Informe de auditoría del skill
eval-design Informe de auditoría.
Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.
Trust Score de OpenAgentSkill
Trust Score de OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adopción en GitHub
Info76
816 estrellas de GitHub
Actividad de stars/forks
Info71
816 estrellas y 65 forks; la actividad de issues no está disponible en los metadatos actuales
Mantenimiento reciente
Aprobado88
2 meses desde el último push
Claridad de licencia
Aprobado86
Apache-2.0
Completitud de README/SKILL.md
Aprobado86
Los metadatos incluyen suficiente contexto de uso y flujo de trabajo
Riesgo de dependencias/runtime
Info72
Superficie de ejecución de comandos
Disponibilidad de instalación
Aprobado92
npx skills add agentscope-ai/OpenJudge --skill eval-design
Seguridad del comando de instalación
Info68
dynamic command execution, standard package or runtime install path
Superficie de permisos
Info62
shell or command execution, filesystem or document access
Evidencia del repositorio
Aprobado86
https://github.com/agentscope-ai/OpenJudge/tree/main/skills/eval_pipeline/01-eval-design
Estado de revisión
Info66
Hay datos de revisión por IA disponibles
Resultados comprobados por Agent
Info54
Aún no hay datos de resultados del Agent
Comprobaciones
Revisión de instalación y adopción
Ruta de instalación
92
npx skills add agentscope-ai/OpenJudge --skill eval-design
Repositorio
88
https://github.com/agentscope-ai/OpenJudge/tree/main/skills/eval_pipeline/01-eval-design
Licencia
86
Apache-2.0
Mantenimiento
88
2 meses desde el último push
Revisión por IA
55
Minor inconsistency: SKILL.md states a 60/30/10 difficulty split, but the bundled coverage_check.py uses EXPECTED_STRATA = {'easy': 0.55, 'boundary': 0.30, 'adversarial': 0.10}. This could confuse users relying on the documented split.
Completitud de README/SKILL.md
86
Usable description available
Riesgo de dependencias
72
Superficie de ejecución de comandos
Seguridad del comando de instalación
68
dynamic command execution, standard package or runtime install path
Superficie de permisos
62
shell or command execution, filesystem or document access
Actividad de stars/forks
71
816 estrellas y 65 forks; la actividad de issues no está disponible en los metadatos actuales
Adopción
88
816 estrellas de GitHub
Advertencias
- Minor inconsistency: SKILL.md states a 60/30/10 difficulty split, but the bundled coverage_check.py uses EXPECTED_STRATA = {'easy': 0.55, 'boundary': 0.30, 'adversarial': 0.10}. This could confuse users relying on the documented split.
- Quality score needs review
Método
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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