Registro público
Informe de salud del softwareesquema 0.11.0 · métricas 1.13.0 · 2026-07-15 08:55 UTC

opendatalab / labelu

Open-source multimodal data annotation platform with AI auto-annotation support.

PythonApache-2.0★ 1621 estrellas⑂ 185 forksdesde oct 2022Ver en GitHub ↗

opendatalab/labelu tiene un índice de salud de 60 sobre 100, lo que lo sitúa en la banda Moderado. Su puntuación más alta es Vitality (77/100) y la más baja, Security (23/100). Se actualizó por última vez hace 2 días. Una sola persona concentra la mayor parte del trabajo reciente.

60
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.

60
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)
VitalidadComunidad yAdopciónSostenibilidady GobernanzaCalidad deIngenieríaSeguridadPreparaciónpara IA

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

OpenDataLabOrganización
2911 seguidores67 repositorios públicosdesde ene 2022

Este repositorio está respaldado por una organización: una custodia compartida y responsable que puede sobrevivir a cualquier mantenedor individual.

Ecosistemas de paquetes

RegistroPaqueteVersiónDescargas / mesVersionesÚltima publicación
PyPIlabelu1.5.4-125hace 2 días

Métricas por categoría

Vitalidad

¿Está vivo el proyecto: se escribe código y se publican versiones?

77Bueno · 22% del índice global
Cómo se puntúa
36/36Recencia de push — último push hace 2 días
6.9/36Cadencia de commits — 10/52 semanas con commits
15.6/18Volumen de commits — 54 commits en el último año
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
Datos de entrada utilizados
commits_last_year54
human_commit_share
days_since_last_push2
active_weeks_last_year10
Cómo se puntúa
27/27Publica versiones — 100 versiones publicadas
36/36Recencia de las versiones — última versión hace 2 días
27/27Cadencia de publicación — una versión cada ~9,8 días
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Datos de entrada utilizados
releases_count100
latest_release_tagv1.5.4
releases_from_tagsno
days_since_latest_release2
mean_days_between_releases9,8

Comunidad y Adopción

¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?

65Moderado · 18% del índice global
Cómo se puntúa
52.1/60Estrellas — 1621 estrellas
18.9/25Forks — 185 forks
6.8/15Observadores — 18 observadores
Datos de entrada utilizados
forks185
stars1621
watchers18
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Cómo se puntúa
22.5/22.5README
22.5/22.5Licencia — licencia reconocida (Apache-2.0)
0/18Guía CONTRIBUTING
0/13.5Código de conducta
0/7.2Plantilla de issues
0/6.3Plantilla de PR
Datos de entrada utilizados
has_readme
has_license
has_contributingno
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sostenibilidad y Gobernanza

¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?

69Moderado · 24% del índice global
Cómo se puntúa
9/54Factor bus — la mitad de los commits recae en 1 contribuyente(s)
9.2/22.5Distribución de commits — el principal contribuyente firma el 59% de los commits
13.5/13.5Amplitud de contribuyentes — 18 contribuyentes
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Datos de entrada utilizados
bus_factor1
contributors_sampled18
top_contributor_share0,59
Cómo se puntúa
31.7/46.8Resolución de issues — 68% de issues cerradas
34.7/38.3Aceptación de PR — 165/182 PR decididos fusionados
3/15OpenSSF Scorecard: Code-Review — Found 2/10 approved changesets -- score normalized to 2
Datos de entrada utilizados
merged_prs165
open_issues34
closed_issues72
issue_closed_ratio0,679
closed_unmerged_prs17
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
24.9/25Alcance del propietario — 2911 seguidores de opendatalab
22/25Trayectoria — 67 repos públicos, cuenta de ~4 años
Datos de entrada utilizados
followers2911
owner_typeOrganization
is_verified
owner_loginopendatalab
public_repos67
account_age_days1646
Cómo se puntúa
25/25Publicado y resoluble — 1 paquete(s) en pypi
35/35Recencia de publicación — última publicación hace 2 días
20/20Historial de versiones — 125 versiones en el registro
20/20No obsoleto — activo, ni obsoleto ni retirado
Datos de entrada utilizados
packageslabelu
ecosystemspypi
any_deprecatedno
min_days_since_publish2

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

55Moderado · 20% del índice global
Cómo se puntúa
24/24Flujos de trabajo de CI — 4 flujo(s) de trabajo
24/24Pruebas presentes
0/16Configuración de linter
0/9.6Hooks de pre-commit
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 0 out of 8 merged PRs checked by a CI test -- score normalized to 0
Datos de entrada utilizados
has_ci
has_tests
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentación

65Moderado
Cómo se puntúa
30/30README
0/25Directorio de documentación
15/15Sitio de documentación / página del proyecto — https://opendatalab.github.io/labelU/
10/10Descripción del repositorio
0/10Topics
10/10Wiki
Datos de entrada utilizados
topics
has_wiki
homepagehttps://opendatalab.github.io/labelU/
has_readme
has_docs_dirno
has_description

Seguridad

¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?

23Crítico · 16% del índice global
Cómo se puntúa
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — sin datos
0/2.5CI-Tests — 0 out of 8 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 2/10 approved changesets -- score normalized to 2
2.5/2.5Contributors — project has 6 contributing companies or organizations
0/10Dangerous-Workflow — dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Licencia — license file detected
7.5/7.5Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — sin datos
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 92 existing vulnerabilities detected
Datos de entrada utilizados
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate2,3
Excluidos de la puntuación (sin datos o no aplicable): branch_protection, packaging. 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.

55Moderado · 0% del índice global
Cómo se puntúa
45/45Instrucciones para agentes — CLAUDE.md
0/15Documentación legible por máquinas (llms.txt)
0/40Historial de commits legible — sin datos
Datos de entrada utilizados
has_llms_txtno
legible_history_share
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes3783
Excluidos de la puntuación (sin datos o no aplicable): Historial de commits legible. Los pesos restantes se han renormalizado.
Cómo se puntúa
0/18Arranque con un solo comando
22/22Pruebas automatizadas
0/11Configuración de lint / formato
0/11Verificación estática de tipos
10/10Entorno reproducible — Dockerfile, lockfile
0/10Práctica demostrada con agentes — sin datos
0/8Mantenimiento automatizado — sin datos
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Datos de entrada utilizados
has_nixno
has_tests
lockfilesuv.lock
has_dockerfile
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
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.
Cómo se puntúa
0/45Código verificable por tipos — Python sin configuración de verificación de tipos
55/55Tamaños de archivo manejables — 0/127 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languagePython
largest_source_bytes48.950
source_files_sampled127
oversized_source_files0

Datos clave

1621estrellas de GitHub
18contribuidores
54commits en los últimos 12 meses
2días desde el último push
100versiones publicadas
1factor bus
34issues abiertas
PyPIecosistemas de paquetes

Más detalle

OpenSSF Scorecard 2.3 / 10
2.3agregado

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 · 2026-07-15 08:54 UTC

10Binary-Artifactsno binaries found in the repo
n/dBranch-Protectioninternal 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
0CI-Tests0 out of 8 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 2/10 approved changesets -- score normalized to 2
10Contributorsproject has 6 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
n/dPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities92 existing vulnerabilities detected
Dependencias directas 21
RegistroPaqueteRestricción de versiónManifiesto
PyPIfastapi>=0.100.0pyproject.toml
PyPIloguru>=0.6.0pyproject.toml
PyPIsqlalchemy>=2.0.0pyproject.toml
PyPIboto3pyproject.toml
PyPIcryptography>=41.0.0pyproject.toml
PyPIpython-jose>=3.3.0pyproject.toml
PyPIpydantic>=2.0.0pyproject.toml
PyPIpydantic-settings>=2.0.0pyproject.toml
PyPItyper>=0.7.0pyproject.toml
PyPIuvicorn>=0.19.0pyproject.toml
PyPIemail-validator>=2.0.0pyproject.toml
PyPIpython-multipart>=0.0.5pyproject.toml
PyPIpython-dotenv>=0.21.0pyproject.toml
PyPIappdirs>=1.4.4pyproject.toml
PyPIaiofiles>=22.1.0pyproject.toml
PyPIpillow>=9.3.0pyproject.toml
PyPIalembic>=1.9.4pyproject.toml
PyPIhttpx>=0.27.0pyproject.toml
PyPItfrecord>=1.14.5pyproject.toml
PyPIwebsockets>=10.0.0pyproject.toml
PyPIbcrypt==4.3.0pyproject.toml
Todas las dependencias 86

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 25 paquetes directos y 61 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
PyPIaiofiles25.1.0directa
PyPIalembic1.18.4directa
PyPIappdirs1.4.4directa
PyPIbcrypt4.3.0directa
PyPIboto31.42.90directa
PyPIcryptography46.0.5directa
PyPIemail-validator2.3.0directa
PyPIfastapi0.125.0directa
PyPIfastapidirecta
PyPIhttpx0.28.1directa
PyPIhttpxdirecta
PyPIloguru0.7.3directa
PyPIpillow12.1.1directa
PyPIpillowdirecta
PyPIpydantic2.12.5directa
PyPIpydantic-settings2.13.1directa
PyPIpython-dotenv1.2.2directa
PyPIpython-jose3.5.0directa
PyPIpython-multipart0.0.22directa
PyPIsqlalchemy2.0.48directa
PyPItfrecord1.14.6directa
PyPItyper0.7.0directa
PyPIuvicorn0.42.0directa
PyPIuvicorndirecta
PyPIwebsockets16.0directa
PyPIannotated-doc0.0.4indirecta
PyPIannotated-types0.7.0indirecta
PyPIanyio4.12.1indirecta
PyPIblack26.3.1indirecta
PyPIbotocore1.42.90indirecta
PyPIcertifi2026.2.25indirecta
PyPIcffi2.0.0indirecta
PyPIcharset-normalizer3.4.6indirecta
PyPIclick8.3.1indirecta
PyPIcolorama0.4.6indirecta
PyPIcoverage7.13.5indirecta
PyPIcrc32c2.8indirecta
PyPIdnspython2.8.0indirecta
PyPIecdsa0.19.1indirecta
PyPIeinopsindirecta
PyPIflake87.3.0indirecta
PyPIgreenlet3.3.2indirecta
PyPIh110.16.0indirecta
PyPIhttpcore1.0.9indirecta
PyPIidna3.11indirecta
PyPIiniconfig2.3.0indirecta
PyPIjmespath1.1.0indirecta
PyPIlabelu1.3.6indirecta
PyPImako1.3.10indirecta
PyPImarkupsafe3.0.3indirecta
PyPImccabe0.7.0indirecta
PyPImypy-extensions1.1.0indirecta
PyPImysqlclient2.2.8indirecta
PyPInumpy2.4.3indirecta
PyPInumpyindirecta
PyPIopencv-python-headlessindirecta
PyPIpackaging26.0indirecta
PyPIpathspec1.0.4indirecta
PyPIplatformdirs4.9.4indirecta
PyPIpluggy1.6.0indirecta
PyPIprotobuf7.34.0indirecta
PyPIpyasn10.6.3indirecta
PyPIpycodestyle2.14.0indirecta
PyPIpycparser3.0indirecta
PyPIpydantic-core2.41.5indirecta
PyPIpyflakes3.4.0indirecta
PyPIpygments2.19.2indirecta
PyPIpytest9.0.2indirecta
PyPIpytest-cov7.0.0indirecta
PyPIpython-dateutil2.9.0.post0indirecta
PyPIpytokens0.4.1indirecta
PyPIrequests2.32.5indirecta
PyPIrsa4.9.1indirecta
PyPIs3transfer0.16.0indirecta
PyPIsam3indirecta
PyPIsix1.17.0indirecta
PyPIstarlette0.50.0indirecta
PyPItimmindirecta
PyPItomli2.4.0indirecta
PyPItorchindirecta
PyPItorchvisionindirecta
PyPItransformersindirecta
PyPItyping-extensions4.15.0indirecta
PyPItyping-inspection0.4.2indirecta
PyPIurllib32.6.3indirecta
PyPIwin32-setctime1.2.0indirecta
Informe JSON sin procesar legible por máquina
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                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "sast",
                "name": "SAST",
                "detail": "SAST tool is not run on all commits -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file not detected",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "Project has not signed or included provenance with any releases.",
                "points": 0,
                "status": "missed",
                "details": [],
                "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": "92 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": 8
            },
            "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": 55,
        "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.md"
              ],
              "agent_instruction_max_bytes": 3783
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "CLAUDE.md",
                "points": 45,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "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": "at_risk",
            "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": 39,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [
                "uv.lock"
              ],
              "has_dockerfile": true,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": false,
              "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": 0,
                "status": "missed",
                "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": "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": 48950,
              "source_files_sampled": 127,
              "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": "0/127 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 127,
                      "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": "2026-07-15T08:55:13.403497Z",
  "schema_version": "0.11.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/o/opendatalab/labelu.svg",
  "full_name": "opendatalab/labelu",
  "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 v0.11.0 — metodología completa · wiki de métricas.

Cómo se sitúa un resultado dentro del registro general: estadísticas agregadasPyPI.