Registro público
Informe de salud del softwareesquema 0.27.0 · métricas 1.13.0 · 2026-07-27 22:27 UTC

CompOmics / ms2rescore

Modular and user-friendly platform for AI-assisted rescoring of peptide identifications

PythonApache-2.0★ 66 estrellas⑂ 22 forksdesde ago 2017Ver en GitHub ↗

CompOmics/ms2rescore tiene un índice de salud de 72 sobre 100, lo que lo sitúa en la banda Bueno. Su puntuación más alta es Engineering Quality (90/100) y la más baja, AI Readiness (43/100). Se actualizó por última vez hace 3 días. 2 personas concentran la mayor parte del trabajo reciente.

72
global / 100
Bueno

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.

72
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

129 seguidores114 repositorios públicosdesde mar 2015

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

Ecosistemas de paquetes

RegistroPaqueteVersiónDescargas / mesVersionesÚltima publicaciónEtiquetas
PyPIms2rescore4.0.1-45hace 3 díasms2rescorems2pipdeeplcproteomicsmass-spectrometrypeptide-identificationrescoringmachine-learning

Métricas por categoría

Vitalidad

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

79Bueno · 22% del índice global
Cómo se puntúa
36/36Recencia de push — último push hace 3 días
13.2/36Cadencia de commits — 19/52 semanas con commits
18/18Volumen de commits — 210 commits en el último año
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
Datos de entrada utilizados
commits_last_year210
human_commit_share1
days_since_last_push3
active_weeks_last_year19
Cómo se puntúa
27/27Publica versiones — 43 versiones publicadas
36/36Recencia de las versiones — última versión hace 3 días
19.8/27Cadencia de publicación — una versión cada ~78,8 días
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Datos de entrada utilizados
releases_count43
latest_release_tagv4.0.1
releases_from_tagsno
days_since_latest_release3
mean_days_between_releases78,8

Comunidad y Adopción

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

56Moderado · 18% del índice global
Cómo se puntúa
29.4/60Estrellas — 66 estrellas
11/25Forks — 22 forks
3.9/15Observadores — 6 observadores
Datos de entrada utilizados
forks22
stars66
watchers6
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)
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?

73Bueno · 24% del índice global
Cómo se puntúa
25.2/54Factor bus — la mitad de los commits recae en 2 contribuyente(s)
11.5/22.5Distribución de commits — el principal contribuyente firma el 49% de los commits
9.5/13.5Amplitud de contribuyentes — 7 contribuyentes
10/10OpenSSF Scorecard: Contributors — project has 6 contributing companies or organizations
Datos de entrada utilizados
bus_factor2
contributors_sampled7
top_contributor_share0,489
Cómo se puntúa
41.4/46.8Resolución de issues — 89% de issues cerradas
32.1/38.3Aceptación de PR — 110/131 PR decididos fusionados
1.5/15OpenSSF Scorecard: Code-Review — Found 1/7 approved changesets -- score normalized to 1
Datos de entrada utilizados
merged_prs110
open_issues14
closed_issues109
issue_closed_ratio0,886
closed_unmerged_prs21
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
15.2/25Alcance del propietario — 129 seguidores de CompOmics
25/25Trayectoria — 114 repos públicos, cuenta de ~11 años
Datos de entrada utilizados
followers129
owner_typeOrganization
is_verified
owner_loginCompOmics
public_repos114
account_age_days4143
Cómo se puntúa
25/25Publicado y resoluble — 1 paquete(s) en pypi
35/35Recencia de publicación — última publicación hace 3 días
20/20Historial de versiones — 45 versiones en el registro
20/20No obsoleto — activo, ni obsoleto ni retirado
Datos de entrada utilizados
packagesms2rescore
ecosystemspypi
any_deprecatedno
min_days_since_publish3

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

90Excelente · 20% del índice global
Cómo se puntúa
24/24Flujos de trabajo de CI — 2 flujo(s) de trabajo
24/24Pruebas presentes
16/16Configuración de linter
9.6/9.6Hooks de pre-commit
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8
Datos de entrada utilizados
has_ci
has_tests
has_editorconfigno
has_linter_config
has_precommit_config

Documentación

90Excelente
Cómo se puntúa
30/30README
25/25Directorio de documentación
15/15Sitio de documentación / página del proyecto — https://ms2rescore.readthedocs.io
10/10Descripción del repositorio
10/10Topics — 5 topics
0/10Wiki
Datos de entrada utilizados
topicsproteomics, ms2pip, percolator, deeplc, peptide-identification
has_wikino
homepagehttps://ms2rescore.readthedocs.io
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?

58Moderado · 16% del índice global
Cómo se puntúa
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — sin datos
2/2.5CI-Tests — 4 out of 5 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-Review — Found 1/7 approved changesets -- score normalized to 1
2.5/2.5Contributors — project has 6 contributing companies or organizations
10/10Dangerous-Workflow — no 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 5 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
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
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Datos de entrada utilizados
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4,7
Excluidos de la puntuación (sin datos o no aplicable): branch_protection. Los pesos restantes se han renormalizado.
Cómo se puntúa
35/35Dependencias directas libres de avisos conocidos — ninguna dependencia directa tiene un aviso conocido
25/25Dependencias indirectas libres de avisos conocidos — ninguna dependencia indirecta tiene un aviso conocido
0/40Sin avisos pendientes — ningún aviso tiene fecha de publicación
Datos de entrada utilizados
sourceosv
advisories0
affected_packages0
assessed_packages69
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
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:ms2rescore@4.0.1 —lo que arrastra la instalación del paquete publicado—: 69 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? 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.

43En riesgo · 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)
32/40Historial de commits legible — 60 de 100 commits humanos declaran su intención (asunto estructurado o cuerpo explicativo)
Datos de entrada utilizados
has_llms_txtno
legible_history_share0,6
agent_instruction_files
agent_instruction_max_bytes
Cómo se puntúa
0/18Arranque con un solo comando
22/22Pruebas automatizadas
11/11Configuración de lint / formato
0/11Verificación estática de tipos
10/10Entorno reproducible — Dockerfile, lockfile
4/10Práctica demostrada con agentes — 2 de los últimos 100 commits con autoría o crédito de agente
0/8Mantenimiento automatizado — no se observan 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_tests
lockfilesuv.lock
has_dockerfile
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_config
typecheck_configs
agent_commit_share0,02
toolchain_manifests
dependency_bot_commit_share0
Cómo se puntúa
0/45Código verificable por tipos — Python sin configuración de verificación de tipos
53.9/55Tamaños de archivo manejables — 1/49 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languagePython
largest_source_bytes116.571
source_files_sampled49
oversized_source_files1
Cómo se puntúa
0/40Esquema de API (OpenAPI/GraphQL/proto)
0/20Servidor MCP
40/40Ejemplos ejecutables — examples, notebooks
Datos de entrada utilizados
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files

Datos clave

66estrellas de GitHub
7contribuidores
210commits en los últimos 12 meses
3días desde el último push
43versiones publicadas
2factor bus
14issues 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 ★ / 22 ⇿
0Estrellas
22Forks
41Versiones

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.

048121620242212019-082023-012026-06
Mayor 2Menor 3Parche 13

Cada punto abarca 7 días.

OpenSSF Scorecard 4.7 / 10
4.7agregado

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 22:27 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
8CI-Tests4 out of 5 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 1/7 approved changesets -- score normalized to 1
10Contributorsproject has 6 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow 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
10Vulnerabilities0 existing vulnerabilities detected
Dependencias directas 18
RegistroPaqueteRestricción de versiónManifiesto
PyPIcascade-config>=0.4.0pyproject.toml
PyPIclick>=7pyproject.toml
PyPIcustomtkinter>=5,<6pyproject.toml
PyPIdeeplc>=4.0.0pyproject.toml
PyPIim2deep>=2.0.1pyproject.toml
PyPIjinja2>=3pyproject.toml
PyPIlxml>=4.5pyproject.toml
PyPIms2pip>=4.2.0pyproject.toml
PyPIms2rescore_rs>=0.5.0pyproject.toml
PyPInumpy>=1.25,<3.0pyproject.toml
PyPIpackaging>=25.0pyproject.toml
PyPIpandas>=1pyproject.toml
PyPIplotly>=5pyproject.toml
PyPIpsm_utils>=1.1pyproject.toml
PyPIpyarrow>=14pyproject.toml
PyPIpyteomics>=4.7.2pyproject.toml
PyPIrich>=12pyproject.toml
PyPIristretto-ms>=0.3.0pyproject.toml
Todas las dependencias 210

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 19 paquetes directos y 191 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
PyPIcascade-config0.4.0directa
PyPIclick8.4.2directa
PyPIcustomtkinter5.2.2directa
PyPIdeeplc4.0.0directa
PyPIim2deep2.0.2directa
PyPIjinja23.1.6directa
PyPIlxml6.1.1directa
PyPIms2pip4.2.0directa
PyPIms2rescore-rs0.5.0directa
PyPInumpy2.4.6directa
PyPInumpy2.5.1directa
PyPIpackaging26.2directa
PyPIpandas2.3.3directa
PyPIplotly6.9.0directa
PyPIpsm-utils1.5.5directa
PyPIpyarrow25.0.0directa
PyPIpyteomics4.7.5directa
PyPIrich14.3.4directa
PyPIristretto-ms0.3.0directa
PyPIaiohappyeyeballs2.7.1indirecta
PyPIaiohttp3.14.3indirecta
PyPIaiosignal1.4.0indirecta
PyPIalabaster1.0.0indirecta
PyPIaltgraph0.17.5indirecta
PyPIannotated-types0.8.0indirecta
PyPIanyio4.14.2indirecta
PyPIappnope0.1.4indirecta
PyPIasttokens3.0.2indirecta
PyPIattrs26.1.0indirecta
PyPIbabel2.18.0indirecta
PyPIbeautifulsoup44.15.0indirecta
PyPIblack26.5.1indirecta
PyPIbleach6.4.0indirecta
PyPIcbor26.1.3indirecta
PyPIcertifi2026.7.22indirecta
PyPIcffi2.1.0indirecta
PyPIcfgv3.5.0indirecta
PyPIcharset-normalizer3.4.9indirecta
PyPIcolorama0.4.6indirecta
PyPIcomm0.2.3indirecta
PyPIcontourpy1.3.3indirecta
PyPIcoverage7.15.2indirecta
PyPIcuda-bindings13.3.1indirecta
PyPIcuda-pathfinder1.6.0indirecta
PyPIcuda-toolkit13.0.3.0indirecta
PyPIcycler0.12.1indirecta
PyPIdarkdetect0.8.0indirecta
PyPIdebugpy1.8.21indirecta
PyPIdecorator5.3.1indirecta
PyPIdefusedxml0.7.1indirecta
PyPIdistlib0.4.3indirecta
PyPIdocutils0.22.4indirecta
PyPIexecuting2.2.1indirecta
PyPIfastjsonschema2.21.2indirecta
PyPIfilelock3.32.0indirecta
PyPIfonttools4.63.0indirecta
PyPIfrozenlist1.8.0indirecta
PyPIfsspec2026.6.0indirecta
PyPIgreenlet3.5.4indirecta
PyPIh110.16.0indirecta
PyPIidentify2.6.19indirecta
PyPIidna3.18indirecta
PyPIimagesize2.0.0indirecta
PyPIiniconfig2.3.0indirecta
PyPIipykernel7.3.0indirecta
PyPIipython9.15.0indirecta
PyPIipython-pygments-lexers1.1.1indirecta
PyPIipywidgets8.1.8indirecta
PyPIjedi0.20.0indirecta
PyPIjoblib1.5.3indirecta
PyPIjsonschema4.26.0indirecta
PyPIjsonschema-specifications2025.9.1indirecta
PyPIjupyter-client8.9.1indirecta
PyPIjupyter-core5.9.1indirecta
PyPIjupyterlab-pygments0.3.0indirecta
PyPIjupyterlab-widgets3.0.16indirecta
PyPIkiwisolver1.5.0indirecta
PyPIlightning2.6.5indirecta
PyPIlightning-utilities0.15.3indirecta
PyPImacholib1.16.4indirecta
PyPImarkdown-it-py4.2.0indirecta
PyPImarkupsafe3.0.3indirecta
PyPImatplotlib3.11.1indirecta
PyPImatplotlib-inline0.2.2indirecta
PyPImdit-py-plugins0.6.1indirecta
PyPImdurl0.1.2indirecta
PyPImistune3.3.4indirecta
PyPIml-dtypes0.5.4indirecta
PyPImpmath1.3.0indirecta
PyPIms2rescore4.0.1indirecta
PyPImultidict6.7.1indirecta
PyPImumble0.3.0indirecta
PyPImypy-extensions1.1.0indirecta
PyPImyst-parser5.1.0indirecta
PyPInarwhals2.24.0indirecta
PyPInbclient0.11.0indirecta
PyPInbconvert7.17.1indirecta
PyPInbformat5.10.4indirecta
PyPInbsphinx0.9.8indirecta
PyPInest-asyncio21.7.2indirecta
PyPInetworkx3.6.1indirecta
PyPInodeenv1.10.0indirecta
PyPInumpydoc1.10.0indirecta
PyPInvidia-cublas13.1.1.3indirecta
PyPInvidia-cuda-cupti13.0.85indirecta
PyPInvidia-cuda-nvrtc13.0.88indirecta
PyPInvidia-cuda-runtime13.0.96indirecta
PyPInvidia-cudnn-cu139.20.0.48indirecta
PyPInvidia-cufft12.0.0.61indirecta
PyPInvidia-cufile1.15.1.6indirecta
PyPInvidia-curand10.4.0.35indirecta
PyPInvidia-cusolver12.0.4.66indirecta
PyPInvidia-cusparse12.6.3.3indirecta
PyPInvidia-cusparselt-cu130.8.1indirecta
PyPInvidia-nccl-cu122.30.7indirecta
PyPInvidia-nccl-cu132.29.7indirecta
PyPInvidia-nvjitlink13.3.33indirecta
PyPInvidia-nvshmem-cu133.4.5indirecta
PyPInvidia-nvtx13.0.85indirecta
PyPIonnx1.22.0indirecta
PyPIonnx2torch1.5.15indirecta
PyPIpandocfilters1.5.1indirecta
PyPIparso0.8.7indirecta
PyPIpathspec1.1.1indirecta
PyPIpefile2024.8.26indirecta
PyPIpexpect4.9.0indirecta
PyPIpillow12.3.0indirecta
PyPIplatformdirs4.11.0indirecta
PyPIpluggy1.6.0indirecta
PyPIpre-commit4.6.1indirecta
PyPIprompt-toolkit3.0.52indirecta
PyPIpropcache0.5.2indirecta
PyPIprotobuf7.35.1indirecta
PyPIpsims1.3.6indirecta
PyPIpsutil7.2.2indirecta
PyPIptyprocess0.7.0indirecta
PyPIpure-eval0.2.3indirecta
PyPIpycparser3.0indirecta
PyPIpydantic2.13.4indirecta
PyPIpydantic-core2.46.4indirecta
PyPIpygments2.20.0indirecta
PyPIpyinstaller6.21.0indirecta
PyPIpyinstaller-hooks-contrib2026.6indirecta
PyPIpyopenms3.5.0indirecta
PyPIpyparsing3.3.2indirecta
PyPIpytest9.1.1indirecta
PyPIpytest-cov7.1.0indirecta
PyPIpython-dateutil2.9.0.post0indirecta
PyPIpython-discovery1.5.0indirecta
PyPIpytokens0.4.1indirecta
PyPIpytorch-lightning2.6.5indirecta
PyPIpytz2026.2indirecta
PyPIpywin32-ctypes0.2.3indirecta
PyPIpyyaml6.0.3indirecta
PyPIpyzmq27.1.0indirecta
PyPIreferencing0.37.0indirecta
PyPIrequests2.34.2indirecta
PyPIroman-numerals4.1.0indirecta
PyPIrpds-py2026.6.3indirecta
PyPIruff0.16.0indirecta
PyPIrustyms0.10.0indirecta
PyPIscikit-learn1.9.0indirecta
PyPIscipy1.17.1indirecta
PyPIscipy1.18.0indirecta
PyPIsemver3.0.4indirecta
PyPIsetuptools83.0.0indirecta
PyPIsix1.17.0indirecta
PyPIsnowballstemmer3.1.1indirecta
PyPIsoupsieve2.9.1indirecta
PyPIsphinx9.0.4indirecta
PyPIsphinx9.1.0indirecta
PyPIsphinx-argparse0.6.0indirecta
PyPIsphinx-autobuild2025.8.25indirecta
PyPIsphinx-inline-tabs2025.12.21.14indirecta
PyPIsphinx-rtd-theme3.1.0indirecta
PyPIsphinxcontrib-applehelp2.0.0indirecta
PyPIsphinxcontrib-devhelp2.0.0indirecta
PyPIsphinxcontrib-htmlhelp2.1.0indirecta
PyPIsphinxcontrib-jquery4.1indirecta
PyPIsphinxcontrib-jsmath1.0.1indirecta
PyPIsphinxcontrib-qthelp2.0.0indirecta
PyPIsphinxcontrib-serializinghtml2.0.0indirecta
PyPIsqlalchemy2.0.51indirecta
PyPIstack-data0.6.3indirecta
PyPIstarlette1.3.1indirecta
PyPIsympy1.14.0indirecta
PyPIthreadpoolctl3.6.0indirecta
PyPItinycss21.5.1indirecta
PyPItomli2.4.1indirecta
PyPItorch2.13.0indirecta
PyPItorchmetrics1.9.0indirecta
PyPItorchvision0.28.0indirecta
PyPItornado6.5.7indirecta
PyPItqdm4.69.1indirecta
PyPItraitlets5.15.1indirecta
PyPItriton3.7.1indirecta
PyPItyping-extensions4.16.0indirecta
PyPItyping-inspection0.4.2indirecta
PyPItzdata2026.3indirecta
PyPIurllib32.7.0indirecta
PyPIuvicorn0.51.0indirecta
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Avisos de dependencias 0

Instalar pypi:ms2rescore@4.0.1 arrastra 69 paquetes, directos y transitivos: 0 tienen avisos conocidos, de los cuales 0 son dependencias directas.

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          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T16:26:02Z",
          "body_truncated": false,
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        },
        {
          "oid": "96eeeed076878e72273326cbdb1a240217db93f6",
          "body": "Fix /docs after ristretto integration",
          "is_bot": false,
          "headline": "Merge pull request #270 from CompOmics/fix/docs-after-ristretto",
          "author_name": "Ralf Gabriels",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T15:15:12Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ce23c8583bb8d235329e1a63f5ae4b8a3531ab18",
          "body": "… into fix/docs-after-ristretto",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T15:08:00Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ee8a1c83f5db86e7f7e977546b6569c2c3175707",
          "body": "Move package-level private rescoring functions to new module _ristretto_utils.py",
          "is_bot": false,
          "headline": "Merge pull request #269 from CompOmics/fix/rescoring-module",
          "author_name": "ArthurDeclercq",
          "author_login": "ArthurDeclercq",
          "committed_at": "2026-07-17T14:51:02Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "58f3c47bab9207a942233b5eadf5d550d75ecbe4",
          "body": null,
          "is_bot": false,
          "headline": "fix: correct test import",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T14:37:20Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ac1e427e50717701b80bd3a662a3acf789a487f4",
          "body": null,
          "is_bot": false,
          "headline": "docs: add rescoring module",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:57:10Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "0f1f5231272cc9de4003dca08104a053af2814c9",
          "body": "…s2rescore into fix/docs-after-ristretto",
          "is_bot": false,
          "headline": "Merge branch 'fix/rescoring-module' of https://github.com/CompOmics/m…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:54:18Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "e09fb2137af2ec341d131b98b317e2f5204cbc26",
          "body": null,
          "is_bot": false,
          "headline": "fix: update tests",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:52:38Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "adeb0ec94e5761aa92f9fb73f054eab2f4f61bc1",
          "body": null,
          "is_bot": false,
          "headline": "Move back some functions only used in rescoring.py",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:47:50Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "bcd475395ae8cc3e41c00a3336bb450cd5adbddd",
          "body": "… into fix/rescoring-module",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:39:43Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "7a52505fbf5d000501632f8e54153751a30aaa83",
          "body": "…to_utils.py",
          "is_bot": false,
          "headline": "Move package-level private rescoring functions to new module _ristret…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T13:39:33Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "45c45ae90c959631c34a17b0a5a73c997f10d302",
          "body": null,
          "is_bot": false,
          "headline": "end to end test",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T10:57:21Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "24fadb75b22e88d41258b33d973fe6524f07bc61",
          "body": null,
          "is_bot": false,
          "headline": "docs: update in-depth-python-api and userguide",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-17T10:23:12Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "0ab30b69551e98415c44dc7ed42af0991ad3719e",
          "body": "Replace mokapot rescoring engine with ristretto",
          "is_bot": false,
          "headline": "Merge pull request #268 from CompOmics/feat/add-ristretto",
          "author_name": "ArthurDeclercq",
          "author_login": "ArthurDeclercq",
          "committed_at": "2026-07-17T09:03:16Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "c255a3fcfd7fa6f8f0d4e05509e7dceba0ac7fdc",
          "body": null,
          "is_bot": false,
          "headline": "fix import error for python 3.12",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-17T08:40:04Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "59de3f31e2cda5fc086e12d11048967cb955805c",
          "body": "Review findings from the cumulative-diff review, addressed:\n\n- rescoring: null never actually disabled rescoring (CascadeConfig's\n  none_overrides_value=False silently ignores it), and the \"skip\n  rescoring\" path that was reachable only in principle would have\n  written no output at all. Rather than\n[…]\nreference).\n\nAlso reformats CHANGELOG.md's \"Breaking changes\" section (non-standard\nKeep a Changelog category) into the canonical Added/Changed/Removed\nsections, with breaking items tagged [BREAKING].",
          "is_bot": false,
          "headline": "Remove ability to skip rescoring; run infer_score_direction per-run",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T16:18:24Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "0f24e598a48cca9c51664b8af7140fcdd1e5711f",
          "body": "Cumulative summary of the mokapot -> ristretto rescoring engine\nmigration (report_fdr, model choice, auto score-direction inference,\noutput-file renames, GUI log.html fix, and the resulting config/API\nremovals), merged into the existing Unreleased section.",
          "is_bot": false,
          "headline": "Add ristretto migration entries to CHANGELOG",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:50:30Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "fba6411e330732d1456e1d74b3f18ec60e7bd5a1",
          "body": "…of-date",
          "is_bot": false,
          "headline": "docs: minor cleanup; temporarily remove overview figure as it is out-…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:34:56Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "95a9ee6928ed58e02447e5976d956eee0a0ea214",
          "body": "The GUI's logging setup only ever attached a plain FileHandler for\n<prefix>.log.txt -- unlike the CLI, it never used a recording Rich console\nor called save_html(), so <prefix>.log.html (documented as always written)\nwas silently never created for GUI-launched runs. Added the same\nConsole(record=Tru\n[…]\naises.\n\nAlso add an INFO-level log line when rescoring actually starts, since the\nper-fold/rollup/competition detail ristretto itself logs is moving to\nDEBUG level (fixed at the source, in ristretto).",
          "is_bot": false,
          "headline": "Fix missing HTML log file in GUI runs; log when rescoring starts",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T15:07:55Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "617cec085aabe9688be7903546a398aa21330356",
          "body": "…nal API surface\n\n- Fix rescore() always returning empty feature_weights/n_iterations: it\n  rebuilds the final result via ristretto.evaluate() (no training, so those\n  fields come back empty), discarding the real values from the earlier\n  ristretto.rescore() call. Now carried over explicitly, includ\n[…]\ne that explicit rather than presenting it as public API. Also\n  privatized build_features_dataframe and fix_constant_pep, which were\n  only ever called internally within the rescoring workflow itself.",
          "is_bot": false,
          "headline": "Fix empty feature weights, simplify output naming, and minimize inter…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T14:55:43Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "dc62a277fad5dce4ed1b70d317dd237a5f3bf71e",
          "body": "A user-provided rescoring dict (e.g. \"rescoring\": {} or {\"model\": \"lda\"})\nis passed through as-is by the config cascade -- same as feature_generators\nand psm_generator, where an override is taken at face value rather than\nmerged with defaults (CascadeConfig's max_recursion_depth stops it from\nreachi\n[…]\nig[\"rescoring\"] to ristretto.rescore()\nvia **kwargs instead of naming train_fdr/model explicitly, so ristretto's\nown defaults (train_fdr=0.01, model=\"svm\") apply to whatever the user's\nconfig omitted.",
          "is_bot": false,
          "headline": "Fix KeyError on partial/empty rescoring config",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T13:10:51Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "49104a38ba6de291222a2d0afb7b84d97f3c69a5",
          "body": "Clarifies alongside the existing null (skip rescoring) note that\nrescoring: {} is equivalent to omitting the key, since cascade_config\ndeep-merges schema defaults into a partial dict. Also fixes a stale\nmention that rescoring only exposed train_fdr (model was added earlier).",
          "is_bot": false,
          "headline": "Document that an empty rescoring object uses the schema defaults",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:51:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "aa7a2c013d105819b3e32cb160a4ee28f97ea26e",
          "body": "…e-ristretto-fixes",
          "is_bot": false,
          "headline": "Merge remote-tracking branch 'origin/feat/add-ristretto' into worktre…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:50:04Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "2610434c03c257e1a540b44e89ed151f902f327d",
          "body": "Adds a top-level report_fdr setting (default 0.01) used for console-logged\nidentification counts, HTML report stats/charts, and FlashLFQ output\nfiltering. This is independent of rescoring.train_fdr, which only controls\nristretto's semi-supervised training. Standalone report regeneration\n(ms2rescore-report) gains a --fdr option to override the report_fdr stored\nin the original run's full-config.json, without rerunning rescoring.",
          "is_bot": false,
          "headline": "Add report_fdr config option to replace hardcoded 1% FDR threshold",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:49:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "8d8aec14de9bd1e2498584cf2f6425f3adc136ac",
          "body": "… into feat/add-ristretto",
          "is_bot": false,
          "headline": "Merge branch 'release/4.0' of https://github.com/CompOmics/ms2rescore…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:35:36Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "7d8ed98782fb24870f9dd2259246c95dc9c28a3b",
          "body": "Add rescoring.model config option (svm, the default iterative\nPercolator-style SVM; or lda, a single-pass Fisher LDA that's faster\nbut less powerful on hard-to-separate datasets), threaded through to\nristretto.rescore()'s model kwarg. Adds a GUI dropdown alongside the\nexisting training-FDR field.\n\nR\n[…]\nthe jsonschema2md CLI's\npinned Python interpreter is missing on this machine (pre-existing\nenvironment issue, unrelated to this change) -- the generated\nsections were mirrored exactly by hand instead.",
          "is_bot": false,
          "headline": "Expose ristretto's model choice; always write rescoring tables",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:28:34Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "519a3bce4dadd38f3bbae60bbce0325c38d94e76",
          "body": "ristretto-ms>=0.3.0 is now on PyPI, so the local ../ironbrew editable\npath override is no longer needed. Removes the now-empty\n[tool.uv.sources] override and its TODO, and regenerates uv.lock to\nresolve ristretto-ms from the registry.",
          "is_bot": false,
          "headline": "Depend on published ristretto-ms instead of the local editable path",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T12:13:13Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "cfe700f86ce527f199509d02908d2949192a6e55",
          "body": "Rescoring result tables are now plain TSV (not Parquet) at every\nidentification level, and only the post-rescoring (\"after\") result is\nwritten -- \"before\" is reconstructed on demand from the main PSM\nlist's provenance data, so it's never persisted. Also documents the\nmax_psm_rank_output > 1 caveat (multi-rank rows, not statistically\nrigorous FDR) and drops the now-obsolete after_report paragraph.",
          "is_bot": false,
          "headline": "Update output-files.rst for the TSV-only, after-only rescoring tables",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T11:28:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "b153875ecc24fcf27c8c128467c138ad4cf65e6c",
          "body": "max_psm_rank_output is a special feature for surfacing ambiguous\nresults (e.g. mumble's multiple candidate peptidoforms per spectrum),\nnot for statistically rigorous FDR control when > 1 -- that trade-off\nis now explicit and applied consistently everywhere, instead of\ncomputing a separate always-ran\n[…]\nindexing returns the same underlying PSM\nobject references, not copies, so evaluate_before_from_provenance now\ntakes a real deep copy before mutating scores, to avoid corrupting\nthe caller's psm_list.",
          "is_bot": false,
          "headline": "Rework max_psm_rank_output and report reconstruction",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T11:27:32Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "6e425d19010c4fe6c40c969728a239c44523df98",
          "body": "build_features_dataframe now includes a \"run\" column, and every\nristretto.rescore()/evaluate() call passes run_col=\"run\" (requires\nristretto-ms>=0.3.0). Competition and CV splitting now group by\n(run, spectrum_id) at the source, instead of spectrum_id alone --\npreviously, two different physical spec\n[…]\nntirely: \"run\" now\ncomes back natively on every RescoreResult.psms from ristretto itself,\nso there's no more index-alignment hack needed on the ms2rescore side.\n\nBumps the ristretto-ms pin to >=0.3.0.",
          "is_bot": false,
          "headline": "Fix multi-run spectrum_id collisions via ristretto's new run_col",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:49:38Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "5a7706a3a105beba66ece6bb14458bddb7d61145",
          "body": "write_rescoring_tables() writes an additional set of *_after_report\nParquet tables when max_psm_rank_output > 1 (the rank-1 report view,\ndistinct from the multi-rank *_after files), which wasn't mentioned\nanywhere in the output-files reference.",
          "is_bot": false,
          "headline": "Document after_report Parquet output files",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:11:06Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "723a9a09d380ce588140dc97a62a400bd190202a",
          "body": "…n report\n\ncompute_id_stats/identification_overlap consumed before_result unmasked,\nunlike core.py's console-log count, which excludes mumble-generated\nalternate candidates via psm.metadata['original_psm']. Attach that\nmask onto before_result.psms itself (evaluate_before), the same way\nrun already i\n[…]\nfor that level.\n\nAdds direct unit coverage for both: _n_identified's masking behavior,\nand identification_overlap's run-based disambiguation (which the old\nbare-spectrum_id grouping would undercount).",
          "is_bot": false,
          "headline": "Apply mumble original_psm mask and run+spectrum_id key consistently i…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:10:35Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "96b8dc0ac4362a22dbcbb746b022ac17ce9245ee",
          "body": "fix_constant_pep previously only filtered result.psms, leaving\npeptidoform/peptide/protein rollups computed on the decoy-contaminated\npopulation -- _write_group_metadata would then look up stale\nscores/qvalues/PEPs for surviving PSMs. Now recomputes q-values, PEP,\nand rollups from scratch via ristre\n[…]\nt while the\nreal output looked fine. Split the fix into a result-only core\n(_fix_constant_pep_result) plus the existing psm_list-aware wrapper,\nand apply the core check to report_result independently.",
          "is_bot": false,
          "headline": "Refresh rollups in fix_constant_pep and apply it to report_result too",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-16T09:05:14Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "98c5d51e0dc150b5eddb4afd283f1d3f94a189d7",
          "body": null,
          "is_bot": false,
          "headline": "commit new lock file",
          "author_name": "ArthurDeclercq",
          "author_login": null,
          "committed_at": "2026-07-16T08:13:47Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "4726d4d10685d9d9181b5b9849d85e48ae357a0b",
          "body": "fix_constant_pep now takes/returns a (PSMList, RescoreResult) pair;\nrescore() returns (psm_list, after_result, after_report_result).\nRescoreResult.psms now always carries a run column. Adds coverage for\nthe new report_result (rank-1 view, distinct from a multi-rank final\nresult) and the after_report Parquet table written when they differ.\nAlso asserts rescoring_tables_unavailable is set when the ristretto\nParquet tables are missing.",
          "is_bot": false,
          "headline": "Update tests for rescore()'s 3-tuple return and RescoreResult schema",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:14:47Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "9abf1c917111df815ddc0c3c881fb1acf1663019",
          "body": "The \"Rescoring result tables\" table had inconsistent column-divider\npositions across its border/header/content rows (four different\nwidths), which docutils/Sphinx rejects as a malformed table. Rebuilt\nit with consistent column widths (verified programmatically: every\nrow shares identical +/| column positions).",
          "is_bot": false,
          "headline": "Fix malformed RST grid table in output-files.rst",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:11:37Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "f6af833dac16dc1bec5b28bc6c25b4b5aec45672",
          "body": "…p levels\n\ncreate_psm_dataframe: filter psm_list to rank==1 (the report is always\na single-best-PSM-per-spectrum view, regardless of max_psm_rank_output)\nand merge before/after by (run, spectrum_id) instead of (spectrum_id,\npeptidoform). The old peptidoform-keyed merge produced NaN\nscore_before/qval\n[…]\n after_report table (rank-1 view) over after (which may be\nmulti-rank) when both exist. The overview tab shows an explicit notice\nwhen tables are unavailable instead of silently rendering empty stats.",
          "is_bot": false,
          "headline": "Rework report before/after comparison to (run, spectrum_id) + 4 rollu…",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:09:00Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "27c4c4c98371b91736ed060ffb7cba8857223cf9",
          "body": "ristretto's RescoreResult schema has no room for a passthrough run\ncolumn, so attach it after the fact (index-aligned against the\noriginal PSMList) on before_result, final_result, and report_result.\nEnables merging before/after by (run, spectrum_id) in the report,\nguarding against spectrum_id collis\n[…]\nset when it differs from after (i.e. max_psm_rank_output > 1), so\nstandalone report regeneration (ms2rescore-report) can use the same\nalways-rank-1 view as a live run instead of the multi-rank result.",
          "is_bot": false,
          "headline": "Attach run to RescoreResult.psms; persist a separate after_report table",
          "author_name": "RalfG",
          "author_login": "RalfG",
          "committed_at": "2026-07-15T22:03:35Z",
          "body_truncated": true,
          "is_coding_agent": false
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                "key": "pinned_dependencies",
                "name": "Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 1",
                "points": 0.5,
                "status": "partial",
                "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": "0 existing vulnerabilities detected",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              }
            ]
          },
          {
            "key": "dependency_advisories",
            "band": "excellent",
            "name": "Dependency advisories",
            "note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:ms2rescore@4.0.1 runtime dependency closure — what installing the published package pulls in — 69 packages. Reachability is not analyzed.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "no_advisories_left_outstanding"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              },
              {
                "code": "advisories_scope_published",
                "params": {
                  "package": "pypi:ms2rescore@4.0.1",
                  "assessed": 69
                }
              },
              {
                "code": "advisories_reachability",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "source": "osv",
              "advisories": 0,
              "affected_packages": 0,
              "assessed_packages": 69,
              "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": "excellent",
            "name": "Malicious dependencies",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "source": "osv",
              "meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
              "packages": [],
              "red_flag": false,
              "assessed_packages": 69,
              "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": "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": 6
            },
            "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": "at_risk",
        "name": "AI Readiness",
        "value": 43,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "at_risk",
            "name": "Agent context & guidance",
            "note": null,
            "notes": [],
            "value": 32,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": 0.6,
              "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": "60 of 100 human commits state their intent (structured subject or explanatory body)",
                "points": 32,
                "status": "partial",
                "details": [
                  {
                    "code": "legible_history",
                    "params": {
                      "legible": 60,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "at_risk",
            "name": "Verify loop (build / test / typecheck)",
            "note": null,
            "notes": [],
            "value": 48,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [
                "uv.lock"
              ],
              "has_dockerfile": true,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": true,
              "typecheck_configs": [],
              "agent_commit_share": 0.02,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
            "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": 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": "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": "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": "no automated dependency updates observed",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_dependency_automation",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 1",
                "points": 1,
                "status": "partial",
                "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": 116571,
              "source_files_sampled": 49,
              "oversized_source_files": 1
            },
            "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": "1/49 source files over 60KB",
                "points": 53.9,
                "status": "partial",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 49,
                      "oversized": 1
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "examples",
                "notebooks"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "examples, notebooks",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "examples, notebooks"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "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"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-27T22:27:21.075230Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/c/CompOmics/ms2rescore.svg",
  "full_name": "CompOmics/ms2rescore",
  "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 v0.27.0 — metodología completa · wiki de métricas.

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