Informe de auditoría del skill
hyperloom-workload-optimizer Informe de auditoría.
Autonomously optimizes end-to-end LLM inference throughput on AMD Instinct GPUs and reports a validated gain, using the Hyperloom multi-agent optimizer. Given a model, framework, workload (TP/EP, concurrency, ISL/OSL, precision), an objective and a time budget, it explores per-workload which levers to pull (serving/config parameters and env, framework enablement and source patches, and hot GPU-kernel rewrites), benchmarks each candidate, and returns the optimization stack that produced the gain. Use when the user wants to make a model serve faster, raise tokens/sec or throughput, optimize or tune vLLM or SGLang on MI300X/MI325X/MI355X, run Hyperloom, run the kernel-agent, quantize-then-optimize with Quark, set up Hyperloom from scratch, or resume a Hyperloom session. Do not use to stand up a server for plain serving, diagnose a broken ROCm install, or run a one-off kernel/benchmark or trace analysis without the optimization loop.
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
395 estrellas de GitHub
Actividad de stars/forks
Advertencia57
395 estrellas y 39 forks; la actividad de issues no está disponible en los metadatos actuales
Mantenimiento reciente
Aprobado100
4 días 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
Fallido36
command execution surface, credential or environment access
Disponibilidad de instalación
Aprobado92
npx skills add amd/skills --skill hyperloom-workload-optimizer
Seguridad del comando de instalación
Aprobado92
Ruta estándar de paquete o instalación en tiempo de ejecución
Superficie de permisos
Fallido22
secrets or environment access, shell or command execution
Evidencia del repositorio
Aprobado86
https://github.com/amd/skills/tree/main/skills/hyperloom-workload-optimizer
Estado de revisión
Advertencia46
Falta aprobación de revisión por IA
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 amd/skills --skill hyperloom-workload-optimizer
Repositorio
88
https://github.com/amd/skills/tree/main/skills/hyperloom-workload-optimizer
Licencia
86
MIT
Mantenimiento
100
4 días desde el último push
Revisión por IA
55
Review approval is missing
Completitud de README/SKILL.md
86
Usable description available
Riesgo de dependencias
36
command execution surface, credential or environment access
Seguridad del comando de instalación
92
Ruta estándar de paquete o instalación en tiempo de ejecución
Superficie de permisos
22
secrets or environment access, shell or command execution
Actividad de stars/forks
57
395 estrellas y 39 forks; la actividad de issues no está disponible en los metadatos actuales
Adopción
68
395 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
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- Falta aprobación de revisión por IA
- 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, shell or command execution
- Stars/forks activity: 395 stars, 39 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
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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