Registro público
Informe de salud del softwareesquema 0.34.0 · métricas 2.10.0 · 2026-09-19 04:24 UTC

Kiln-AI / Kiln

Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.

Python · Svelte · TypeScriptLicencia propia★ 5075 estrellas⑂ 380 forksdesde jul 2024Ver en GitHub ↗
TipoHerramienta de línea de comandosServidor MCPComplementoServicio de redcómo se determina
#ai#chain-of-thought#collaboration#dataset-generation#fine-tuning#machine-learning#macos#ml#ollama#openai#prompt#prompt-engineering#python#rlhf#synthetic-data#windows#evals#evaluation#evaluation-framework#mcp

Kiln-AI/Kiln tiene un índice de salud de 91 sobre 100, lo que lo sitúa en la banda Excelente. Su puntuación más alta es Vitality (95/100) y la más baja, Security (54/100). Se actualizó por última vez hoy. 2 personas concentran la mayor parte del trabajo reciente.

91
global / 100
Excelente

Índice 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.

91
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)
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.

El resultado global ponderado 77 se calibra a 91 en la escala publicada del índice (calibración del registro 2026-08-02).

Titularidad

Kiln AIOrganización
151 seguidores6 repositorios públicosdesde ago 2024

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
PyPIkiln-ai1.1.2111938hace 3 días
PyPIkiln-server1.1.251834hace 3 días

Métricas por categoría

Vitalidad

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

95Excepcional · 21% del índice global

Actividad de desarrollo

99Excepcional
Cómo se puntúa
36/36Recencia de push — último push hace 0 días
35.3/36Cadencia de commits — 51/52 semanas con commits
18/18Volumen de commits — 4287 commits en el último año
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Datos de entrada utilizados
commits_last_year4287
human_commit_share0,9
days_since_last_push0
active_weeks_last_year51

Disciplina de publicación

90Excelente
Cómo se puntúa
27/27Publica versiones — 35 versiones publicadas
36/36Recencia de las versiones — última versión hace 3 días
27/27Cadencia de publicación — una versión cada ~34 días
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Datos de entrada utilizados
releases_count35
latest_release_tagv1.1.3
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases34

Comunidad y Adopción

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

74Bueno · 17% del índice global

Popularidad y adopción

90Excelente
Cómo se puntúa
60/60Estrellas — 5075 estrellas
21.5/25Forks — 380 forks
8.6/15Observadores — 36 observadores
Datos de entrada utilizados
forks380
stars5075
watchers36
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Salud de la comunidad

71Bueno
Cómo se puntúa
22.5/22.5README
16.9/22.5Licencia — archivo de licencia presente, no es una licencia reconocida
18/18Guía CONTRIBUTING
0/13.5Código de conducta
0/7.2Plantilla de issues
6.3/6.3Plantilla de PR
Datos de entrada utilizados
has_readmesí
has_licensesí
readme_badges6
has_contributingsí
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templatesí

Adopción en el ecosistema (descargas)

54Moderado
Cómo se puntúa
42.9/80Descargas mensuales — 1637 descargas/mes en pypi
0/20Dependientes en el registro — no lo informa este ecosistema
Datos de entrada utilizados
packageskiln-ai, kiln-server
dependents—
ecosystemspypi
total_downloads—
monthly_downloads1637
unverified_packages_excluded—
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?

71Bueno · 23% del índice global

Resiliencia de mantenedores (factor bus)

61Moderado
Cómo se puntúa
25.2/54Factor bus — la mitad de los commits recae en 2 contribuyente(s)
11.9/22.5Distribución de commits — el principal contribuyente firma el 47% de los commits
13.5/13.5Amplitud de contribuyentes — 13 contribuyentes
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Datos de entrada utilizados
bus_factor2
contributors_sampled13
top_contributor_share0,47

Capacidad de respuesta a issues y PR

75Bueno
Cómo se puntúa
34.5/42Resolución de issues — 82% de issues cerradas
25.5/30Aceptación de PR — 1336/1574 PR decididos fusionados
4.3/13Newcomer PR acceptance — 1/3 PR de contribuyentes primerizos fusionados en 30 d
10.5/15OpenSSF Scorecard: Code-Review — Found 10/14 approved changesets -- score normalized to 7
Datos de entrada utilizados
merged_prs1336
open_issues24
closed_issues111
prs_merged_7d19
prs_decided_7d26
prs_merged_30d36
prs_decided_30d50
issue_closed_ratio0,822
closed_unmerged_prs238
first_time_authors_30d3
first_time_prs_merged_30d1
first_time_prs_decided_30d3

Propiedad y custodia

56Moderado
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
15.7/25Alcance del propietario — 151 seguidores de Kiln-AI
10.3/25Trayectoria — 6 repos públicos, cuenta de ~2 años
Datos de entrada utilizados
followers151
owner_typeOrganization
is_verifiedno
owner_loginKiln-AI
public_repos6
account_age_days762

Mantenimiento de paquetes

100Excepcional
Cómo se puntúa
25/25Publicado y resoluble — 2 paquete(s) en pypi
35/35Recencia de publicación — última publicación hace 3 días
20/20Historial de versiones — 38 versiones en el registro
20/20No obsoleto — activo, ni obsoleto ni retirado
Datos de entrada utilizados
packageskiln-ai, kiln-server
ecosystemspypi
any_deprecatedno
min_days_since_publish3

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

86Excelente · 19% del índice global

Prácticas de ingeniería

84Excelente
Cómo se puntúa
24/24Flujos de trabajo de CI — 12 flujo(s) de trabajo
24/24Pruebas presentes
16/16Configuración de linter — eslint.config.mjs, pyproject.toml ([tool.ruff])
0/9.6Hooks de pre-commit
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 13 out of 13 merged PRs checked by a CI test -- score normalized to 10
Datos de entrada utilizados
has_cisí
has_testssí
has_editorconfigno
has_linter_configsí
has_precommit_configno

Documentación

90Excelente
Cómo se puntúa
30/30README
25/25Directorio de documentación
15/15Sitio de documentación / página del proyecto — https://kiln.tech
10/10Descripción del repositorio
10/10Topics — 20 topics
0/10Wiki
Datos de entrada utilizados
topicsai, chain-of-thought, collaboration, dataset-generation, fine-tuning, machine-learning, macos, ml, ollama, openai, prompt, prompt-engineering, python, rlhf, synthetic-data, windows, evals, evaluation, evaluation-framework, mcp
has_wikino
homepagehttps://kiln.tech
docs_sitehttps://kiln.tech
has_readmesí
has_docs_dirsí
has_descriptionsí

Seguridad

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

54Moderado · 16% del índice global

Postura de seguridad

46Débil
Cómo se puntúa
7.5/7.5Binary-Artifacts — no binaries found in the repo
0.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 13 out of 13 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
5.2/7.5Code-Review — Found 10/14 approved changesets -- score normalized to 7
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5Licencia — license file detected
7.5/7.5Maintained — 30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — sin datos
0.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
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 — 134 existing vulnerabilities detected
Datos de entrada utilizados
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4,6
Excluidos de la puntuación (sin datos o no aplicable): Packaging. Los pesos restantes se han renormalizado.

Avisos de dependencias

86Excelente
Cómo se puntúa
35/35Dependencias directas libres de avisos conocidos — ninguna dependencia directa tiene un aviso conocido
11/25Dependencias indirectas libres de avisos conocidos — 1 afectados: nltk 3.10.3 (high 7.0)
40/40Sin avisos pendientes — ningún aviso lleva público más de 90 días
Datos de entrada utilizados
sourceosv
advisories1
affected_packages1
assessed_packages155
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages0
Se cotejó el cierre de dependencias en tiempo de ejecución de pypi:kiln-ai@1.1.2 —lo que arrastra la instalación del paquete publicado—: 155 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.

82Excelente · 4% del índice global

Contexto y guía para agentes

100Excepcional
Cómo se puntúa
45/45Instrucciones para agentes — AGENTS.md
15/15Documentación legible por máquinas (llms.txt) — llms.txt servido por el sitio web del proyecto (https://docs.kiln.tech/llms.txt)
40/40Historial de commits legible — 87 de 90 commits humanos declaran su intención (asunto estructurado o cuerpo explicativo)
Datos de entrada utilizados
has_llms_txtsí
llms_txt_urlhttps://docs.kiln.tech/llms.txt
legible_history_share0,967
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes4788

Bucle de verificación (compilación / pruebas / verificación de tipos)

91Excelente
Cómo se puntúa
18/18Arranque con un solo comando — Makefile
22/22Pruebas automatizadas
11/11Configuración de lint / formato — eslint.config.mjs, pyproject.toml ([tool.ruff])
11/11Verificación estática de tipos — app/desktop/studio_server/api_client/kiln_ai_server_client/py.typed, app/web_ui/tsconfig.json
10/10Entorno reproducible — lockfile
10/10Práctica demostrada con agentes — 45 de los últimos 100 commits con autoría o crédito de agente
8/8Mantenimiento automatizado — 10 de los últimos 100 commits son actualizaciones automáticas de dependencias
1/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 1
Datos de entrada utilizados
has_nixno
has_testssí
lockfilespackage-lock.json, uv.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configsí
typecheck_configsapp/desktop/studio_server/api_client/kiln_ai_server_client/py.typed, app/web_ui/tsconfig.json
agent_commit_share0,45
toolchain_manifests—
dependency_bot_commit_share0,1

Legibilidad del código para modelos

81Excelente
Cómo se puntúa
27/45Código verificable por tipos — Python con configuración de verificación de tipos (app/desktop/studio_server/api_client/kiln_ai_server_client/py.typed, app/web_ui/tsconfig.json)
53.6/55Tamaños de archivo manejables — 28/1092 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languagePython
largest_source_bytes720.888
source_files_sampled1092
oversized_source_files28

Interfaces legibles por máquina

20En riesgo
Cómo se puntúa
0/40Esquema de API (OpenAPI/GraphQL/proto)
20/20Servidor MCP
0/40Ejemplos ejecutables
Datos de entrada utilizados
example_dirs—
has_mcp_signalsí
api_schema_files—
interfaces_expected_ofnetwork-service

Datos clave

5075estrellas de GitHub
13contribuidores
4287commits en los últimos 12 meses
0días desde el último push
35versiones publicadas
2factor bus
24issues abiertas
PyPIecosistemas de paquetes

Advertencias de recopilación de datos

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'kiln-root' from its registry
  • Could not fetch pypi package 'kiln-studio-desktop' from its registry

Más detalle

Historial de estrellas y forks 0 ★ / 380 ⇿
0Estrellas
380Forks
28Versiones

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.

0100200300400380362024-112025-102026-09
Mayor 0Menor 12Parche 13

Cada punto abarca 2 días.

OpenSSF Scorecard 4.6 / 10
4.6agregado

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 · 2026-09-19 04:23 UTC

10Binary-Artifactsno binaries found in the repo
1Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests13 out of 13 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7Code-ReviewFound 10/14 approved changesets -- score normalized to 7
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
n/dPackagingpackaging workflow not detected
1Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 1
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
0Vulnerabilities134 existing vulnerabilities detected
Dependencias directas 63
RegistroPaqueteRestricción de versiónManifiesto
PyPIcertifi>=2024.8.30pyproject.toml
PyPIjq>=1.11.0pyproject.toml
PyPIkiln-ai==0.5.3pyproject.toml
PyPIkiln-server—pyproject.toml
PyPIkiln-studio-desktop—pyproject.toml
PyPIkiln-server—app/desktop/pyproject.toml
PyPIpillow>=11.0.0app/desktop/pyproject.toml
PyPIpystray>=0.19.5app/desktop/pyproject.toml
PyPIpyinstaller==6.22.3app/desktop/pyproject.toml
PyPIscipy>=1.15.2app/desktop/pyproject.toml
PyPIscipy>=1.16.0app/desktop/pyproject.toml
PyPIpygit2>=1.17.0,<2app/desktop/pyproject.toml
PyPIsentry-sdk>=2.0.0app/desktop/pyproject.toml
npm@codemirror/commands^6.10.3app/web_ui/package.json
npm@codemirror/lang-python^6.2.1app/web_ui/package.json
npm@codemirror/language^6.12.3app/web_ui/package.json
npm@codemirror/state^6.6.0app/web_ui/package.json
npm@codemirror/view^6.43.0app/web_ui/package.json
npm@floating-ui/dom^1.7.2app/web_ui/package.json
npm@kinde-oss/kinde-auth-pkce-js^4.3.0app/web_ui/package.json
npm@sentry/sveltekit^10.50.0app/web_ui/package.json
npmdompurify^3.4.13app/web_ui/package.json
npmecharts^6.1.0app/web_ui/package.json
npmhighlight.js^11.10.0app/web_ui/package.json
npmmarked^17.0.5app/web_ui/package.json
npmopenapi-fetch^0.12.2app/web_ui/package.json
npmposthog-js^1.184.2app/web_ui/package.json
PyPIboto3>=1.37.10libs/core/pyproject.toml
PyPIcoverage>=7.6.4libs/core/pyproject.toml
PyPIhttpx>=0.24.0libs/core/pyproject.toml
PyPIexceptiongroup>=1.0.0libs/core/pyproject.toml
PyPIgoogle-cloud-aiplatform>=1.84.0libs/core/pyproject.toml
PyPIgoogle-genai>=1.21.1libs/core/pyproject.toml
PyPIjinja2>=3.1.0libs/core/pyproject.toml
PyPIjq>=1.8.0libs/core/pyproject.toml
PyPIjsonschema>=4.23.0libs/core/pyproject.toml
PyPIlancedb>=0.24.2libs/core/pyproject.toml
PyPIopenai>=1.53.0libs/core/pyproject.toml
PyPIpdoc>=15.0.0libs/core/pyproject.toml
PyPIpydantic>=2.13.0libs/core/pyproject.toml
PyPIpypdf>=6.15.0libs/core/pyproject.toml
PyPIpytest-benchmark>=5.1.0libs/core/pyproject.toml
PyPIpytest-cov>=6.0.0libs/core/pyproject.toml
PyPIpyyaml>=6.0.2libs/core/pyproject.toml
PyPItogether—libs/core/pyproject.toml
PyPItyping-extensions>=4.12.2libs/core/pyproject.toml
PyPIvertexai>=1.43.0libs/core/pyproject.toml
PyPIllama-index>=0.13.3libs/core/pyproject.toml
PyPIanyio>=4.10.0libs/core/pyproject.toml
PyPIpypdfium2>=4.30.0libs/core/pyproject.toml
PyPIpillow>=11.1.0libs/core/pyproject.toml
PyPIllama-index-vector-stores-lancedb>=0.4.2libs/core/pyproject.toml
PyPImcp>=1.10.1libs/core/pyproject.toml
PyPItyper>=0.9.0libs/core/pyproject.toml
PyPIlitellm>=1.87.0,<1.88libs/core/pyproject.toml
PyPIfastapi>=0.115.4libs/server/pyproject.toml
PyPImcp>=1.10.1libs/server/pyproject.toml
PyPIhttpx>=0.27.2libs/server/pyproject.toml
PyPIkiln-ai>=1.1.2libs/server/pyproject.toml
PyPIpydantic>=2.9.2libs/server/pyproject.toml
PyPIpython-dotenv>=1.0.1libs/server/pyproject.toml
PyPIpython-multipart>=0.0.31libs/server/pyproject.toml
PyPIuvicorn>=0.32.0libs/server/pyproject.toml
Todas las dependencias 914

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 106 paquetes directos y 808 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
npm@codemirror/commands6.10.3directa
npm@codemirror/lang-python6.2.1directa
npm@codemirror/language6.12.3directa
npm@codemirror/state6.6.0directa
npm@codemirror/view6.43.0directa
npm@floating-ui/dom1.7.2directa
npm@kinde-oss/kinde-auth-pkce-js4.3.0directa
npm@sentry/sveltekit10.50.0directa
npmdompurify3.4.13directa
npmecharts6.1.0directa
npmhighlight.js11.10.0directa
npmmarked17.0.5directa
npmopenapi-fetch0.12.2directa
npmposthog-js1.258.3directa
PyPIanyio—directa
PyPIanyio4.10.0directa
PyPIanyio4.6.2.post1directa
PyPIboto3—directa
PyPIboto31.35.51directa
PyPIboto31.37.10directa
PyPIcertifi2024.8.30directa
PyPIcoverage—directa
PyPIcoverage7.6.4directa
PyPIexceptiongroup—directa
PyPIexceptiongroup1.2.2directa
PyPIfastapi—directa
PyPIfastapi0.115.4directa
PyPIgoogle-cloud-aiplatform—directa
PyPIgoogle-cloud-aiplatform1.136.0directa
PyPIgoogle-genai—directa
PyPIgoogle-genai1.61.0directa
PyPIhttpx—directa
PyPIhttpx0.27.2directa
PyPIhttpx0.28.1directa
PyPIjinja2—directa
PyPIjinja23.1.4directa
PyPIjinja23.1.6directa
PyPIjq—directa
PyPIjq1.11.0directa
PyPIjsonschema—directa
PyPIjsonschema4.23.0directa
PyPIkiln-ai—directa
PyPIkiln-ai0.5.1directa
PyPIkiln-ai1.1.2directa
PyPIkiln-server—directa
PyPIkiln-server0.1.0directa
PyPIkiln-server1.1.2directa
PyPIkiln-studio-desktop1.1.3directa
PyPIlancedb—directa
PyPIlancedb0.25.0directa
PyPIlitellm—directa
PyPIlitellm1.87.1directa
PyPIllama-index—directa
PyPIllama-index0.14.8directa
PyPIllama-index-vector-stores-lancedb—directa
PyPIllama-index-vector-stores-lancedb0.4.2directa
PyPImcp—directa
PyPImcp1.10.1directa
PyPIopenai—directa
PyPIopenai1.52.2directa
PyPIopenai2.41.0directa
PyPIpdoc—directa
PyPIpdoc15.0.0directa
PyPIpillow—directa
PyPIpillow11.1.0directa
PyPIpydantic—directa
PyPIpydantic2.13.3directa
PyPIpydantic2.9.2directa
PyPIpygit2—directa
PyPIpygit21.18.2directa
PyPIpyinstaller6.15.0directa
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Avisos de dependencias 1

Instalar pypi:kiln-ai@1.1.2 arrastra 155 paquetes, directos y transitivos: 1 tienen avisos conocidos, de los cuales 0 son dependencias directas.

PaqueteVersiónRelaciónGravedadAvisosCorregido en
nltk3.10.3indirectaalta1—

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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Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v2.10.0, esquema v0.34.0 — metodología completa · wiki de métricas.

Cómo se sitúa un resultado dentro del registro general: estadísticas agregadas — PyPI.