Registro público
Informe de salud del softwareesquema 0.27.0 · métricas 1.13.0 · 2026-07-27 16:56 UTC

mainsail-crew / spyglass

A simple mjpeg server for Picamera2

PythonGPL-3.0★ 104 estrellas⑂ 24 forksdesde ene 2023Ver en GitHub ↗

mainsail-crew/spyglass tiene un índice de salud de 65 sobre 100, lo que lo sitúa en la banda Moderado. Su puntuación más alta es Engineering Quality (85/100) y la más baja, Security (43/100). Se actualizó por última vez hace 4 días. Una sola persona concentra la mayor parte del trabajo reciente.

65
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.

65
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

Mainsail-CrewOrganización
343 seguidores21 repositorios públicosdesde oct 2020

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
PyPIspyglassapunta a otro repositorio; no se puntúa0.0.1-0

Métricas por categoría

Vitalidad

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

75Bueno · 22% del índice global
Cómo se puntúa
36/36Recencia de push — último push hace 4 días
8.3/36Cadencia de commits — 12/52 semanas con commits
15.4/18Volumen de commits — 51 commits en el último año
10/10OpenSSF Scorecard: Maintained — 21 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Datos de entrada utilizados
commits_last_year51
human_commit_share0,64
days_since_last_push4
active_weeks_last_year12
Cómo se puntúa
27/27Publica versiones — 21 versiones publicadas
36/36Recencia de las versiones — última versión hace 14 días
19.8/27Cadencia de publicación — una versión cada ~77,5 días
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Datos de entrada utilizados
releases_count21
latest_release_tagv0.19.0
releases_from_tagsno
days_since_latest_release14
mean_days_between_releases77,5

Comunidad y Adopción

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

58Moderado · 18% del índice global
Cómo se puntúa
32.7/60Estrellas — 104 estrellas
11.3/25Forks — 24 forks
4.3/15Observadores — 7 observadores
Datos de entrada utilizados
forks24
stars104
watchers7
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Cómo se puntúa
22.5/22.5README
22.5/22.5Licencia — licencia reconocida (GPL-3.0)
18/18Guía 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_contributing
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?

61Moderado · 24% del índice global
Cómo se puntúa
9/54Factor bus — la mitad de los commits recae en 1 contribuyente(s)
9.5/22.5Distribución de commits — el principal contribuyente firma el 58% de los commits
13.5/13.5Amplitud de contribuyentes — 10 contribuyentes
3/10OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3
Datos de entrada utilizados
bus_factor1
contributors_sampled10
top_contributor_share0,58
Cómo se puntúa
39.2/46.8Resolución de issues — 84% de issues cerradas
32/38.3Aceptación de PR — 107/128 PR decididos fusionados
12/15OpenSSF Scorecard: Code-Review — Found 26/30 approved changesets -- score normalized to 8
Datos de entrada utilizados
merged_prs107
open_issues5
closed_issues26
issue_closed_ratio0,839
closed_unmerged_prs21
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
18.2/25Alcance del propietario — 343 seguidores de mainsail-crew
21.3/25Trayectoria — 21 repos públicos, cuenta de ~5 años
Datos de entrada utilizados
followers343
owner_typeOrganization
is_verified
owner_loginmainsail-crew
public_repos21
account_age_days2106

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

85Excelente · 20% del índice global
Cómo se puntúa
24/24Flujos de trabajo de CI — 5 flujo(s) de trabajo
24/24Pruebas presentes
16/16Configuración de linter
9.6/9.6Hooks de pre-commit
6.4/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9
Datos de entrada utilizados
has_ci
has_tests
has_editorconfig
has_linter_config
has_precommit_config

Documentación

65Moderado
Cómo se puntúa
30/30README
25/25Directorio de documentación
0/15Sitio de documentación / página del proyecto
10/10Descripción del repositorio
0/10Topics
0/10Wiki
Datos de entrada utilizados
topics
has_wikino
homepage
has_readme
has_docs_dir
has_description

Seguridad

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

43En riesgo · 16% del índice global
Cómo se puntúa
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — sin datos
2.2/2.5CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
6/7.5Code-Review — Found 26/30 approved changesets -- score normalized to 8
0.8/2.5Contributors — project has 1 contributing companies or organizations -- score normalized to 3
0/10Dangerous-Workflow — dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Licencia — license file detected
7.5/7.5Maintained — 21 commit(s) and 0 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
6/7.5Vulnerabilities — 2 existing vulnerabilities detected
Datos de entrada utilizados
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4,3
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.

53Moderado · 0% del índice global
Cómo se puntúa
0/45Instrucciones para agentes — sin CLAUDE.md / AGENTS.md / reglas de editor
0/15Documentación legible por máquinas (llms.txt)
40/40Historial de commits legible — 64 de 64 commits humanos declaran su intención (asunto estructurado o cuerpo explicativo)
Datos de entrada utilizados
has_llms_txtno
legible_history_share1
agent_instruction_files
agent_instruction_max_bytes
Cómo se puntúa
18/18Arranque con un solo comando — Makefile
22/22Pruebas automatizadas
11/11Configuración de lint / formato
0/11Verificación estática de tipos
0/10Entorno reproducible
4/10Práctica demostrada con agentes — 2 de los últimos 100 commits con autoría o crédito de agente
8/8Mantenimiento automatizado — 19 de los últimos 100 commits son actualizaciones automáticas de dependencias
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Datos de entrada utilizados
has_nixno
has_tests
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_config
typecheck_configs
agent_commit_share0,02
toolchain_manifests
dependency_bot_commit_share0,19
Cómo se puntúa
0/45Código verificable por tipos — Python sin configuración de verificación de tipos
55/55Tamaños de archivo manejables — 0/23 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languagePython
largest_source_bytes13.538
source_files_sampled23
oversized_source_files0

Datos clave

104estrellas de GitHub
10contribuidores
51commits en los últimos 12 meses
4días desde el último push
21versiones publicadas
1factor bus
5issues abiertas
PyPIecosistemas de paquetes

Advertencias de recopilación de datos

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'spyglass' points at a different repository (http://github.com/Pringley/spyglass); excluded from ecosystem scoring
  • deps.dev does not index pypi:spyglass@0.0.1; advisories assessed against the repository dependency graph instead
  • No resolved dependencies carried a version and a supported ecosystem

Más detalle

Historial de estrellas y forks 0 ★ / 24 ⇿
0Estrellas
24Forks
20Versiones

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.

048121620242422023-012024-092026-05
Mayor 0Menor 9Parche 11

Cada punto abarca 4 días.

OpenSSF Scorecard 4.3 / 10
4.3agregado

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 · 2026-07-27 16:55 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
9CI-Tests25 out of 26 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
8Code-ReviewFound 26/30 approved changesets -- score normalized to 8
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
0Dangerous-Workflowdangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained21 commit(s) and 0 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
8Vulnerabilities2 existing vulnerabilities detected
Dependencias directas 1
RegistroPaqueteRestricción de versiónManifiesto
PyPIaiortc~=1.14.0pyproject.toml
Todas las dependencias 7

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 1 paquetes directos y 6 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
PyPIaiortcdirecta
PyPIcommitizenindirecta
PyPImockindirecta
PyPIpre-commitindirecta
PyPIpylintindirecta
PyPIpytestindirecta
PyPIpytest-mockindirecta
Avisos de dependencias sin evaluar

El cotejo de avisos no pudo ejecutarse para este informe: No resolved dependencies carried a version and a supported ecosystem

Informe JSON sin procesar legible por máquina
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                "key": "fuzzing",
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                "status": "missed",
                "details": [],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "2 of the last 100 commits agent-authored or agent-credited",
                "points": 4,
                "status": "partial",
                "details": [
                  {
                    "code": "agent_authored_commits",
                    "params": {
                      "count": 2,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "19 of the last 100 commits are automated dependency updates",
                "points": 8,
                "status": "met",
                "details": [
                  {
                    "code": "dependency_bot_commits",
                    "params": {
                      "count": 19,
                      "sampled": 100
                    }
                  }
                ],
                "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": 13538,
              "source_files_sampled": 23,
              "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": "0/23 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 23,
                      "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": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "pypi package 'spyglass' points at a different repository (http://github.com/Pringley/spyglass); excluded from ecosystem scoring",
    "deps.dev does not index pypi:spyglass@0.0.1; advisories assessed against the repository dependency graph instead",
    "No resolved dependencies carried a version and a supported ecosystem"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-27T16:56:05.311678Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/mainsail-crew/spyglass.svg",
  "full_name": "mainsail-crew/spyglass",
  "license_state": "standard",
  "license_spdx": "GPL-3.0"
}

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 v0.27.0 — metodología completa · wiki de métricas.

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