Informe de auditoría del skill
research-proposal Informe de auditoría.
Use when the user has a research proposal (problem + proposed methodology + planned experiments) and wants it iteratively strengthened until it clears a passing grade. ScholarEval grades the proposal against the literature (Soundness + Contribution), a Judge scores that feedback 0-100 on a fixed rubric, and a Reviser rewrites the proposal to fix the worst points without diluting the research question; loops until the grade passes or the budget is hit. Not for generating a proposal from scratch, and not for running a literature survey on its own.
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
Info62
163 estrellas de GitHub
Actividad de stars/forks
Advertencia57
163 estrellas y 19 forks; la actividad de issues no está disponible en los metadatos actuales
Mantenimiento reciente
Info76
4 meses desde el último push
Claridad de licencia
Aprobado86
MIT
Completitud de README/SKILL.md
Aprobado86
Los metadatos incluyen suficiente contexto de uso y flujo de trabajo
Riesgo de dependencias/runtime
Info64
credential or environment access, network or browser surface
Disponibilidad de instalación
Aprobado92
npx skills add gaasher/Agent-Loop-Skills --skill research-proposal
Seguridad del comando de instalación
Aprobado92
Ruta estándar de paquete o instalación en tiempo de ejecución
Superficie de permisos
Fallido34
secrets or environment access, filesystem or document access
Evidencia del repositorio
Aprobado86
https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/research-proposal
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 gaasher/Agent-Loop-Skills --skill research-proposal
Repositorio
88
https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/research-proposal
Licencia
86
MIT
Mantenimiento
76
4 meses desde el último push
Revisión por IA
55
The skill instructs agents to retrieve and use literature content, but does not explicitly warn against prompt injection from retrieved web pages or paper snippets. A malicious source could attempt to steer agent behavior.
Completitud de README/SKILL.md
86
Usable description available
Riesgo de dependencias
64
credential or environment access, network or browser surface
Seguridad del comando de instalación
92
Ruta estándar de paquete o instalación en tiempo de ejecución
Superficie de permisos
34
secrets or environment access, filesystem or document access
Actividad de stars/forks
57
163 estrellas y 19 forks; la actividad de issues no está disponible en los metadatos actuales
Adopción
68
163 estrellas de GitHub
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Advertencias
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- The skill instructs agents to retrieve and use literature content, but does not explicitly warn against prompt injection from retrieved web pages or paper snippets. A malicious source could attempt to steer agent behavior.
- The skill relies on executing an external Python script (`lit_search.py`) and WebFetch fallbacks; there is no explicit sandboxing or validation of the script path or downloaded content.
- The SKILL.md is long and dense; some users may need more concrete examples of the full loop execution to avoid misconfiguration.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, filesystem or document access
- Stars/forks activity: 163 stars, 19 forks; issue activity unavailable in current metadata
- Permission surface: secrets or environment access, filesystem or document access
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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