3.0 | indirekt |
| PyPI | h11 | 0.16.0 | indirekt |
| PyPI | hatchling | — | indirekt |
| PyPI | hf-xet | 1.4.3 | indirekt |
| PyPI | httpcore | 1.0.9 | indirekt |
| PyPI | httpx | 0.28.1 | indirekt |
| PyPI | id | 1.6.1 | indirekt |
| PyPI | idna | 3.11 | indirekt |
| PyPI | importlib-metadata | 9.0.0 | indirekt |
| PyPI | iniconfig | 2.3.0 | indirekt |
| PyPI | ipykernel | 7.2.0 | indirekt |
| PyPI | ipython | 9.12.0 | indirekt |
| PyPI | ipython-pygments-lexers | 1.1.1 | indirekt |
| PyPI | ipywidgets | 8.1.8 | indirekt |
| PyPI | isoduration | 20.11.0 | indirekt |
| PyPI | jaraco-classes | 3.4.0 | indirekt |
| PyPI | jaraco-context | 6.1.2 | indirekt |
| PyPI | jaraco-functools | 4.4.0 | indirekt |
| PyPI | jedi | 0.19.2 | indirekt |
| PyPI | jeepney | 0.9.0 | indirekt |
| PyPI | jinja2 | 3.1.6 | indirekt |
| PyPI | json5 | 0.14.0 | indirekt |
| PyPI | jsonpointer | 3.1.1 | indirekt |
| PyPI | jsonschema | 4.26.0 | indirekt |
| PyPI | jsonschema-specifications | 2025.9.1 | indirekt |
| PyPI | jupyter | 1.1.1 | indirekt |
| PyPI | jupyter-client | 8.8.0 | indirekt |
| PyPI | jupyter-console | 6.6.3 | indirekt |
| PyPI | jupyter-core | 5.9.1 | indirekt |
| PyPI | jupyter-events | 0.12.0 | indirekt |
| PyPI | jupyter-lsp | 2.3.1 | indirekt |
| PyPI | jupyter-server | 2.17.0 | indirekt |
| PyPI | jupyter-server-terminals | 0.5.4 | indirekt |
| PyPI | jupyterlab | 4.5.6 | indirekt |
| PyPI | jupyterlab-pygments | 0.3.0 | indirekt |
| PyPI | jupyterlab-server | 2.28.0 | indirekt |
| PyPI | jupyterlab-widgets | 3.0.16 | indirekt |
| PyPI | keyring | 25.7.0 | indirekt |
| PyPI | kiwisolver | 1.5.0 | indirekt |
| PyPI | lark | 1.3.1 | indirekt |
| PyPI | markdown-it-py | 4.0.0 | indirekt |
| PyPI | markupsafe | 3.0.3 | indirekt |
| PyPI | matplotlib-inline | 0.2.1 | indirekt |
| PyPI | mdurl | 0.1.2 | indirekt |
| PyPI | mistune | 3.2.0 | indirekt |
| PyPI | ml-dtypes | 0.5.4 | indirekt |
| PyPI | more-itertools | 11.0.2 | indirekt |
| PyPI | mpmath | 1.3.0 | indirekt |
| PyPI | nbclient | 0.10.4 | indirekt |
| PyPI | nbconvert | 7.17.1 | indirekt |
| PyPI | nbformat | 5.10.4 | indirekt |
| PyPI | nest-asyncio | 1.6.0 | indirekt |
| PyPI | networkx | 3.6.1 | indirekt |
| PyPI | nh3 | 0.3.4 | indirekt |
| PyPI | nltk | — | indirekt |
| PyPI | notebook | 7.5.5 | indirekt |
| PyPI | notebook-shim | 0.2.4 | indirekt |
| PyPI | nvidia-cublas | 13.1.0.3 | indirekt |
| PyPI | nvidia-cuda-cupti | 13.0.85 | indirekt |
| PyPI | nvidia-cuda-nvrtc | 13.0.88 | indirekt |
| PyPI | nvidia-cuda-runtime | 13.0.96 | indirekt |
| PyPI | nvidia-cudnn-cu13 | 9.19.0.56 | indirekt |
| PyPI | nvidia-cufft | 12.0.0.61 | indirekt |
| PyPI | nvidia-cufile | 1.15.1.6 | indirekt |
| PyPI | nvidia-curand | 10.4.0.35 | indirekt |
| PyPI | nvidia-cusolver | 12.0.4.66 | indirekt |
| PyPI | nvidia-cusparse | 12.6.3.3 | indirekt |
| PyPI | nvidia-cusparselt-cu13 | 0.8.0 | indirekt |
| PyPI | nvidia-nccl-cu13 | 2.28.9 | indirekt |
| PyPI | nvidia-nvjitlink | 13.0.88 | indirekt |
| PyPI | nvidia-nvshmem-cu13 | 3.4.5 | indirekt |
| PyPI | nvidia-nvtx | 13.0.85 | indirekt |
| PyPI | overrides | 7.7.0 | indirekt |
| PyPI | packaging | 26.0 | indirekt |
| PyPI | pandocfilters | 1.5.1 | indirekt |
| PyPI | parso | 0.8.6 | indirekt |
| PyPI | pdf2image | 1.17.0 | indirekt |
| PyPI | pexpect | 4.9.0 | indirekt |
| PyPI | pillow | 12.2.0 | indirekt |
| PyPI | platformdirs | 4.9.6 | indirekt |
| PyPI | pluggy | 1.6.0 | indirekt |
| PyPI | prometheus-client | 0.24.1 | indirekt |
| PyPI | prompt-toolkit | 3.0.52 | indirekt |
| PyPI | protobuf | 7.34.1 | indirekt |
| PyPI | psutil | 7.2.2 | indirekt |
| PyPI | ptyprocess | 0.7.0 | indirekt |
| PyPI | pure-eval | 0.2.3 | indirekt |
| PyPI | pycparser | 3.0 | indirekt |
| PyPI | pygments | 2.20.0 | indirekt |
| PyPI | pyparsing | 3.3.2 | indirekt |
| PyPI | pytest | 9.0.3 | indirekt |
| PyPI | pytest-cov | 7.1.0 | indirekt |
| PyPI | pytest-mock | 3.15.1 | indirekt |
| PyPI | pytest-xdist | 3.8.0 | indirekt |
| PyPI | python-dateutil | 2.9.0.post0 | indirekt |
| PyPI | python-json-logger | 4.1.0 | indirekt |
| PyPI | pywin32-ctypes | 0.2.3 | indirekt |
| PyPI | pywinpty | 3.0.3 | indirekt |
| PyPI | pyyaml | 6.0.3 | indirekt |
| PyPI | pyzmq | 27.1.0 | indirekt |
| PyPI | readme-renderer | 44.0 | indirekt |
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| PyPI | zipp | 3.23.1 | indirekt |
Abhängigkeits-Advisories 0
Die Installation von pypi:unstructured_inference@1.6.13 zieht 55 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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}
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"name": "Dependency advisories",
"note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:unstructured_inference@1.6.13 runtime dependency closure — what installing the published package pulls in — 55 packages. Reachability is not analyzed.",
"notes": [
{
"code": "excluded_no_data",
"params": {
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]
}
},
{
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},
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},
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}
],
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"unassessed_packages": 0,
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"direct_affected_packages": 0
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}
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"params": {}
}
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{
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"points": 0,
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"params": {}
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],
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"note": null,
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"value": 100,
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"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": 55,
"malicious_packages": 0,
"direct_malicious_packages": 0,
"withdrawn_malicious_packages": 0,
"installable_malicious_packages": 0
},
"components": [
{
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"points": 100,
"status": "met",
"details": [
{
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"params": {}
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],
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}
]
},
{
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"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
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"params": {}
}
],
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"inputs": {
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"policy_countries": [
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"Iran",
"North Korea"
],
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"assessed_self_published_locations": 5
},
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{
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"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"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": 47,
"weight": 0,
"metrics": [
{
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"band": "critical",
"name": "Agent context & guidance",
"note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
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}
},
{
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],
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"agent_instruction_max_bytes": null
},
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{
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"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
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{
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"points": 0,
"status": "missed",
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"max_points": 15
},
{
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"points": 0,
"status": "excluded",
"details": [
{
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"params": {}
}
],
"max_points": 40
}
]
},
{
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"notes": [
{
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},
{
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],
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"has_tests": true,
"lockfiles": [
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"typed_language": false,
"bootstrap_files": [
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"typecheck_configs": [],
"agent_commit_share": null,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0
},
"components": [
{
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"detail": "Makefile",
"points": 18,
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"details": [
{
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}
],
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},
{
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"detail": null,
"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
"name": "Lint / format config",
"detail": null,
"points": 11,
"status": "met",
"details": [],
"max_points": 11
},
{
"key": "static_type_checking",
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"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "reproducible_environment",
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"detail": "Dockerfile, lockfile",
"points": 10,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "Dockerfile, lockfile"
}
}
],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no data",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "dependency automation configured, none observed in the sampled commits",
"points": 5,
"status": "partial",
"details": [
{
"code": "dependency_bot_config_only",
"params": {}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "OpenSSF Scorecard unavailable",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"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": 59969,
"source_files_sampled": 41,
"oversized_source_files": 0
},
"components": [
{
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"points": 0,
"status": "missed",
"details": [
{
"code": "no_typecheck_config_language",
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}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
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"detail": "0/41 source files over 60KB",
"points": 55,
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{
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"sampled": 41,
"oversized": 0
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}
],
"max_points": 55
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]
},
{
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"note": null,
"notes": [],
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"detail": null,
"points": 0,
"status": "missed",
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},
{
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"detail": null,
"points": 0,
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"details": [],
"max_points": 20
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{
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"params": {
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}
],
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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."
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],
"metrics_version": "1.13.0"
},
"warnings": [
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],
"report_type": "repository",
"generated_at": "2026-07-21T04:34:19.275629Z",
"schema_version": "0.17.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/u/Unstructured-IO/unstructured-inference.svg",
"full_name": "Unstructured-IO/unstructured-inference",
"license_state": "standard",
"license_spdx": "Apache-2.0"
}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.17.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.