Registro públicoExcepcional
Informe de salud del softwareesquema 0.27.0 · métricas 2.5.0 · 2026-07-29 00:57 UTC
fastmachinelearning / hls4ml
Machine learning on FPGAs using HLS
fastmachinelearning/hls4ml tiene un índice de salud de 93 sobre 100, lo que lo sitúa en la banda Excepcional. Su puntuación más alta es Engineering Quality (95/100) y la más baja, AI Readiness (50/100). Se actualizó por última vez hoy. 3 personas concentran la mayor parte del trabajo reciente.
93
global / 100Índice de salud del software
Las métricas se agrupan en categorías ponderadas sobre una escala estandarizada de 1 a 100. El resultado global parte de su media ponderada, calibrada contra la distribución del registro público para que las bandas tengan significado percentil; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe un límite «En riesgo» de 34.
93
Excepcional93-100El nivel más alto del registro (≈ el 5% superior); cumple prácticamente todos los criterios evaluados
Excelente80-92Sólido en todos los frentes; carencias menores
Bueno65-79Saludable; carencias limitadas y manejables
Moderado50-64Aceptable con carencias notables; se recomienda revisión
Débil35-49Debilidades sustanciales en varias áreas
En riesgo20-34Debilidades significativas; su adopción exige cautela
Crítico1-19Problemas 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.
El resultado global ponderado 79 se calibra a 93 en la escala publicada del índice (calibración del registro 2026-08-02).
Titularidad
Fast Machine Learning LabOrganización
270 seguidores61 repositorios públicosdesde oct 2017
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 |
|---|---|---|---|---|---|
| PyPI | hls4ml | 1.3.0 | 3335 | 16 | hace 130 días |
Métricas por categoría
Vitalidad
¿Está vivo el proyecto: se escribe código y se publican versiones?
86
Actividad de desarrollo
89Excelente
Cómo se puntúa
| 36/36 | Recencia de push — último push hace 0 días |
| 24.9/36 | Cadencia de commits — 36/52 semanas con commits |
| 18/18 | Volumen de commits — 121 commits en el último año |
| 10/10 | OpenSSF Scorecard: Maintained — 18 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
Datos de entrada utilizados
| commits_last_year | 121 |
| human_commit_share | 0,8 |
| days_since_last_push | 0 |
| active_weeks_last_year | 36 |
Disciplina de publicación
82Excelente
Cómo se puntúa
| 27/27 | Publica versiones — 20 versiones publicadas |
| 27/36 | Recencia de las versiones — última versión hace 130 días |
| 19.8/27 | Cadencia de publicación — una versión cada ~119 días |
| 0/10 | OpenSSF Scorecard: Signed-Releases — sin datos |
Datos de entrada utilizados
| releases_count | 20 |
| latest_release_tag | v1.3.0 |
| releases_from_tags | no |
| days_since_latest_release | 130 |
| mean_days_between_releases | 119 |
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
86Excelente
Cómo se puntúa
| 53.8/60 | Estrellas — 2081 estrellas |
| 23/25 | Forks — 572 forks |
| 9.5/15 | Observadores — 53 observadores |
Datos de entrada utilizados
| forks | 572 |
| stars | 2081 |
| watchers | 53 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Salud de la comunidad
77Bueno
Cómo se puntúa
| 22.5/22.5 | README |
| 22.5/22.5 | Licencia — licencia reconocida (Apache-2.0) |
| 18/18 | Guía CONTRIBUTING |
| 0/13.5 | 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 |
| readme_badges | — |
| has_contributing | sí |
| has_issue_template | no |
| has_code_of_conduct | no |
| readme_badge_services | — |
| has_pull_request_template | sí |
Adopción en el ecosistema (descargas)
59Moderado
Cómo se puntúa
| 47/80 | Descargas mensuales — 3335 descargas/mes en pypi |
| 0/20 | Dependientes en el registro — no lo informa este ecosistema |
Datos de entrada utilizados
| packages | hls4ml |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 3335 |
Excluidos de la puntuación (sin datos o no aplicable): Dependientes en el registro. Los pesos restantes se han renormalizado.
Sostenibilidad y Gobernanza
¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?
79
Cómo se puntúa
| 36/54 | Factor bus — la mitad de los commits recae en 3 contribuyente(s) |
| 15.5/22.5 | Distribución de commits — el principal contribuyente firma el 31% de los commits |
| 13.5/13.5 | Amplitud de contribuyentes — 75 contribuyentes |
| 10/10 | OpenSSF Scorecard: Contributors — project has 45 contributing companies or organizations |
Datos de entrada utilizados
| bus_factor | 3 |
| contributors_sampled | 75 |
| top_contributor_share | 0,309 |
Cómo se puntúa
| 25/42 | Resolución de issues — 60% de issues cerradas |
| 25.5/30 | Aceptación de PR — 656/771 PR decididos fusionados |
| 0/13 | Newcomer PR acceptance — ningún PR de un contribuyente primerizo decidido en 30 d |
| 15/15 | OpenSSF Scorecard: Code-Review — all changesets reviewed |
Datos de entrada utilizados
| merged_prs | 656 |
| open_issues | 196 |
| closed_issues | 288 |
| prs_merged_7d | — |
| prs_decided_7d | — |
| prs_merged_30d | — |
| prs_decided_30d | — |
| issue_closed_ratio | 0,595 |
| closed_unmerged_prs | 115 |
| first_time_authors_30d | — |
| first_time_prs_merged_30d | — |
| first_time_prs_decided_30d | — |
Excluidos de la puntuación (sin datos o no aplicable): newcomer_pr_acceptance. Los pesos restantes se han renormalizado.
Propiedad y custodia
72Bueno
Cómo se puntúa
| 30/30 | Respaldo de la propiedad — propiedad de una organización |
| 0/20 | Dominio verificado |
| 17.5/25 | Alcance del propietario — 270 seguidores de fastmachinelearning |
| 25/25 | Trayectoria — 61 repos públicos, cuenta de ~8 años |
Datos de entrada utilizados
| followers | 270 |
| owner_type | Organization |
| is_verified | — |
| owner_login | fastmachinelearning |
| public_repos | 61 |
| account_age_days | 3198 |
Mantenimiento de paquetes
100Excepcional
Cómo se puntúa
| 25/25 | Publicado y resoluble — 1 paquete(s) en pypi |
| 35/35 | Recencia de publicación — última publicación hace 130 días |
| 20/20 | Historial de versiones — 16 versiones en el registro |
| 20/20 | No obsoleto — activo, ni obsoleto ni retirado |
Datos de entrada utilizados
| packages | hls4ml |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 130 |
Calidad de Ingeniería
¿Existen unas prácticas mínimas de ingeniería y documentación?
95
Prácticas de ingeniería
92Excelente
Cómo se puntúa
| 24/24 | Flujos de trabajo de CI — 5 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 |
| 18/20 | OpenSSF Scorecard: CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9 |
Datos de entrada utilizados
| has_ci | sí |
| has_tests | sí |
| has_editorconfig | no |
| has_linter_config | sí |
| has_precommit_config | sí |
Documentación
100Excepcional
Cómo se puntúa
| 30/30 | README |
| 25/25 | Directorio de documentación |
| 15/15 | Sitio de documentación / página del proyecto — https://fastmachinelearning.org/hls4ml |
| 10/10 | Descripción del repositorio |
| 10/10 | Topics — 11 topics |
| 10/10 | Wiki |
Datos de entrada utilizados
| topics | hls, machine-learning, fpga, python, keras, pytorch, onnx, vivado, vivado-hls, neural-network, intel-hls |
| has_wiki | sí |
| homepage | https://fastmachinelearning.org/hls4ml |
| 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?
64
Postura de seguridad
55Moderado
Cómo se puntúa
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — sin datos |
| 2.2/2.5 | CI-Tests — 25 out of 26 merged PRs checked by a CI test -- score normalized to 9 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 7.5/7.5 | Code-Review — all changesets reviewed |
| 2.5/2.5 | Contributors — project has 45 contributing companies or organizations |
| 0/10 | Dangerous-Workflow — dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Licencia — license file detected |
| 7.5/7.5 | Maintained — 18 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 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — sin datos |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Datos de entrada utilizados
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 5,5 |
Excluidos de la puntuación (sin datos o no aplicable): branch_protection, signed_releases. Los pesos restantes se han renormalizado.
Avisos de dependencias
100Excepcional
Cómo se puntúa
| 35/35 | 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 | 5 |
| 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:hls4ml@1.3.0 —lo que arrastra la instalación del paquete publicado—: 5 paquetes. No se analiza la alcanzabilidad.
Preparación para IA
¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Tiene un peso deliberadamente pequeño (4%): las herramientas para agentes son una señal real de mantenimiento, pero un repositorio sin ninguna puede alcanzar igualmente 100/100.
50
Contexto y guía para agentes
37Débil
Cómo se puntúa
| 0/45 | Instrucciones para agentes — sin CLAUDE.md / AGENTS.md / reglas de editor |
| 0/15 | Documentación legible por máquinas (llms.txt) |
| 36.7/40 | Historial de commits legible — 55 de 80 commits humanos declaran su intención (asunto estructurado o cuerpo explicativo) |
Datos de entrada utilizados
| has_llms_txt | no |
| legible_history_share | 0,688 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Cómo se puntúa
| 18/18 | Arranque con un solo comando — docs/Makefile, hls4ml/templates/quartus/Makefile |
| 22/22 | Pruebas automatizadas |
| 11/11 | Configuración de lint / formato |
| 0/11 | Verificación estática de tipos |
| 0/10 | Entorno reproducible |
| 0/10 | Práctica demostrada con agentes — ningún commit con autoría de agente entre los últimos 100 |
| 8/8 | Mantenimiento automatizado — 1 de los últimos 100 commits son actualizaciones automáticas de dependencias |
| 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 | — |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | docs/Makefile, hls4ml/templates/quartus/Makefile |
| has_devcontainer | no |
| has_linter_config | sí |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0,01 |
Legibilidad del código para modelos
54Moderado
Cómo se puntúa
| 0/45 | Código verificable por tipos — Python sin configuración de verificación de tipos |
| 54.4/55 | Tamaños de archivo manejables — 7/644 archivos fuente de más de 60 KB |
Datos de entrada utilizados
| primary_language | Python |
| largest_source_bytes | 261.465 |
| source_files_sampled | 644 |
| oversized_source_files | 7 |
Datos clave
2081estrellas de GitHub
75contribuidores
121commits en los últimos 12 meses
0días desde el último push
20versiones publicadas
3factor bus
196issues abiertas
PyPIecosistemas de paquetes
Advertencias de recopilación de datos
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
Más detalle
Historial de estrellas y forks 0 ★ / 572 ⇿
0Estrellas
572Forks
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.
Mayor 1Menor 10Parche 6
Cada punto abarca 8 días.
OpenSSF Scorecard 5.5 / 10
5.5agregado
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 |
| n/d | Branch-Protection | 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 |
| 9 | CI-Tests | 25 out of 26 merged PRs checked by a CI test -- score normalized to 9 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 10 | Code-Review | all changesets reviewed |
| 10 | Contributors | project has 45 contributing companies or organizations |
| 0 | Dangerous-Workflow | dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 18 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 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 0 | Security-Policy | security policy file not detected |
| n/d | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Dependencias directas 5
| Registro | Paquete | Restricción de versión | Manifiesto |
|---|---|---|---|
| PyPI | h5py | — | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
| PyPI | pydigitalwavetools | ==1.1 | pyproject.toml |
| PyPI | pyyaml | — | pyproject.toml |
| PyPI | quantizers | — | pyproject.toml |
Todas las dependencias 25
Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 5 paquetes directos y 20 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.
| Registro | Paquete | Versión | Relación |
|---|---|---|---|
| PyPI | h5py | — | directa |
| PyPI | numpy | — | directa |
| PyPI | pydigitalwavetools | 1.1 | directa |
| PyPI | pyyaml | — | directa |
| PyPI | quantizers | — | directa |
| PyPI | da4ml | — | indirecta |
| PyPI | hgq | — | indirecta |
| PyPI | hgq2 | — | indirecta |
| PyPI | keras | 3.14.1 | indirecta |
| PyPI | keras-tuner | 1.1.3 | indirecta |
| PyPI | onnx | — | indirecta |
| PyPI | ortools | 9.4.1874 | indirecta |
| PyPI | pquant-ml | — | indirecta |
| PyPI | setuptools | — | indirecta |
| PyPI | setuptools-scm | — | indirecta |
| PyPI | sparsepixels | — | indirecta |
| PyPI | sphinx | — | indirecta |
| PyPI | sphinx-contributors | — | indirecta |
| PyPI | sphinx-github-changelog | — | indirecta |
| PyPI | sphinx-rtd-theme | — | indirecta |
| PyPI | sphinx-tabs | — | indirecta |
| PyPI | sympy | — | indirecta |
| PyPI | tensorflow | — | indirecta |
| PyPI | tensorflow-model-optimization | — | indirecta |
| PyPI | toposort | — | indirecta |
Avisos de dependencias 0
Instalar pypi:hls4ml@1.3.0 arrastra 5 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.
Informe JSON sin procesar legible por máquina
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{
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"note": null,
"notes": [],
"value": 77,
"inputs": {
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"readme_badges": null,
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{
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{
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{
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{
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{
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}
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{
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{
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"details": [],
"max_points": 10
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},
{
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"notes": [
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{
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}
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"status": "missed",
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{
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},
{
"key": "fuzzing",
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"detail": "project is not fuzzed",
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"status": "missed",
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{
"key": "license",
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{
"key": "maintained",
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"detail": "18 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10",
"points": 7.5,
"status": "met",
"details": [],
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{
"key": "packaging",
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"points": 5,
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},
{
"key": "pinned_dependencies",
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"status": "missed",
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{
"key": "sast",
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"status": "missed",
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{
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"status": "missed",
"details": [],
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},
{
"key": "signed_releases",
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"status": "excluded",
"details": [
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"code": "no_data",
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],
"max_points": 7.5
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{
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"detail": "detected GitHub workflow tokens with excessive permissions",
"points": 0,
"status": "missed",
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},
{
"key": "vulnerabilities",
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]
},
{
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"notes": [
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},
{
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{
"code": "advisories_scope_published",
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},
{
"code": "advisories_reachability",
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],
"value": 100,
"inputs": {
"source": "osv",
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"affected_packages": 0,
"assessed_packages": 5,
"unassessed_packages": 0,
"affected_by_severity": "none",
"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",
"params": {}
}
],
"max_points": 35
},
{
"key": "indirect_dependencies_free_of_known_advisories",
"name": "Indirect dependencies free of known advisories",
"detail": "no indirect dependency carries a known advisory",
"points": 25,
"status": "met",
"details": [
{
"code": "no_indirect_advisories",
"params": {}
}
],
"max_points": 25
},
{
"key": "no_advisories_left_outstanding",
"name": "No advisories left outstanding",
"detail": "no advisory carries a publication date",
"points": 0,
"status": "excluded",
"details": [
{
"code": "advisories_no_publication_date",
"params": {}
}
],
"max_points": 40
}
]
},
{
"key": "malicious_dependencies",
"band": "exceptional",
"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",
"packages": [],
"red_flag": false,
"assessed_packages": 5,
"malicious_packages": 0,
"direct_malicious_packages": 0,
"withdrawn_malicious_packages": 0,
"installable_malicious_packages": 0
},
"components": [
{
"key": "no_dependency_reported_as_a_malicious_package",
"name": "No dependency reported as a malicious package",
"detail": "no dependency is reported as a malicious package",
"points": 100,
"status": "met",
"details": [
{
"code": "no_malicious_dependencies",
"params": {}
}
],
"max_points": 100
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "exceptional",
"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"
],
"commit_weight_rule": {
"min_commits": 50,
"min_commit_share": 0.1
},
"review_only_matches": 0,
"below_threshold_exposures": [],
"assessed_self_published_locations": 5
},
"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": 50,
"weight": 0.04,
"metrics": [
{
"key": "ai_agent_context",
"band": "weak",
"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 37,
"inputs": {
"has_llms_txt": false,
"legible_history_share": 0.688,
"agent_instruction_files": [],
"agent_instruction_max_bytes": null
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "55 of 80 human commits state their intent (structured subject or explanatory body)",
"points": 36.7,
"status": "partial",
"details": [
{
"code": "legible_history",
"params": {
"legible": 55,
"sampled": 80
}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "moderate",
"name": "Verify loop (build / test / typecheck)",
"note": null,
"notes": [],
"value": 59,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [
"docs/Makefile",
"hls4ml/templates/quartus/Makefile"
],
"has_devcontainer": false,
"has_linter_config": true,
"typecheck_configs": [],
"agent_commit_share": 0,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0.01
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": "docs/Makefile, hls4ml/templates/quartus/Makefile",
"points": 18,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "docs/Makefile, hls4ml/templates/quartus/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": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no agent-authored commits among the last 100",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_authored_commits",
"params": {
"sampled": 100
}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "1 of the last 100 commits are automated dependency updates",
"points": 8,
"status": "met",
"details": [
{
"code": "dependency_bot_commits",
"params": {
"count": 1,
"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": 54,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 261465,
"source_files_sampled": 644,
"oversized_source_files": 7
},
"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": "7/644 source files over 60KB",
"points": 54.4,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 644,
"oversized": 7
}
}
],
"max_points": 55
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight: agent tooling is a real maintenance signal, but its absence must never gate the top of the scale (calibration saturates at raw 91, so 100/100 remains reachable with AI Readiness at zero)."
}
],
"classification": {
"top": [
"library"
],
"labels": [
"library"
],
"scores": {
"library": 6
},
"primary": "library",
"evidence": [
{
"tier": "distribution",
"label": "library",
"source": "registry:pypi",
"weight": 6
}
],
"artifacts": [],
"confidence": "medium",
"host_extension": false,
"runs_as_process": false,
"consumed_by_code": true
},
"metrics_version": "2.5.0"
},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token"
],
"report_type": "repository",
"generated_at": "2026-07-29T00:57:59.624569Z",
"schema_version": "0.27.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/f/fastmachinelearning/hls4ml.svg",
"full_name": "fastmachinelearning/hls4ml",
"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 v2.5.0, esquema v0.27.0 — metodología completa · wiki de métricas.
Cómo se sitúa un resultado dentro del registro general: estadísticas agregadas — PyPI.