Öffentliches RegisterGut
Software-GesundheitsberichtSchema 0.15.0 · Metriken 1.13.0 · 2026-07-19 16:13 UTC
datalab-to / surya
OCR, layout analysis, reading order, table recognition in 90+ languages
datalab-to/surya erreicht einen Gesundheitsindex von 71 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (90/100) ab, am schwächsten bei AI Readiness (35/100). Zuletzt vor 2 Tagen aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.
71
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.
71
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
DatalabOrganisation
812 Follower12 öffentliche Reposseit Juli 2024
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 | surya-ocr | 0.22.0 | 893.385 | 93 | vor 2 Tagen | ocrpdftablestext-detectiontext-recognition |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
90
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 2 Tagen |
| 18.7/36 | Commit-Rhythmus — 27/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 237 Commits im letzten Jahr |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10 |
Verwendete Eingangsdaten
| commits_last_year | 237 |
| human_commit_share | — |
| days_since_last_push | 2 |
| active_weeks_last_year | 27 |
Release-Disziplin
100Exzellent
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 86 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 2 Tagen |
| 27/27 | Release-Rhythmus — ein Release etwa alle 34,7 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 86 |
| latest_release_tag | v0.22.0 |
| releases_from_tags | nein |
| days_since_latest_release | 2 |
| mean_days_between_releases | 34,7 |
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?
81
Popularität & Verbreitung
97Exzellent
Wie die Bewertung erfolgt
| 60/60 | Stars — 21.119 Stars |
| 25/25 | Forks — 1.518 Forks |
| 11.7/15 | Watcher — 128 Watcher |
Verwendete Eingangsdaten
| forks | 1.518 |
| stars | 21.119 |
| watchers | 128 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
50Mittel
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (Apache-2.0) |
| 0/18 | CONTRIBUTING-Leitfaden |
| 0/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 0/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | nein |
| has_issue_template | nein |
| has_code_of_conduct | nein |
| has_pull_request_template | nein |
Ökosystem-Verbreitung (Downloads)
99Exzellent
Wie die Bewertung erfolgt
| 79.3/80 | Downloads pro Monat — 893.385 Downloads/Monat über pypi |
| 0/20 | Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen |
Verwendete Eingangsdaten
| packages | surya-ocr |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 893.385 |
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?
61
Maintainer-Resilienz (Bus-Faktor)
36Gefährdet
Wie die Bewertung erfolgt
| 9/54 | Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab |
| 7.9/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 65 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 18 Beitragende |
| 6/10 | OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6 |
Verwendete Eingangsdaten
| bus_factor | 1 |
| contributors_sampled | 18 |
| top_contributor_share | 0,649 |
Wie die Bewertung erfolgt
| 22.5/46.8 | Issue-Lösungsquote — 48 % der Issues geschlossen |
| 34/38.3 | PR-Annahme — 190/214 entschiedene PRs gemergt |
| 0/15 | OpenSSF Scorecard: Code-Review — Found 0/12 approved changesets -- score normalized to 0 |
Verwendete Eingangsdaten
| merged_prs | 190 |
| open_issues | 152 |
| closed_issues | 141 |
| issue_closed_ratio | 0,481 |
| closed_unmerged_prs | 24 |
Eigentümerschaft & Trägerschaft
63Mittel
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 20.9/25 | Reichweite des Inhabers — 812 Follower von datalab-to |
| 12.1/25 | Kontohistorie — 12 öffentliche Repos, Kontoalter ca. 1 Jahre |
Verwendete Eingangsdaten
| followers | 812 |
| owner_type | Organization |
| is_verified | — |
| owner_login | datalab-to |
| public_repos | 12 |
| account_age_days | 723 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 2 Tagen |
| 20/20 | Versionshistorie — 93 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | surya-ocr |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 2 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
82
Engineering-Praktiken
94Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 4 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 10 out of 10 merged PRs checked by a CI test -- score normalized to 10 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
65Mittel
Wie die Bewertung erfolgt
| 30/30 | README |
| 0/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — https://www.datalab.to |
| 10/10 | Repository-Beschreibung |
| 0/10 | Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | — |
| has_wiki | ja |
| homepage | https://www.datalab.to |
| has_readme | ja |
| has_docs_dir | nein |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
36
Sicherheitslage
36Gefährdet
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 2.2/7.5 | Branch-Protection — branch protection is not maximal on development and all release branches |
| 2.5/2.5 | CI-Tests — 10 out of 10 merged PRs checked by a CI test -- score normalized to 10 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 0/7.5 | Code-Review — Found 0/12 approved changesets -- score normalized to 0 |
| 1.5/2.5 | Contributors — project has 2 contributing companies or organizations -- score normalized to 6 |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 0/7.5 | Dependency-Update-Tool — no update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Lizenz — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10 |
| 0/5 | Packaging — keine Daten |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 0/7.5 | Vulnerabilities — 56 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 3,6 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): packaging, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
35
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
| 0/18 | Bootstrap mit einem Befehl |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 10/10 | Reproduzierbare Umgebung — lockfile |
| 0/10 | Belegte Agentenpraxis — keine Daten |
| 0/8 | Automatisierte Wartung — keine Daten |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | uv.lock |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | — |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | — |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis, Automatisierte Wartung. Die verbleibenden Gewichte wurden renormalisiert.
AI-Code-Lesbarkeit für Modelle
55Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 55/55 | Handhabbare Dateigrößen — 0/109 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 53.026 |
| source_files_sampled | 109 |
| oversized_source_files | 0 |
Eckdaten
21.119GitHub-Sterne
18Mitwirkende
237Commits, letzte 12 Monate
2Tage seit letztem Push
86Releases
1Bus-Faktor
152offene Issues
PyPIPaket-Ökosysteme
Weitere Details
OpenSSF Scorecard 3.6 / 10
3.6Gesamtwert
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).
| 10 | Binary-Artifacts | no binaries found in the repo |
| 3 | Branch-Protection | branch protection is not maximal on development and all release branches |
| 10 | CI-Tests | 10 out of 10 merged PRs checked by a CI test -- score normalized to 10 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 0 | Code-Review | Found 0/12 approved changesets -- score normalized to 0 |
| 6 | Contributors | project has 2 contributing companies or organizations -- score normalized to 6 |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 0 | Dependency-Update-Tool | no update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10 |
| k. A. | Packaging | packaging workflow not detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 0 | Security-Policy | security policy file not detected |
| k. A. | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 0 | Vulnerabilities | 56 existing vulnerabilities detected |
Direkte Abhängigkeiten 17
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | transformers | >=5.12.1 | pyproject.toml |
| PyPI | torch | >=2.7.0,<3 | pyproject.toml |
| PyPI | torchvision | >=0.20.0,<1 | pyproject.toml |
| PyPI | pydantic | >=2.5.3,<3 | pyproject.toml |
| PyPI | pydantic-settings | >=2.1.0,<3 | pyproject.toml |
| PyPI | python-dotenv | >=1.0.0,<2 | pyproject.toml |
| PyPI | pillow | >=10.2.0,<11 | pyproject.toml |
| PyPI | pypdfium2 | >=5.10.1,<6 | pyproject.toml |
| PyPI | filetype | >=1.2.0,<2 | pyproject.toml |
| PyPI | click | >=8.1.8,<9 | pyproject.toml |
| PyPI | platformdirs | >=4.3.6,<5 | pyproject.toml |
| PyPI | opencv-python-headless | ==4.11.0.86 | pyproject.toml |
| PyPI | openai | >=1.55.0,<3 | pyproject.toml |
| PyPI | httpx | >=0.27.0,<1 | pyproject.toml |
| PyPI | huggingface-hub | >=1.5.0,<2 | pyproject.toml |
| PyPI | filelock | >=3.16.0,<4 | pyproject.toml |
| PyPI | beautifulsoup4 | >=4.12.0,<5 | pyproject.toml |
Alle Abhängigkeiten 212
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 17 direkte und 195 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | beautifulsoup4 | 4.14.3 | direkt |
| PyPI | click | 8.4.2 | direkt |
| PyPI | filelock | 3.29.0 | direkt |
| PyPI | filetype | 1.2.0 | direkt |
| PyPI | httpx | 0.27.2 | direkt |
| PyPI | huggingface-hub | 1.22.0 | direkt |
| PyPI | openai | 1.109.1 | direkt |
| PyPI | opencv-python-headless | 4.11.0.86 | direkt |
| PyPI | pillow | 10.4.0 | direkt |
| PyPI | platformdirs | 4.9.6 | direkt |
| PyPI | pydantic | 2.13.4 | direkt |
| PyPI | pydantic-settings | 2.14.1 | direkt |
| PyPI | pypdfium2 | 5.10.1 | direkt |
| PyPI | python-dotenv | 1.2.2 | direkt |
| PyPI | torch | 2.12.0 | direkt |
| PyPI | torchvision | 0.27.0 | direkt |
| PyPI | transformers | 5.13.0 | direkt |
| PyPI | aiohappyeyeballs | 2.6.1 | indirekt |
| PyPI | aiohttp | 3.13.5 | indirekt |
| PyPI | aiosignal | 1.4.0 | indirekt |
| PyPI | altair | 6.1.0 | indirekt |
| PyPI | annotated-doc | 0.0.4 | indirekt |
| PyPI | annotated-types | 0.7.0 | 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 | async-timeout | 5.0.1 | indirekt |
| PyPI | attrs | 26.1.0 | indirekt |
| PyPI | babel | 2.18.0 | indirekt |
| PyPI | bleach | 6.3.0 | indirekt |
| PyPI | blinker | 1.9.0 | indirekt |
| PyPI | cachetools | 7.1.1 | indirekt |
| PyPI | certifi | 2026.4.22 | indirekt |
| PyPI | cffi | 2.0.0 | indirekt |
| PyPI | cfgv | 3.5.0 | indirekt |
| PyPI | charset-normalizer | 3.4.7 | indirekt |
| PyPI | colorama | 0.4.6 | indirekt |
| PyPI | comm | 0.2.3 | indirekt |
| PyPI | cuda-bindings | 13.2.0 | indirekt |
| PyPI | cuda-pathfinder | 1.5.4 | indirekt |
| PyPI | cuda-toolkit | 13.0.2 | indirekt |
| PyPI | datasets | 4.8.5 | indirekt |
| PyPI | debugpy | 1.8.20 | indirekt |
| PyPI | decorator | 5.2.1 | indirekt |
| PyPI | defusedxml | 0.7.1 | indirekt |
| PyPI | dill | 0.4.1 | indirekt |
| PyPI | distlib | 0.4.0 | indirekt |
| PyPI | distro | 1.9.0 | indirekt |
| PyPI | exceptiongroup | 1.3.1 | indirekt |
| PyPI | executing | 2.2.1 | indirekt |
| PyPI | fastjsonschema | 2.21.2 | indirekt |
| PyPI | flask | 3.1.3 | indirekt |
| PyPI | fqdn | 1.5.1 | indirekt |
| PyPI | frozenlist | 1.8.0 | indirekt |
| PyPI | fsspec | 2026.2.0 | indirekt |
| PyPI | gitdb | 4.0.12 | indirekt |
| PyPI | gitpython | 3.1.50 | indirekt |
| PyPI | h11 | 0.16.0 | indirekt |
| PyPI | hf-xet | 1.5.1 | indirekt |
| PyPI | httpcore | 1.0.9 | indirekt |
| PyPI | httptools | 0.7.1 | indirekt |
| PyPI | identify | 2.6.19 | indirekt |
| PyPI | idna | 3.15 | indirekt |
| PyPI | iniconfig | 2.3.0 | indirekt |
| PyPI | ipykernel | 7.2.0 | indirekt |
| PyPI | ipython | 8.39.0 | indirekt |
| PyPI | ipython | 9.13.0 | indirekt |
| PyPI | ipython-pygments-lexers | 1.1.1 | indirekt |
| PyPI | ipywidgets | 8.1.8 | indirekt |
| PyPI | isoduration | 20.11.0 | indirekt |
| PyPI | itsdangerous | 2.2.0 | indirekt |
| PyPI | jedi | 0.20.0 | indirekt |
| PyPI | jinja2 | 3.1.6 | indirekt |
| PyPI | jiter | 0.14.0 | 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 | 0.12.1 | indirekt |
| PyPI | jupyter-lsp | 2.3.1 | indirekt |
| PyPI | jupyter-server | 2.18.2 | indirekt |
| PyPI | jupyter-server-terminals | 0.5.4 | indirekt |
| PyPI | jupyterlab | 4.5.7 | indirekt |
| PyPI | jupyterlab-pygments | 0.3.0 | indirekt |
| PyPI | jupyterlab-server | 2.28.0 | indirekt |
| PyPI | jupyterlab-widgets | 3.0.16 | indirekt |
| PyPI | lark | 1.3.1 | indirekt |
| PyPI | markdown-it-py | 4.2.0 | indirekt |
| PyPI | markupsafe | 3.0.3 | indirekt |
| PyPI | matplotlib-inline | 0.2.2 | indirekt |
| PyPI | mdurl | 0.1.2 | indirekt |
| PyPI | mistune | 3.2.1 | indirekt |
| PyPI | mpmath | 1.3.0 | indirekt |
| PyPI | multidict | 6.7.1 | indirekt |
| PyPI | multiprocess | 0.70.19 | indirekt |
| PyPI | narwhals | 2.21.0 | 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 | 3.4.2 | indirekt |
| PyPI | networkx | 3.6.1 | indirekt |
| PyPI | nodeenv | 1.10.0 | indirekt |
| PyPI | notebook | 7.5.6 | indirekt |
| PyPI | notebook-shim | 0.2.4 | indirekt |
| PyPI | numpy | 2.2.6 | indirekt |
| PyPI | numpy | 2.4.4 | indirekt |
| PyPI | nvidia-cublas | 13.1.1.3 | 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 | 9.20.0.48 | 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 | 0.8.1 | indirekt |
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"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
"name": "Packaging",
"detail": "packaging workflow not detected",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 5
},
{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "sast",
"name": "SAST",
"detail": "SAST tool is not run on all commits -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "security_policy",
"name": "Security-Policy",
"detail": "security policy file not detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "signed_releases",
"name": "Signed-Releases",
"detail": "no releases found",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 7.5
},
{
"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": "56 existing vulnerabilities detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "excellent",
"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": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
"red_flag": false,
"exposures": [],
"policy_countries": [
"Russia",
"Iran",
"North Korea"
],
"review_only_matches": 0,
"assessed_self_published_locations": 7
},
"components": [
{
"key": "policy_exposure_multiplier",
"name": "Policy exposure multiplier",
"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": 35,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
"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": [
"legible_commit_history"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
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"legible_history_share": null,
"agent_instruction_files": [],
"agent_instruction_max_bytes": null
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "no data",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "moderate",
"name": "Verify loop (build / test / typecheck)",
"note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"demonstrated_agent_practice",
"automated_maintenance"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 52,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [
"uv.lock"
],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [],
"has_devcontainer": false,
"has_linter_config": true,
"typecheck_configs": [],
"agent_commit_share": null,
"toolchain_manifests": [],
"dependency_bot_commit_share": null
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 18
},
{
"key": "automated_tests",
"name": "Automated tests",
"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",
"name": "Static type checking",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "reproducible_environment",
"name": "Reproducible environment",
"detail": "lockfile",
"points": 10,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "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": "no data",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"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": 53026,
"source_files_sampled": 109,
"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"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "0/109 source files over 60KB",
"points": 55,
"status": "met",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 109,
"oversized": 0
}
}
],
"max_points": 55
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [],
"report_type": "repository",
"generated_at": "2026-07-19T16:13:27.155140Z",
"schema_version": "0.15.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/d/datalab-to/surya.svg",
"full_name": "datalab-to/surya",
"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 v0.15.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.