Registro públicoBueno
Informe de salud del softwareesquema 0.15.0 · métricas 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 tiene un índice de salud de 71 sobre 100, lo que lo sitúa en la banda Bueno. Su puntuación más alta es Vitality (90/100) y la más baja, AI Readiness (35/100). Se actualizó por última vez hace 2 días. Una sola persona concentra la mayor parte del trabajo reciente.
71
global / 100Índice de salud del software
Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.
71
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
Perfil de puntuación
Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.
Titularidad
DatalabOrganización
812 seguidores12 repositorios públicosdesde jul 2024
Este repositorio está respaldado por una organización: una custodia compartida y responsable que puede sobrevivir a cualquier mantenedor individual.
Ecosistemas de paquetes
| Registro | Paquete | Versión | Descargas / mes | Versiones | Última publicación | Etiquetas |
|---|---|---|---|---|---|---|
| PyPI | surya-ocr | 0.22.0 | 893.385 | 93 | hace 2 días | ocrpdftablestext-detectiontext-recognition |
Métricas por categoría
Vitalidad
¿Está vivo el proyecto: se escribe código y se publican versiones?
90
Actividad de desarrollo
83Bueno
Cómo se puntúa
| 36/36 | Recencia de push — último push hace 2 días |
| 18.7/36 | Cadencia de commits — 27/52 semanas con commits |
| 18/18 | Volumen de commits — 237 commits en el último año |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10 |
Datos de entrada utilizados
| commits_last_year | 237 |
| human_commit_share | — |
| days_since_last_push | 2 |
| active_weeks_last_year | 27 |
Disciplina de publicación
100Excelente
Cómo se puntúa
| 27/27 | Publica versiones — 86 versiones publicadas |
| 36/36 | Recencia de las versiones — última versión hace 2 días |
| 27/27 | Cadencia de publicación — una versión cada ~34,7 días |
| 0/10 | OpenSSF Scorecard: Signed-Releases — sin datos |
Datos de entrada utilizados
| releases_count | 86 |
| latest_release_tag | v0.22.0 |
| releases_from_tags | no |
| days_since_latest_release | 2 |
| mean_days_between_releases | 34,7 |
Excluidos de la puntuación (sin datos o no aplicable): OpenSSF Scorecard: Signed-Releases. Los pesos restantes se han renormalizado.
Comunidad y Adopción
¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?
81
Popularidad y adopción
97Excelente
Cómo se puntúa
| 60/60 | Estrellas — 21.119 estrellas |
| 25/25 | Forks — 1518 forks |
| 11.7/15 | Observadores — 128 observadores |
Datos de entrada utilizados
| forks | 1518 |
| stars | 21.119 |
| watchers | 128 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Salud de la comunidad
50Moderado
Cómo se puntúa
| 22.5/22.5 | README |
| 22.5/22.5 | Licencia — licencia reconocida (Apache-2.0) |
| 0/18 | Guía CONTRIBUTING |
| 0/13.5 | Código de conducta |
| 0/7.2 | Plantilla de issues |
| 0/6.3 | Plantilla de PR |
Datos de entrada utilizados
| has_readme | sí |
| has_license | sí |
| has_contributing | no |
| has_issue_template | no |
| has_code_of_conduct | no |
| has_pull_request_template | no |
Adopción en el ecosistema (descargas)
99Excelente
Cómo se puntúa
| 79.3/80 | Descargas mensuales — 893.385 descargas/mes en pypi |
| 0/20 | Dependientes en el registro — no lo informa este ecosistema |
Datos de entrada utilizados
| packages | surya-ocr |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 893.385 |
Excluidos de la puntuación (sin datos o no aplicable): Dependientes en el registro. Los pesos restantes se han renormalizado.
Sostenibilidad y Gobernanza
¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?
61
Resiliencia de mantenedores (factor bus)
36En riesgo
Cómo se puntúa
| 9/54 | Factor bus — la mitad de los commits recae en 1 contribuyente(s) |
| 7.9/22.5 | Distribución de commits — el principal contribuyente firma el 65% de los commits |
| 13.5/13.5 | Amplitud de contribuyentes — 18 contribuyentes |
| 6/10 | OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6 |
Datos de entrada utilizados
| bus_factor | 1 |
| contributors_sampled | 18 |
| top_contributor_share | 0,649 |
Capacidad de respuesta a issues y PR
56Moderado
Cómo se puntúa
| 22.5/46.8 | Resolución de issues — 48% de issues cerradas |
| 34/38.3 | Aceptación de PR — 190/214 PR decididos fusionados |
| 0/15 | OpenSSF Scorecard: Code-Review — Found 0/12 approved changesets -- score normalized to 0 |
Datos de entrada utilizados
| merged_prs | 190 |
| open_issues | 152 |
| closed_issues | 141 |
| issue_closed_ratio | 0,481 |
| closed_unmerged_prs | 24 |
Propiedad y custodia
63Moderado
Cómo se puntúa
| 30/30 | Respaldo de la propiedad — propiedad de una organización |
| 0/20 | Dominio verificado |
| 20.9/25 | Alcance del propietario — 812 seguidores de datalab-to |
| 12.1/25 | Trayectoria — 12 repos públicos, cuenta de ~1 años |
Datos de entrada utilizados
| followers | 812 |
| owner_type | Organization |
| is_verified | — |
| owner_login | datalab-to |
| public_repos | 12 |
| account_age_days | 723 |
Mantenimiento de paquetes
100Excelente
Cómo se puntúa
| 25/25 | Publicado y resoluble — 1 paquete(s) en pypi |
| 35/35 | Recencia de publicación — última publicación hace 2 días |
| 20/20 | Historial de versiones — 93 versiones en el registro |
| 20/20 | No obsoleto — activo, ni obsoleto ni retirado |
Datos de entrada utilizados
| packages | surya-ocr |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 2 |
Calidad de Ingeniería
¿Existen unas prácticas mínimas de ingeniería y documentación?
82
Prácticas de ingeniería
94Excelente
Cómo se puntúa
| 24/24 | Flujos de trabajo de CI — 4 flujo(s) de trabajo |
| 24/24 | Pruebas presentes |
| 16/16 | Configuración de linter |
| 9.6/9.6 | Hooks de pre-commit |
| 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 |
Datos de entrada utilizados
| has_ci | sí |
| has_tests | sí |
| has_editorconfig | no |
| has_linter_config | sí |
| has_precommit_config | sí |
Documentación
65Moderado
Cómo se puntúa
| 30/30 | README |
| 0/25 | Directorio de documentación |
| 15/15 | Sitio de documentación / página del proyecto — https://www.datalab.to |
| 10/10 | Descripción del repositorio |
| 0/10 | Topics |
| 10/10 | Wiki |
Datos de entrada utilizados
| topics | — |
| has_wiki | sí |
| homepage | https://www.datalab.to |
| has_readme | sí |
| has_docs_dir | no |
| has_description | sí |
Seguridad
¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?
36
Postura de seguridad
36En riesgo
Cómo se puntúa
| 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 | Licencia — 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 — sin datos |
| 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 — sin datos |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 0/7.5 | Vulnerabilities — 56 existing vulnerabilities detected |
Datos de entrada utilizados
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 3,6 |
Excluidos de la puntuación (sin datos o no aplicable): packaging, signed_releases. Los pesos restantes se han renormalizado.
Preparación para IA
¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.
35
Contexto y guía para agentes
1Crítico
Cómo se puntúa
| 0/45 | Instrucciones para agentes — sin CLAUDE.md / AGENTS.md / reglas de editor |
| 0/15 | Documentación legible por máquinas (llms.txt) |
| 0/40 | Historial de commits legible — sin datos |
Datos de entrada utilizados
| has_llms_txt | no |
| legible_history_share | — |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Excluidos de la puntuación (sin datos o no aplicable): Historial de commits legible. Los pesos restantes se han renormalizado.
Cómo se puntúa
| 0/18 | Arranque con un solo comando |
| 22/22 | Pruebas automatizadas |
| 11/11 | Configuración de lint / formato |
| 0/11 | Verificación estática de tipos |
| 10/10 | Entorno reproducible — lockfile |
| 0/10 | Práctica demostrada con agentes — sin datos |
| 0/8 | Mantenimiento automatizado — sin datos |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Datos de entrada utilizados
| has_nix | no |
| has_tests | sí |
| lockfiles | uv.lock |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | — |
| has_devcontainer | no |
| has_linter_config | sí |
| typecheck_configs | — |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | — |
Excluidos de la puntuación (sin datos o no aplicable): Práctica demostrada con agentes, Mantenimiento automatizado. Los pesos restantes se han renormalizado.
Legibilidad del código para modelos
55Moderado
Cómo se puntúa
| 0/45 | Código verificable por tipos — Python sin configuración de verificación de tipos |
| 55/55 | Tamaños de archivo manejables — 0/109 archivos fuente de más de 60 KB |
Datos de entrada utilizados
| primary_language | Python |
| largest_source_bytes | 53.026 |
| source_files_sampled | 109 |
| oversized_source_files | 0 |
Datos clave
21.119estrellas de GitHub
18contribuidores
237commits en los últimos 12 meses
2días desde el último push
86versiones publicadas
1factor bus
152issues abiertas
PyPIecosistemas de paquetes
Más detalle
OpenSSF Scorecard 3.6 / 10
3.6agregado
Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).
| 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 |
| n/d | 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 |
| n/d | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 0 | Vulnerabilities | 56 existing vulnerabilities detected |
Dependencias directas 17
| Registro | Paquete | Restricción de versión | Manifiesto |
|---|---|---|---|
| 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 |
Todas las dependencias 212
Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 17 paquetes directos y 195 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.
| Registro | Paquete | Versión | Relación |
|---|---|---|---|
| PyPI | beautifulsoup4 | 4.14.3 | directa |
| PyPI | click | 8.4.2 | directa |
| PyPI | filelock | 3.29.0 | directa |
| PyPI | filetype | 1.2.0 | directa |
| PyPI | httpx | 0.27.2 | directa |
| PyPI | huggingface-hub | 1.22.0 | directa |
| PyPI | openai | 1.109.1 | directa |
| PyPI | opencv-python-headless | 4.11.0.86 | directa |
| PyPI | pillow | 10.4.0 | directa |
| PyPI | platformdirs | 4.9.6 | directa |
| PyPI | pydantic | 2.13.4 | directa |
| PyPI | pydantic-settings | 2.14.1 | directa |
| PyPI | pypdfium2 | 5.10.1 | directa |
| PyPI | python-dotenv | 1.2.2 | directa |
| PyPI | torch | 2.12.0 | directa |
| PyPI | torchvision | 0.27.0 | directa |
| PyPI | transformers | 5.13.0 | directa |
| PyPI | aiohappyeyeballs | 2.6.1 | indirecta |
| PyPI | aiohttp | 3.13.5 | indirecta |
| PyPI | aiosignal | 1.4.0 | indirecta |
| PyPI | altair | 6.1.0 | indirecta |
| PyPI | annotated-doc | 0.0.4 | indirecta |
| PyPI | annotated-types | 0.7.0 | indirecta |
| PyPI | anyio | 4.13.0 | indirecta |
| PyPI | appnope | 0.1.4 | indirecta |
| PyPI | argon2-cffi | 25.1.0 | indirecta |
| PyPI | argon2-cffi-bindings | 25.1.0 | indirecta |
| PyPI | arrow | 1.4.0 | indirecta |
| PyPI | asttokens | 3.0.1 | indirecta |
| PyPI | async-lru | 2.3.0 | indirecta |
| PyPI | async-timeout | 5.0.1 | indirecta |
| PyPI | attrs | 26.1.0 | indirecta |
| PyPI | babel | 2.18.0 | indirecta |
| PyPI | bleach | 6.3.0 | indirecta |
| PyPI | blinker | 1.9.0 | indirecta |
| PyPI | cachetools | 7.1.1 | indirecta |
| PyPI | certifi | 2026.4.22 | indirecta |
| PyPI | cffi | 2.0.0 | indirecta |
| PyPI | cfgv | 3.5.0 | indirecta |
| PyPI | charset-normalizer | 3.4.7 | indirecta |
| PyPI | colorama | 0.4.6 | indirecta |
| PyPI | comm | 0.2.3 | indirecta |
| PyPI | cuda-bindings | 13.2.0 | indirecta |
| PyPI | cuda-pathfinder | 1.5.4 | indirecta |
| PyPI | cuda-toolkit | 13.0.2 | indirecta |
| PyPI | datasets | 4.8.5 | indirecta |
| PyPI | debugpy | 1.8.20 | indirecta |
| PyPI | decorator | 5.2.1 | indirecta |
| PyPI | defusedxml | 0.7.1 | indirecta |
| PyPI | dill | 0.4.1 | indirecta |
| PyPI | distlib | 0.4.0 | indirecta |
| PyPI | distro | 1.9.0 | indirecta |
| PyPI | exceptiongroup | 1.3.1 | indirecta |
| PyPI | executing | 2.2.1 | indirecta |
| PyPI | fastjsonschema | 2.21.2 | indirecta |
| PyPI | flask | 3.1.3 | indirecta |
| PyPI | fqdn | 1.5.1 | indirecta |
| PyPI | frozenlist | 1.8.0 | indirecta |
| PyPI | fsspec | 2026.2.0 | indirecta |
| PyPI | gitdb | 4.0.12 | indirecta |
| PyPI | gitpython | 3.1.50 | indirecta |
| PyPI | h11 | 0.16.0 | indirecta |
| PyPI | hf-xet | 1.5.1 | indirecta |
| PyPI | httpcore | 1.0.9 | indirecta |
| PyPI | httptools | 0.7.1 | indirecta |
| PyPI | identify | 2.6.19 | indirecta |
| PyPI | idna | 3.15 | indirecta |
| PyPI | iniconfig | 2.3.0 | indirecta |
| PyPI | ipykernel | 7.2.0 | indirecta |
| PyPI | ipython | 8.39.0 | indirecta |
| PyPI | ipython | 9.13.0 | indirecta |
| PyPI | ipython-pygments-lexers | 1.1.1 | indirecta |
| PyPI | ipywidgets | 8.1.8 | indirecta |
| PyPI | isoduration | 20.11.0 | indirecta |
| PyPI | itsdangerous | 2.2.0 | indirecta |
| PyPI | jedi | 0.20.0 | indirecta |
| PyPI | jinja2 | 3.1.6 | indirecta |
| PyPI | jiter | 0.14.0 | indirecta |
| PyPI | json5 | 0.14.0 | indirecta |
| PyPI | jsonpointer | 3.1.1 | indirecta |
| PyPI | jsonschema | 4.26.0 | indirecta |
| PyPI | jsonschema-specifications | 2025.9.1 | indirecta |
| PyPI | jupyter | 1.1.1 | indirecta |
| PyPI | jupyter-client | 8.8.0 | indirecta |
| PyPI | jupyter-console | 6.6.3 | indirecta |
| PyPI | jupyter-core | 5.9.1 | indirecta |
| PyPI | jupyter-events | 0.12.1 | indirecta |
| PyPI | jupyter-lsp | 2.3.1 | indirecta |
| PyPI | jupyter-server | 2.18.2 | indirecta |
| PyPI | jupyter-server-terminals | 0.5.4 | indirecta |
| PyPI | jupyterlab | 4.5.7 | indirecta |
| PyPI | jupyterlab-pygments | 0.3.0 | indirecta |
| PyPI | jupyterlab-server | 2.28.0 | indirecta |
| PyPI | jupyterlab-widgets | 3.0.16 | indirecta |
| PyPI | lark | 1.3.1 | indirecta |
| PyPI | markdown-it-py | 4.2.0 | indirecta |
| PyPI | markupsafe | 3.0.3 | indirecta |
| PyPI | matplotlib-inline | 0.2.2 | indirecta |
| PyPI | mdurl | 0.1.2 | indirecta |
| PyPI | mistune | 3.2.1 | indirecta |
| PyPI | mpmath | 1.3.0 | indirecta |
| PyPI | multidict | 6.7.1 | indirecta |
| PyPI | multiprocess | 0.70.19 | indirecta |
| PyPI | narwhals | 2.21.0 | indirecta |
| PyPI | nbclient | 0.10.4 | indirecta |
| PyPI | nbconvert | 7.17.1 | indirecta |
| PyPI | nbformat | 5.10.4 | indirecta |
| PyPI | nest-asyncio | 1.6.0 | indirecta |
| PyPI | networkx | 3.4.2 | indirecta |
| PyPI | networkx | 3.6.1 | indirecta |
| PyPI | nodeenv | 1.10.0 | indirecta |
| PyPI | notebook | 7.5.6 | indirecta |
| PyPI | notebook-shim | 0.2.4 | indirecta |
| PyPI | numpy | 2.2.6 | indirecta |
| PyPI | numpy | 2.4.4 | indirecta |
| PyPI | nvidia-cublas | 13.1.1.3 | indirecta |
| PyPI | nvidia-cuda-cupti | 13.0.85 | indirecta |
| PyPI | nvidia-cuda-nvrtc | 13.0.88 | indirecta |
| PyPI | nvidia-cuda-runtime | 13.0.96 | indirecta |
| PyPI | nvidia-cudnn-cu13 | 9.20.0.48 | indirecta |
| PyPI | nvidia-cufft | 12.0.0.61 | indirecta |
| PyPI | nvidia-cufile | 1.15.1.6 | indirecta |
| PyPI | nvidia-curand | 10.4.0.35 | indirecta |
| PyPI | nvidia-cusolver | 12.0.4.66 | indirecta |
| PyPI | nvidia-cusparse | 12.6.3.3 | indirecta |
| PyPI | nvidia-cusparselt-cu13 | 0.8.1 | indirecta |
| PyPI | nvidia-nccl-cu13 | 2.29.7 | indirecta |
| PyPI | nvidia-nvjitlink | 13.0.88 | indirecta |
| PyPI | nvidia-nvshmem-cu13 | 3.4.5 | indirecta |
| PyPI | nvidia-nvtx | 13.0.85 | indirecta |
| PyPI | overrides | 7.7.0 | indirecta |
| PyPI | packaging | 26.2 | indirecta |
| PyPI | pandas | 2.3.3 | indirecta |
| PyPI | pandas | 3.0.3 | indirecta |
| PyPI | pandocfilters | 1.5.1 | indirecta |
| PyPI | parso | 0.8.7 | indirecta |
| PyPI | pdftext | 0.7.1 | indirecta |
| PyPI | pexpect | 4.9.0 | indirecta |
| PyPI | pluggy | 1.6.0 | indirecta |
| PyPI | pre-commit | 4.6.0 | indirecta |
| PyPI | prometheus-client | 0.25.0 | indirecta |
| PyPI | prompt-toolkit | 3.0.52 | indirecta |
| PyPI | propcache | 0.5.2 | indirecta |
| PyPI | protobuf | 7.34.1 | indirecta |
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{
"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": {}
}
],
"value": 1,
"inputs": {
"has_llms_txt": false,
"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": {
"primary_language": "Python",
"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"
}Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.
Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.15.0 — metodología completa · wiki de métricas.
Cómo se sitúa un resultado dentro del registro general: estadísticas agregadas — PyPI.