google/pyglove tiene un índice de salud de 68 sobre 100, lo que lo sitúa en la banda Moderado. Su puntuación más alta es Community & Adoption (74/100) y la más baja, AI Readiness (30/100). Se actualizó por última vez hace 4 días. Una sola persona concentra la mayor parte del trabajo reciente.
68
global / 100
Moderado
Índice 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.
68
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.
Dependencias directas libres de avisos conocidos — ninguna dependencia directa tiene un aviso conocido
25/25
Dependencias indirectas libres de avisos conocidos — ninguna dependencia indirecta tiene un aviso conocido
0/40
Sin avisos pendientes — ningún aviso tiene fecha de publicación
Datos de entrada utilizados
source
osv
advisories
0
affected_packages
0
assessed_packages
2
unassessed_packages
0
affected_by_severity
none
direct_affected_packages
0
Excluidos de la puntuación (sin datos o no aplicable): Sin avisos pendientes. Los pesos restantes se han renormalizado. Se cotejó el cierre de dependencias en tiempo de ejecución de pypi:pyglove@0.4.5 —lo que arrastra la instalación del paquete publicado—: 2 paquetes. No se analiza la alcanzabilidad.
¿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.
Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
Más detalle
Historial de estrellas y forks 0 ★ / 39 ⇿
0Estrellas
39Forks
10Versiones
Cuándo se añadió cada estrella y fork, recopilado de GitHub y agrupado por día. El crecimiento acumulado se sitúa justo encima de las adiciones diarias que lo componen, de modo que ambos se leen en conjunto: la acumulación orgánica sostenida no se parece en nada a un pico abrupto y efímero. Cuando esa diferencia es medible, se informa como autenticidad del crecimiento.
Mayor 0Menor 3Parche 7
Cada punto abarca 4 días.
OpenSSF Scorecard 5.9 / 10
5.9agregado
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).Scorecard v5.5.0 · 2026-07-25 08:48 UTC
Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 0 paquetes directos y 10 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.
Registro
Paquete
Versión
Relación
PyPI
absl-py
—
indirecta
PyPI
autodocsumm
—
indirecta
PyPI
docstring-parser
—
indirecta
PyPI
fsspec
—
indirecta
PyPI
furo
—
indirecta
PyPI
jinja2
—
indirecta
PyPI
myst-nb
—
indirecta
PyPI
sphinx-autodoc-typehints
—
indirecta
PyPI
sphinx-copybutton
—
indirecta
PyPI
termcolor
—
indirecta
Avisos de dependencias 0
Instalar pypi:pyglove@0.4.5 arrastra 2 paquetes, directos y transitivos: 0 tienen avisos conocidos, de los cuales 0 son dependencias directas.
Ningún aviso conocido afecta a las dependencias evaluadas.
Un aviso significa que la versión registrada en el grafo de dependencias cae dentro del rango afectado de un aviso. No se analiza la alcanzabilidad, y el grafo incluye fijaciones de desarrollo y prueba: un hallazgo puede referirse al utillaje y no al software distribuido.
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"body": "With this change, `Dict.get()`, `Dict.values()`, `Dict.items()` will evaluate element value if the element is `pg.Inferential`. This also aligns the semantics with `List.__getitem__()` `List.__getattr__` and `List.__iter__()`.\n\nExample:\n```\nclass A(pg.Object):\n pass\n\nd = pg.Dict(x=pg.Ref(A())\nassert\n[…]\n # Instead of `pg.Ref`\nassert isinstance(list(d.values())[0], A) # Instead of `pg.Ref`\nassert isinstance(list(d.items())[0][1], A) # Instead of `pg.Ref`\n```\nPiperOrigin-RevId: 821342698",
"is_bot": false,
"headline": "`pg.Dict`: Evaluate inferential value for standard dict operations.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-10-19T16:33:23Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "ea13a507bbfbdf70c6beb0e2a1900df8145d7365",
"body": "PiperOrigin-RevId: 820474675",
"is_bot": false,
"headline": "`with_contextual_override` to support coroutines.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-10-17T01:43:10Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "779e9b1cf2fa8a779693d51cc12d62fe8a51c22c",
"body": "- `pg.ValueSpec.to_json_schema()` to support default value.\n- Introduce `pg.ValueSpec.from_json_schema()` / `pg.Schema.from_json_schema()` to allow creation of ValueSpec from JSON schema.\n\nPiperOrigin-RevId: 820048200",
"is_bot": false,
"headline": "`pg.typing`: Enhancements to JSON schema conversion.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-10-16T04:29:39Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "e7c8e81c40a225a8c4f0541acb3a9f936d769d9c",
"body": "* Counters are metrics that track the number of times an event occurs. It's\nmonotonically increasing over time.\n\n* Scalars are metrics that track a single value at a given time, for example,\navailable memory size. It does not accumulate over time like counters.\n\n* Distributions are metrics that track the distribution of a numerical value.\nFor example, the latency of an operation.\n\nPiperOrigin-RevId: 816976497",
"is_bot": false,
"headline": "Formalize monitoring metrics: Counter, Scalar and Distributions.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-10-09T03:14:09Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "4a1e8b45a3982a727f9abfbf87a2ef5882ccb055",
"body": "…duration`.\n\nPiperOrigin-RevId: 814384615",
"is_bot": false,
"headline": "[pg.monitoring] Handle empty error with `pg.monitoring.Scalar.record_…",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-10-02T21:22:12Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "ef07d6af69fcbeb6b983a5a458375326c0344ead",
"body": "PiperOrigin-RevId: 814368980",
"is_bot": false,
"headline": "[pg.monitoring] Scalar.record_duration to optionally track error.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-10-02T20:45:17Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "46304868d813ddcce575163a58d9579844f340da",
"body": "PiperOrigin-RevId: 814064958",
"is_bot": false,
"headline": "Add `pg.monitoring.Scalar.record_duration()`.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-10-02T05:02:07Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "e9edd54ca5d16218ec264e9636e86136d2e0afc4",
"body": "…() by providing an empty dict as a fallback.\n\nPiperOrigin-RevId: 813104024",
"is_bot": false,
"headline": "This change ensures that hyper_dict is not None before calling .items…",
"author_name": "PyGlove Authors",
"author_login": null,
"committed_at": "2025-09-30T06:11:34Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "5a187bafd52d2cc0843679f977633c2826a92991",
"body": "…ecution.\n\n```python\ncollection = pg.monitoring.metric_collection('/myapp')\nfoo = collection.get_counter('foo', 'description for foo')\nassert foo.increment() == 1\n\nbar = collection.get_scalar('bar', 'description for bar', parameters={'module': str})\nbar.record(10, module='module1')\nassert bar.distribution(module='module1').mean == 10.0\n```\nPiperOrigin-RevId: 811946930",
"is_bot": false,
"headline": "Introduce `pg.monitoring`: MetricCollection for monitoring program ex…",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-09-26T21:01:11Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "a4c17fe82eeee8bb84fa4d37484a0c51292f4396",
"body": "This allows user to redirect loggers to custom IOs.\n\nPiperOrigin-RevId: 811115465",
"is_bot": false,
"headline": "Introduce `pg.logging.use_stream`.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-09-25T01:18:17Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "c6819c42469f36e31eab2ff09d3f18c3dd33c8d1",
"body": "PiperOrigin-RevId: 805921677",
"is_bot": false,
"headline": "Extract `pg.match_error` from `pg.catch_errors`.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-09-11T18:34:36Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "c080ff49620cd427c7230fa5e9bdd24cb9582e55",
"body": "PiperOrigin-RevId: 786880724",
"is_bot": false,
"headline": "`JsonConvertible.add_module_alias` to support multiple aliases",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-07-24T23:47:52Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "5f38cad20384fdcc6eb848284e82e6934e2f1aaa",
"body": "PiperOrigin-RevId: 783503779",
"is_bot": false,
"headline": "Merge pull request #372 from google:daiyip-patch-1",
"author_name": "pyglove authors",
"author_login": null,
"committed_at": "2025-07-15T22:58:38Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "0f4c7f73dd41c9c814b94f0c374db1a30efc65e5",
"body": null,
"is_bot": false,
"headline": "Bump PyGlove dev version to 0.5.0",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-07-15T19:11:10Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "d655ea0f60c22add8a43fe3024ce1801bdc27e78",
"body": "PiperOrigin-RevId: 782393896",
"is_bot": false,
"headline": "Python 3.13 Support.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-07-12T20:43:34Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "4809eb5314420d1969c7528b43ac80a304fa9c92",
"body": null,
"is_bot": false,
"headline": "Python 3.13 support",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-07-12T17:33:49Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "976ecbfb2800d7119ef835feec8c37a00ac05419",
"body": "…ues=True`.\n\n`np.ndarray == <number>` will return a `np.ndarray` which does not evaluate to a bool value. By putting `field.default_value` on the left, the `__eq__` method of `field.default_value` will take higher priority than `np.ndarray.__eq__`.\n\nPiperOrigin-RevId: 761832755",
"is_bot": false,
"headline": "Make `pg.to_json` compatible with `np.ndarray` with `hide_default_val…",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-05-22T07:08:59Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "e55ea669df8dce6b2fdb1f92494da1ca080b7445",
"body": "PiperOrigin-RevId: 759654174",
"is_bot": false,
"headline": "Expose `pg.ErrorInfo`.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-05-16T16:57:29Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "28c9b51d35d4390ae68e0487d6428e7754d32272",
"body": "Previous pg.utils.ErrorInfo is not HTML friendly. To fix this issue, we introduce\nsymbolic ErrorInfo class. Besides HTML support, it inherits all the benefits of PyGlove objects, e.g. automatic serialization support, flexible formatting, symbolic traversal etc.\n\nThis CL also fix pytype errors for python files under docs.\n\nPiperOrigin-RevId: 759385485",
"is_bot": false,
"headline": "Introduce symbolic ErrorInfo.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-05-16T01:23:40Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "f7390c120b0c173ee73c97c239c7668def226723",
"body": "```python\nclass Foo(metadata=abc.ABCMeta):\n @property\n @abc.abstractmethod\n def bar(self):\n pass\n\nclass Bar(pg.Object, Foo):\n # `bar` will introduce property `bar` that overrides the abstract `Foo.bar`.\n bar: int\n\nassert Bar(1).bar == 1\n```\n\nPiperOrigin-RevId: 759224544",
"is_bot": false,
"headline": "Allow symbolic attribute to override abstract properties.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-05-15T18:18:01Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "9018f2589b4ef861a02100bb03b67e07187e5509",
"body": "```python\nclass A(pg.Object):\n x: int\n y: str\n\n# Schema to JSON schema.\nA.__schema__.to_json_schema()\n\n# ValueSpec to JSON schema.\nA.__schema__['x'].value.to_json_schema()\n```\n\nPiperOrigin-RevId: 754047380",
"is_bot": false,
"headline": "Add PyGlove schema to JSON Schema conversion.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-05-02T17:16:06Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "d1f94c9120ce50db9180f618bfbaf44a6c72a86e",
"body": "To make composition of child HTML elements easier, we make\n1. Html.element to support nestable list of child elements.\n2. LabelGroup to support nestable list of Labels that contains None.\n3. TabControl to support nestable list of Tabs that contains None. `select` supports name and name list.\n\nPiperOrigin-RevId: 752953428",
"is_bot": false,
"headline": "Enhancement to PyGlove HTML controls.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-04-30T01:03:02Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "cb9964e8651f363a93273dde0d1a1769ddca9f58",
"body": "Allow passing `is_noneable` and `use_none_as_default` arguments. We also unset the default value for Union converted from annotation without default value.\n\nPiperOrigin-RevId: 752777826",
"is_bot": false,
"headline": "Enhancing `pg.typing.ValueSpec.noneable()`",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-04-29T16:59:07Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "4b9c25fb57b76a1669cec783dca343e77fb37f05",
"body": "PiperOrigin-RevId: 752414465",
"is_bot": false,
"headline": "Automated Code Change",
"author_name": "Hana Joo",
"author_login": "h-joo",
"committed_at": "2025-04-28T19:55:38Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "9c0086afd0220248732973ed485749f3a70fde46",
"body": "…pers.\n\nPiperOrigin-RevId: 751851424",
"is_bot": false,
"headline": "Add `pg.logging.register_frame_to_skip` to better support logger wrap…",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-04-26T23:04:13Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "5f0aa14a8fa3e83d09cf605687f51150bea87dba",
"body": "Allow passing `is_noneable` and `use_none_as_default` arguments. We also unset the default value for Union converted from annotation without default value.\n\nPiperOrigin-RevId: 750812311",
"is_bot": false,
"headline": "Enhancing `pg.typing.ValueSpec.noneable()`",
"author_name": "Joe Richey",
"author_login": "josephlr",
"committed_at": "2025-04-24T02:31:01Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "9209f971c82736d5e569a933a4410dc8a4bbc121",
"body": "Allow passing `is_noneable` and `use_none_as_default` arguments. We also unset the default value for Union converted from annotation without default value.\n\nPiperOrigin-RevId: 750779137",
"is_bot": false,
"headline": "Enhancing `pg.typing.ValueSpec.noneable()`",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-04-24T00:24:21Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "29591ea564770b0080fb12aae15b6c4c60571fb9",
"body": "Also `pg.print` now supports variable positional arguments.\n\nPiperOrigin-RevId: 750352628",
"is_bot": false,
"headline": "`pg.format` to handle recursive structures.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-04-22T22:44:05Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "b3ad22583c65e630a5dfb8ff524789e91e19ced4",
"body": "This allows exceptions like SystemExit to be caught from bad code.\n\nPiperOrigin-RevId: 749911071",
"is_bot": false,
"headline": "Catch BaseException for pg.coding.evaluate.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-04-21T21:02:45Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "86aad872fc60c8c16ee64e8b8e414e0e6c68b3f2",
"body": "PiperOrigin-RevId: 740822511",
"is_bot": false,
"headline": "Internal change.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-03-26T17:37:04Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "fd75005974e56fd64e446633832afc949e7bef90",
"body": "PiperOrigin-RevId: 739010067",
"is_bot": false,
"headline": "Set the default file/sequence permissions to world readable.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-03-21T01:39:09Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "2e8981da77f89b8743659998b0a7336fa54d91f8",
"body": "… default.\n\nPiperOrigin-RevId: 736539531",
"is_bot": false,
"headline": "Set the default value of arg `exist_ok` for `pg.io.mkdirs` to True by…",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-03-13T16:50:06Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "65c7e520cb8aa0ca5c8bf7d7f1cc9ab72123d5b2",
"body": "PiperOrigin-RevId: 723558958",
"is_bot": false,
"headline": "Improve annotation inference to be compatible with module reloading.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-02-05T18:17:58Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "97d6446a2dea91c67f5f39335e0509573ccb8cdd",
"body": "With this CL, PyGlove is compatible with `__future__.annotations`.\nFor example:\n\n```python\nfrom __future__ import annotations\n\nclass A(pg.Object):\n child: A | None\n```\n\nPiperOrigin-RevId: 723114026",
"is_bot": false,
"headline": "pg.typing to support `__future__.annotations`.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-02-04T17:20:26Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "50d8a38b0328222c2570c95c93caf934b7354966",
"body": "Protocol is a special base class, which cannot be tested via `issubclass` and `isinstance` (without @typing.runtime_checkable). Before this change, PyGlove does not treat Protocol subclasses correctly for typing. This CL fixes this by:\n1) pg.typing.is_subclass(cls, protocol_cls) always returns True \n[…]\n\nclass Foo(Protocol):\n def __call__(self) -> int:\n ...\n\nclass A(pg.Object):\n foo: Foo\n\nclass Bar:\n def __call__(self) -> int:\n return 0\n\nA(Bar()) # Will work.\n```\n\nPiperOrigin-RevId: 722370929",
"is_bot": false,
"headline": "`pg.typing` to support Protocol subclassses.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-02-02T17:32:25Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "c8c5d1ca9e5a750ed828b07c0668bc56da30dd94",
"body": "… at the last expression.\n\nPiperOrigin-RevId: 721812914",
"is_bot": false,
"headline": "`pg.coding.evaluate`: Fix raise when attribute assignment takes place…",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-01-31T18:09:20Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "ddeb5932ba0c26cdc58e76f99800726bf644f974",
"body": "PiperOrigin-RevId: 718981795",
"is_bot": false,
"headline": "Fix stuck multiprocessing sandbox on large STDOUT.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-01-23T21:00:47Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "d31a11938dabb9f518cf41986e9d3e469b6161f7",
"body": "PiperOrigin-RevId: 716236530",
"is_bot": false,
"headline": "cleanup of deprecated test methods",
"author_name": "PyGlove Authors",
"author_login": null,
"committed_at": "2025-01-16T15:59:01Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "063caf94dd80587954d9206126e382760ace7d34",
"body": "…narios.\n\n```\nclass A(pg.Object):\n x: str\n\n def foo(self):\n return 1\n\nclass B(A):\n pass\n\nclass C(pg.Object):\n x: Literal['a', 'b']\n\n def foo(self):\n return 2\n\nclass D(B, C):\n pass\n\n# D.foo will be C.foo.\n# Like-wise, D.x shall be Literal['a', 'b'] as B doesn't override A.x.\n# Before this CL D.x is str (B takes precedence of C in multi-inheritance order).\n```\n\nPiperOrigin-RevId: 716220803",
"is_bot": false,
"headline": "Fix symbolic attribute inheritance behavior for multi-inheritance sce…",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-01-16T15:03:50Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "ba91fc783b079bc8fb783256a00d6c3bf0a7c670",
"body": "Contextual objects are objects whose attribute values could be manipulated by context manager `pg.contextual_override` and/or accessed from parent objects via `pg.contextual_attribute` placeholder.\n\nExample:\n\n```\nclass A(pg.ContextualObject):\n x: int\n y: Any = pg.contextual_attribute()\n\na = A(1)\nprint(a.x) # 1\nwith pg.contextual_override(x=2):\n print(a.x) # 2\nprint(a.x) #1\n\npg.Dict(y=3, a=a)\nprint(a.y) # 3. (Accessing parent's y)\n```\n\nPiperOrigin-RevId: 716013142",
"is_bot": false,
"headline": "Introduce `pg.ContextualObject`.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-01-16T01:35:43Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "264cfa133d400aeb3b6df548f8df6b317d0565d8",
"body": "With this change, the following code is valid, and the reported elapsed time for the 'task' status will be aggregated accordingly.\n\n```\n with pg.timeit() as t:\n with pg.timeit('task'):\n time.sleep(1)\n time.sleep(1)\n with pg.timeit('task'):\n time.sleep(1)\n print(t.status()['task'].elapse)\n # Will print a number around 2.\n```\n\nPiperOrigin-RevId: 715540752",
"is_bot": false,
"headline": "`pg.time` now supports aggregating sub-timers with the same name.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-01-14T22:53:02Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "20cf7607bb2bdb1edc8c51a039d3461a09ca7c8e",
"body": "PiperOrigin-RevId: 715083241",
"is_bot": false,
"headline": "Expose `lf.coding.permission` and `lf.coding.maybe_sandbox_call`.",
"author_name": "Daiyi Peng",
"author_login": "daiyip",
"committed_at": "2025-01-13T21:16:04Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "faca119a9c80361e8bbe4c0d91d23ddd4a3f8572",
"body": "PiperOrigin-RevId: 713385689",
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"band": "good",
"name": "Package maintenance",
"note": null,
"notes": [],
"value": 79,
"inputs": {
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"pyglove"
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"ecosystems": "pypi",
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"min_days_since_publish": 374
},
"components": [
{
"key": "published_resolvable",
"name": "Published & resolvable",
"detail": "1 package(s) on pypi",
"points": 25,
"status": "met",
"details": [
{
"code": "packages_published",
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"count": 1,
"ecosystems": "pypi"
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}
],
"max_points": 25
},
{
"key": "publish_recency",
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"detail": "latest publish 374 days ago",
"points": 14,
"status": "partial",
"details": [
{
"code": "publish_recency",
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}
],
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},
{
"key": "version_history",
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"detail": "1463 published versions",
"points": 20,
"status": "met",
"details": [
{
"code": "published_versions",
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"count": 1463
}
}
],
"max_points": 20
},
{
"key": "not_deprecated",
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"points": 20,
"status": "met",
"details": [
{
"code": "package_not_deprecated",
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}
],
"max_points": 20
}
]
}
],
"description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
},
{
"key": "engineering",
"band": "good",
"name": "Engineering Quality",
"value": 71,
"weight": 0.2,
"metrics": [
{
"key": "engineering_practices",
"band": "moderate",
"name": "Engineering practices",
"note": null,
"notes": [],
"value": 68,
"inputs": {
"has_ci": true,
"has_tests": true,
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"components": [
{
"key": "ci_workflows",
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"detail": "4 workflow(s)",
"points": 24,
"status": "met",
"details": [
{
"code": "ci_workflows",
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{
"key": "tests_present",
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"points": 24,
"status": "met",
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"max_points": 24
},
{
"key": "linter_config",
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"points": 0,
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{
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{
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"details": [],
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},
{
"key": "openssf_scorecard_ci_tests",
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"points": 20,
"status": "met",
"details": [],
"max_points": 20
}
]
},
{
"key": "documentation",
"band": "good",
"name": "Documentation",
"note": null,
"notes": [],
"value": 75,
"inputs": {
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"manipulation",
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"homepage": null,
"has_readme": true,
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"components": [
{
"key": "readme",
"name": "README",
"detail": null,
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{
"key": "documentation_directory",
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"points": 25,
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"details": [],
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{
"key": "documentation_homepage_site",
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"detail": null,
"points": 0,
"status": "missed",
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"max_points": 15
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{
"key": "repository_description",
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"detail": null,
"points": 10,
"status": "met",
"details": [],
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},
{
"key": "topics",
"name": "Topics",
"detail": "8 topics",
"points": 10,
"status": "met",
"details": [
{
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}
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{
"key": "wiki",
"name": "Wiki",
"detail": null,
"points": 0,
"status": "missed",
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]
}
],
"description": "Are baseline engineering and documentation practices in place?"
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{
"key": "security",
"band": "moderate",
"name": "Security",
"value": 67,
"weight": 0.16,
"metrics": [
{
"key": "security_posture",
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"notes": [
{
"code": "excluded_no_data",
"params": {
"components": [
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}
},
{
"code": "weights_renormalized",
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"value": 59,
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"components": [
{
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},
{
"key": "branch_protection",
"name": "Branch-Protection",
"detail": "internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
}
],
"max_points": 7.5
},
{
"key": "ci_tests",
"name": "CI-Tests",
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"points": 2.5,
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"max_points": 2.5
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{
"key": "cii_best_practices",
"name": "CII-Best-Practices",
"detail": "no effort to earn an OpenSSF best practices badge detected",
"points": 0,
"status": "missed",
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},
{
"key": "code_review",
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"points": 2.2,
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{
"key": "contributors",
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"points": 0.8,
"status": "partial",
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{
"key": "dangerous_workflow",
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"points": 10,
"status": "met",
"details": [],
"max_points": 10
},
{
"key": "dependency_update_tool",
"name": "Dependency-Update-Tool",
"detail": "no update tool detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "fuzzing",
"name": "Fuzzing",
"detail": "project is not fuzzed",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "license",
"name": "License",
"detail": "license file detected",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "11 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 9",
"points": 6.8,
"status": "partial",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
"name": "Packaging",
"detail": "packaging workflow not detected",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
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],
"max_points": 5
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{
"key": "pinned_dependencies",
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"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 run on all commits",
"points": 5,
"status": "met",
"details": [],
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{
"key": "security_policy",
"name": "Security-Policy",
"detail": "security policy file detected",
"points": 5,
"status": "met",
"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": "0 existing vulnerabilities detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
}
]
},
{
"key": "dependency_advisories",
"band": "excellent",
"name": "Dependency advisories",
"note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:pyglove@0.4.5 runtime dependency closure — what installing the published package pulls in — 2 packages. Reachability is not analyzed.",
"notes": [
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"code": "excluded_no_data",
"params": {
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},
{
"code": "weights_renormalized",
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{
"code": "advisories_scope_published",
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},
{
"code": "advisories_reachability",
"params": {}
}
],
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"inputs": {
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"advisories": 0,
"affected_packages": 0,
"assessed_packages": 2,
"unassessed_packages": 0,
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"direct_affected_packages": 0
},
"components": [
{
"key": "direct_dependencies_free_of_known_advisories",
"name": "Direct dependencies free of known advisories",
"detail": "no direct dependency carries a known advisory",
"points": 35,
"status": "met",
"details": [
{
"code": "no_direct_advisories",
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],
"max_points": 35
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{
"key": "indirect_dependencies_free_of_known_advisories",
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"detail": "no indirect dependency carries a known advisory",
"points": 25,
"status": "met",
"details": [
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"code": "no_indirect_advisories",
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"max_points": 25
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{
"key": "no_advisories_left_outstanding",
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"points": 0,
"status": "excluded",
"details": [
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"code": "advisories_no_publication_date",
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],
"max_points": 40
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]
},
{
"key": "malicious_dependencies",
"band": "excellent",
"name": "Malicious dependencies",
"note": null,
"notes": [],
"value": 100,
"inputs": {
"source": "osv",
"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",
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"assessed_packages": 2,
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"direct_malicious_packages": 0,
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"components": [
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"points": 100,
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],
"max_points": 100
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]
},
{
"key": "high_risk_jurisdiction_exposure",
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"notes": [
{
"code": "jurisdiction_evidence_limits",
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],
"value": 100,
"inputs": {
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"components": [
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],
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]
}
],
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{
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"components": [
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{
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{
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"components": [
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"points": 0,
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{
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{
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{
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{
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{
"key": "automated_maintenance",
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],
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{
"key": "openssf_scorecard_pinned_dependencies",
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},
{
"key": "ai_code_legibility",
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"notes": [],
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],
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{
"key": "manageable_file_sizes",
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"points": 53.2,
"status": "partial",
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]
},
{
"key": "ai_interfaces",
"band": "at_risk",
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"note": null,
"notes": [],
"value": 40,
"inputs": {
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"components": [
{
"key": "api_schema_openapi_graphql_proto",
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"detail": null,
"points": 0,
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{
"key": "mcp_server",
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"points": 0,
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{
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"status": "met",
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"code": "file_list",
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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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],
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"warnings": [
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"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/g/google/pyglove.svg",
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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 v0.27.0 — metodología completa · wiki de métricas.
