Registro públicoBueno
Informe de salud del softwareesquema 0.12.0 · métricas 1.13.0 · 2026-07-18 11:55 UTC
meta-pytorch / torchx
TorchX is a universal job launcher for PyTorch applications. TorchX is designed to have fast iteration time for training/research and support for E2E production ML pipelines when you're ready.
meta-pytorch/torchx tiene un índice de salud de 75 sobre 100, lo que lo sitúa en la banda Bueno. Su puntuación más alta es Engineering Quality (92/100) y la más baja, Security (58/100). Se actualizó por última vez hoy. 3 personas concentran la mayor parte del trabajo reciente.
75
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.
75
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
Meta PyTorchOrganización
1389 seguidores58 repositorios públicosdesde jun 2022
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 | torchxapunta a otro repositorio; no se puntúa | 0.7.0 | 193.706 | 17 | hace 731 días | pytorchmachine-learning |
Métricas por categoría
Vitalidad
¿Está vivo el proyecto: se escribe código y se publican versiones?
78
Actividad de desarrollo
95Excelente
Cómo se puntúa
| 36/36 | Recencia de push — último push hace 0 días |
| 31.2/36 | Cadencia de commits — 45/52 semanas con commits |
| 18/18 | Volumen de commits — 159 commits en el último año |
| 10/10 | OpenSSF Scorecard: Maintained — 22 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
Datos de entrada utilizados
| commits_last_year | 159 |
| human_commit_share | — |
| days_since_last_push | 0 |
| active_weeks_last_year | 45 |
Disciplina de publicación
52Moderado
Cómo se puntúa
| 27/27 | Publica versiones — 15 versiones publicadas |
| 0/36 | Recencia de las versiones — última versión hace 731 días |
| 19.8/27 | Cadencia de publicación — una versión cada ~111,3 días |
| 0/10 | OpenSSF Scorecard: Signed-Releases — sin datos |
Datos de entrada utilizados
| releases_count | 15 |
| latest_release_tag | v0.7.0 |
| releases_from_tags | no |
| days_since_latest_release | 731 |
| mean_days_between_releases | 111,3 |
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?
76
Popularidad y adopción
67Moderado
Cómo se puntúa
| 42.7/60 | Estrellas — 427 estrellas |
| 18.2/25 | Forks — 155 forks |
| 6.5/15 | Observadores — 16 observadores |
Datos de entrada utilizados
| forks | 155 |
| stars | 427 |
| watchers | 16 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Salud de la comunidad
86Excelente
Cómo se puntúa
| 22.5/22.5 | README |
| 16.9/22.5 | Licencia — archivo de licencia presente, no es una licencia reconocida |
| 18/18 | Guía CONTRIBUTING |
| 13.5/13.5 | Código de conducta |
| 0/7.2 | Plantilla de issues |
| 6.3/6.3 | Plantilla de PR |
Datos de entrada utilizados
| has_readme | sí |
| has_license | sí |
| has_contributing | sí |
| has_issue_template | no |
| has_code_of_conduct | sí |
| has_pull_request_template | sí |
Sostenibilidad y Gobernanza
¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?
70
Cómo se puntúa
| 36/54 | Factor bus — la mitad de los commits recae en 3 contribuyente(s) |
| 16.1/22.5 | Distribución de commits — el principal contribuyente firma el 29% de los commits |
| 13.5/13.5 | Amplitud de contribuyentes — 99 contribuyentes |
| 10/10 | OpenSSF Scorecard: Contributors — project has 21 contributing companies or organizations |
Datos de entrada utilizados
| bus_factor | 3 |
| contributors_sampled | 99 |
| top_contributor_share | 0,286 |
Capacidad de respuesta a issues y PR
59Moderado
Cómo se puntúa
| 31.2/46.8 | Resolución de issues — 67% de issues cerradas |
| 16/38.3 | Aceptación de PR — 434/1036 PR decididos fusionados |
| 12/15 | OpenSSF Scorecard: Code-Review — Found 22/27 approved changesets -- score normalized to 8 |
Datos de entrada utilizados
| merged_prs | 434 |
| open_issues | 68 |
| closed_issues | 137 |
| issue_closed_ratio | 0,668 |
| closed_unmerged_prs | 602 |
Propiedad y custodia
74Bueno
Cómo se puntúa
| 30/30 | Respaldo de la propiedad — propiedad de una organización |
| 0/20 | Dominio verificado |
| 22.6/25 | Alcance del propietario — 1389 seguidores de meta-pytorch |
| 21.1/25 | Trayectoria — 58 repos públicos, cuenta de ~4 años |
Datos de entrada utilizados
| followers | 1389 |
| owner_type | Organization |
| is_verified | — |
| owner_login | meta-pytorch |
| public_repos | 58 |
| account_age_days | 1499 |
Calidad de Ingeniería
¿Existen unas prácticas mínimas de ingeniería y documentación?
92
Prácticas de ingeniería
94Excelente
Cómo se puntúa
| 24/24 | Flujos de trabajo de CI — 10 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 — 30 out of 30 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
90Excelente
Cómo se puntúa
| 30/30 | README |
| 25/25 | Directorio de documentación |
| 15/15 | Sitio de documentación / página del proyecto — https://pytorch.org/torchx |
| 10/10 | Descripción del repositorio |
| 10/10 | Topics — 12 topics |
| 0/10 | Wiki |
Datos de entrada utilizados
| topics | pytorch, machine-learning, kubernetes, slurm, distributed-training, pipelines, components, deep-learning, python, aws-batch, ray, airflow |
| has_wiki | no |
| homepage | https://pytorch.org/torchx |
| has_readme | sí |
| has_docs_dir | sí |
| has_description | sí |
Seguridad
¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?
58
Postura de seguridad
58Moderado
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 — 30 out of 30 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 |
| 6/7.5 | Code-Review — Found 22/27 approved changesets -- score normalized to 8 |
| 2.5/2.5 | Contributors — project has 21 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.2/2.5 | Licencia — license file detected |
| 7.5/7.5 | Maintained — 22 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 4/5 | SAST — SAST tool is not run on all commits -- score normalized to 8 |
| 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 — 39 existing vulnerabilities detected |
Datos de entrada utilizados
| source | openssf_scorecard |
| checks_evaluated | 17 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 1 |
| scorecard_aggregate | 5,8 |
Excluidos de la puntuación (sin datos o no aplicable): 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.
66
Contexto y guía para agentes
75Bueno
Cómo se puntúa
| 45/45 | Instrucciones para agentes — .claude/CLAUDE.md |
| 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 | .claude/CLAUDE.md |
| agent_instruction_max_bytes | 1532 |
Excluidos de la puntuación (sin datos o no aplicable): Historial de commits legible. Los pesos restantes se han renormalizado.
Cómo se puntúa
| 18/18 | Arranque con un solo comando — docs/Makefile |
| 22/22 | Pruebas automatizadas |
| 11/11 | Configuración de lint / formato |
| 0/11 | Verificación estática de tipos |
| 10/10 | Entorno reproducible — Dockerfile, lockfile |
| 0/10 | Práctica demostrada con agentes — sin datos |
| 5/8 | Mantenimiento automatizado — automatización de dependencias configurada, no observada en los commits muestreados |
| 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 | sí |
| typed_language | no |
| bootstrap_files | docs/Makefile |
| has_devcontainer | no |
| has_linter_config | sí |
| typecheck_configs | — |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Excluidos de la puntuación (sin datos o no aplicable): Práctica demostrada con agentes. 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 |
| 54.8/55 | Tamaños de archivo manejables — 1/231 archivos fuente de más de 60 KB |
Datos de entrada utilizados
| primary_language | Python |
| largest_source_bytes | 68.380 |
| source_files_sampled | 231 |
| oversized_source_files | 1 |
Interfaces legibles por máquina
40En riesgo
Cómo se puntúa
| 0/40 | Esquema de API (OpenAPI/GraphQL/proto) |
| 0/20 | Servidor MCP |
| 40/40 | Ejemplos ejecutables — examples |
Datos de entrada utilizados
| example_dirs | examples |
| has_mcp_signal | no |
| api_schema_files | — |
Datos clave
427estrellas de GitHub
99contribuidores
159commits en los últimos 12 meses
0días desde el último push
15versiones publicadas
3factor bus
68issues abiertas
PyPIecosistemas de paquetes
Advertencias de recopilación de datos
- pypi package 'torchx' points at a different repository (https://github.com/pytorch/torchx); excluded from ecosystem scoring
Más detalle
OpenSSF Scorecard 5.8 / 10
5.8agregado
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 | 30 out of 30 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 |
| 8 | Code-Review | Found 22/27 approved changesets -- score normalized to 8 |
| 10 | Contributors | project has 21 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 9 | License | license file detected |
| 10 | Maintained | 22 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 8 | SAST | SAST tool is not run on all commits -- score normalized to 8 |
| 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 | 39 existing vulnerabilities detected |
Dependencias directas 7
| Registro | Paquete | Restricción de versión | Manifiesto |
|---|---|---|---|
| PyPI | docstring-parser | >=0.8.1 | pyproject.toml |
| PyPI | pyyaml | — | pyproject.toml |
| PyPI | docker | — | pyproject.toml |
| PyPI | filelock | — | pyproject.toml |
| PyPI | fsspec | >=2023.10.0 | pyproject.toml |
| PyPI | tabulate | — | pyproject.toml |
| PyPI | typing-extensions | — | pyproject.toml |
Todas las dependencias 237
Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 7 paquetes directos y 230 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.
| Registro | Paquete | Versión | Relación |
|---|---|---|---|
| PyPI | docker | 7.1.0 | directa |
| PyPI | docstring-parser | 0.17.0 | directa |
| PyPI | filelock | 3.20.3 | directa |
| PyPI | fsspec | 2026.2.0 | directa |
| PyPI | pyyaml | 6.0.3 | directa |
| PyPI | tabulate | 0.9.0 | directa |
| PyPI | typing-extensions | 4.15.0 | directa |
| PyPI | absl-py | 2.3.1 | indirecta |
| PyPI | aiobotocore | 3.1.2 | indirecta |
| PyPI | aiohappyeyeballs | 2.6.1 | indirecta |
| PyPI | aiohttp | 3.14.1 | indirecta |
| PyPI | aioitertools | 0.13.0 | indirecta |
| PyPI | aiosignal | 1.4.0 | indirecta |
| PyPI | alembic | 1.18.4 | indirecta |
| PyPI | annotated-doc | 0.0.4 | indirecta |
| PyPI | annotated-types | 0.7.0 | indirecta |
| PyPI | antlr4-python3-runtime | 4.9.3 | indirecta |
| PyPI | anyio | 4.12.1 | indirecta |
| PyPI | asttokens | 3.0.1 | indirecta |
| PyPI | async-timeout | 5.0.1 | indirecta |
| PyPI | attrs | 23.2.0 | indirecta |
| PyPI | ax-platform | 0.2.3 | indirecta |
| PyPI | bcrypt | 5.0.0 | indirecta |
| PyPI | black | 25.11.0 | indirecta |
| PyPI | blinker | 1.9.0 | indirecta |
| PyPI | boto3 | 1.42.42 | indirecta |
| PyPI | botocore | 1.42.42 | indirecta |
| PyPI | botorch | 0.6.0 | indirecta |
| PyPI | cachetools | 6.2.4 | indirecta |
| PyPI | captum | 0.8.0 | indirecta |
| PyPI | certifi | 2026.1.4 | indirecta |
| PyPI | cffi | 2.0.0 | indirecta |
| PyPI | charset-normalizer | 3.4.4 | indirecta |
| PyPI | click | 8.1.8 | indirecta |
| PyPI | cloudpickle | 3.1.2 | indirecta |
| PyPI | colorama | 0.4.6 | indirecta |
| PyPI | contourpy | 1.3.2 | indirecta |
| PyPI | contourpy | 1.3.3 | indirecta |
| PyPI | coverage | 7.13.1 | indirecta |
| PyPI | cryptography | 46.0.7 | indirecta |
| PyPI | cuda-bindings | 12.9.4 | indirecta |
| PyPI | cuda-pathfinder | 1.3.3 | indirecta |
| PyPI | cycler | 0.12.1 | indirecta |
| PyPI | databricks-sdk | 0.80.0 | indirecta |
| PyPI | dataclasses-json | 0.5.7 | indirecta |
| PyPI | decorator | 5.2.1 | indirecta |
| PyPI | enum-compat | 0.0.3 | indirecta |
| PyPI | exceptiongroup | 1.3.1 | indirecta |
| PyPI | executing | 2.2.1 | indirecta |
| PyPI | fastapi | 0.137.1 | indirecta |
| PyPI | flask | 3.1.2 | indirecta |
| PyPI | flask-cors | 6.0.2 | indirecta |
| PyPI | fonttools | 4.61.1 | indirecta |
| PyPI | frozenlist | 1.8.0 | indirecta |
| PyPI | gitdb | 4.0.12 | indirecta |
| PyPI | gitpython | 3.1.46 | indirecta |
| PyPI | google-auth | 2.47.0 | indirecta |
| PyPI | google-auth-oauthlib | 1.0.0 | indirecta |
| PyPI | gpytorch | 1.15.1 | indirecta |
| PyPI | graphene | 3.4.3 | indirecta |
| PyPI | graphql-core | 3.2.7 | indirecta |
| PyPI | graphql-relay | 3.2.0 | indirecta |
| PyPI | greenlet | 3.3.1 | indirecta |
| PyPI | grpcio | 1.76.0 | indirecta |
| PyPI | gunicorn | 23.0.0 | indirecta |
| PyPI | h11 | 0.16.0 | indirecta |
| PyPI | huey | 2.6.0 | indirecta |
| PyPI | hydra-core | 1.3.2 | indirecta |
| PyPI | idna | 3.18 | indirecta |
| PyPI | importlib-metadata | 6.11.0 | indirecta |
| PyPI | iniconfig | 2.3.0 | indirecta |
| PyPI | invoke | 2.2.1 | indirecta |
| PyPI | ipython | 8.38.0 | indirecta |
| PyPI | ipython | 9.9.0 | indirecta |
| PyPI | ipython-pygments-lexers | 1.1.1 | indirecta |
| PyPI | itsdangerous | 2.2.0 | indirecta |
| PyPI | jaxtyping | 0.3.6 | indirecta |
| PyPI | jedi | 0.19.2 | indirecta |
| PyPI | jinja2 | — | indirecta |
| PyPI | jinja2 | 3.1.6 | indirecta |
| PyPI | jmespath | 1.1.0 | indirecta |
| PyPI | joblib | 1.5.3 | indirecta |
| PyPI | jsonschema | 4.26.0 | indirecta |
| PyPI | jsonschema-specifications | 2025.9.1 | indirecta |
| PyPI | kiwisolver | 1.4.9 | indirecta |
| PyPI | kubernetes | 25.3.0 | indirecta |
| PyPI | libcst | 1.8.6 | indirecta |
| PyPI | lightning-utilities | 0.15.2 | indirecta |
| PyPI | linear-operator | 0.6 | indirecta |
| PyPI | lintrunner | 0.13.0 | indirecta |
| PyPI | lintrunner-adapters | 0.13.0 | indirecta |
| PyPI | mako | 1.3.12 | indirecta |
| PyPI | markdown | 3.10.1 | indirecta |
| PyPI | markdown-it-py | 4.0.0 | indirecta |
| PyPI | markupsafe | 3.0.3 | indirecta |
| PyPI | marshmallow | 3.26.2 | indirecta |
| PyPI | marshmallow-enum | 1.5.1 | indirecta |
| PyPI | matplotlib | 3.10.8 | indirecta |
| PyPI | matplotlib-inline | 0.2.1 | indirecta |
| PyPI | mdurl | 0.1.2 | indirecta |
| PyPI | mlflow | 3.13.0 | indirecta |
| PyPI | mlflow-skinny | 3.13.0 | indirecta |
| PyPI | mlflow-tracing | 3.13.0 | indirecta |
| PyPI | mock | 4.0.3 | indirecta |
| PyPI | moto | 5.0.28 | indirecta |
| PyPI | mpmath | 1.3.0 | indirecta |
| PyPI | multidict | 6.7.0 | indirecta |
| PyPI | mypy-extensions | 1.1.0 | indirecta |
| PyPI | narwhals | 2.15.0 | indirecta |
| PyPI | nest-asyncio | 1.6.0 | indirecta |
| PyPI | networkx | 3.4.2 | indirecta |
| PyPI | networkx | 3.6.1 | indirecta |
| PyPI | numpy | 1.26.4 | indirecta |
| PyPI | nvidia-cublas-cu12 | 12.8.4.1 | indirecta |
| PyPI | nvidia-cuda-cupti-cu12 | 12.8.90 | indirecta |
| PyPI | nvidia-cuda-nvrtc-cu12 | 12.8.93 | indirecta |
| PyPI | nvidia-cuda-runtime-cu12 | 12.8.90 | indirecta |
| PyPI | nvidia-cudnn-cu12 | 9.10.2.21 | indirecta |
| PyPI | nvidia-cufft-cu12 | 11.3.3.83 | indirecta |
| PyPI | nvidia-cufile-cu12 | 1.13.1.3 | indirecta |
| PyPI | nvidia-curand-cu12 | 10.3.9.90 | indirecta |
| PyPI | nvidia-cusolver-cu12 | 11.7.3.90 | indirecta |
| PyPI | nvidia-cusparse-cu12 | 12.5.8.93 | indirecta |
| PyPI | nvidia-cusparselt-cu12 | 0.7.1 | indirecta |
| PyPI | nvidia-nccl-cu12 | 2.27.5 | indirecta |
| PyPI | nvidia-nvjitlink-cu12 | 12.8.93 | indirecta |
| PyPI | nvidia-nvshmem-cu12 | 3.4.5 | indirecta |
| PyPI | nvidia-nvtx-cu12 | 12.8.90 | indirecta |
| PyPI | oauthlib | 3.3.1 | indirecta |
| PyPI | omegaconf | 2.2.3 | indirecta |
| PyPI | opentelemetry-api | 1.39.1 | indirecta |
| PyPI | opentelemetry-proto | 1.39.1 | indirecta |
| PyPI | opentelemetry-sdk | 1.39.1 | indirecta |
| PyPI | opentelemetry-semantic-conventions | 0.60b1 | indirecta |
| PyPI | packaging | 25.0 | indirecta |
| PyPI | pandas | 2.3.3 | indirecta |
| PyPI | paramiko | 4.0.0 | indirecta |
| PyPI | parso | 0.8.5 | indirecta |
| PyPI | pexpect | 4.9.0 | indirecta |
| PyPI | pillow | 12.1.0 | indirecta |
| PyPI | pip | 25.3 | indirecta |
| PyPI | platformdirs | 4.5.1 | indirecta |
| PyPI | plotly | 6.5.2 | indirecta |
| PyPI | pluggy | 1.6.0 | indirecta |
| PyPI | prettytable | 3.17.0 | indirecta |
| PyPI | prompt-toolkit | 3.0.52 | indirecta |
| PyPI | propcache | 0.4.1 | indirecta |
| PyPI | protobuf | 5.29.5 | indirecta |
| PyPI | psutil | 7.2.1 | indirecta |
| PyPI | ptyprocess | 0.7.0 | indirecta |
| PyPI | pure-eval | 0.2.3 | indirecta |
| PyPI | pyarrow | 24.0.0 | indirecta |
| PyPI | pyasn1 | 0.6.3 | indirecta |
| PyPI | pyasn1-modules | 0.4.2 | indirecta |
| PyPI | pycparser | 3.0 | indirecta |
| PyPI | pydantic | 2.12.5 | indirecta |
| PyPI | pydantic-core | 2.41.5 | indirecta |
| PyPI | pygments | 2.19.2 | indirecta |
| PyPI | pynacl | 1.6.2 | indirecta |
| PyPI | pyparsing | 3.3.2 | indirecta |
| PyPI | pyre-check | 0.9.25 | indirecta |
| PyPI | pyre-extensions | 0.0.32 | indirecta |
| PyPI | pytest | 9.0.3 | indirecta |
| PyPI | pytest-cov | 7.0.0 | indirecta |
| PyPI | python-dateutil | 2.9.0.post0 | indirecta |
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],
"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": "39 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": 4
},
"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": "moderate",
"name": "AI Readiness",
"value": 66,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
"band": "good",
"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": 75,
"inputs": {
"has_llms_txt": false,
"legible_history_share": null,
"agent_instruction_files": [
".claude/CLAUDE.md"
],
"agent_instruction_max_bytes": 1532
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": ".claude/CLAUDE.md",
"points": 45,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": ".claude/CLAUDE.md"
}
}
],
"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": "good",
"name": "Verify loop (build / test / typecheck)",
"note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.",
"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
"demonstrated_agent_practice"
]
}
},
{
"code": "weights_renormalized",
"params": {}
}
],
"value": 73,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [
"uv.lock"
],
"has_dockerfile": true,
"typed_language": false,
"bootstrap_files": [
"docs/Makefile"
],
"has_devcontainer": false,
"has_linter_config": true,
"typecheck_configs": [],
"agent_commit_share": null,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": "docs/Makefile",
"points": 18,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "docs/Makefile"
}
}
],
"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": "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": "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": 68380,
"source_files_sampled": 231,
"oversized_source_files": 1
},
"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": "1/231 source files over 60KB",
"points": 54.8,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 231,
"oversized": 1
}
}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
"band": "at_risk",
"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"example_dirs": [
"examples"
],
"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": "examples",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "examples"
}
}
],
"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": [
"pypi package 'torchx' points at a different repository (https://github.com/pytorch/torchx); excluded from ecosystem scoring"
],
"report_type": "repository",
"generated_at": "2026-07-18T11:55:34.530562Z",
"schema_version": "0.12.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/meta-pytorch/torchx.svg",
"full_name": "meta-pytorch/torchx",
"license_state": "custom",
"license_spdx": null
}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 v0.12.0 — metodología completa · wiki de métricas.
Cómo se sitúa un resultado dentro del registro general: estadísticas agregadas — PyPI.