Rapport d’audit du skill
hyperloom-workload-optimizer Rapport d’audit.
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 OpenAgentSkill
Trust Score OpenAgentSkill
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
Adoption GitHub
Info62
395 stars GitHub
Activité stars/forks
Avertissement57
395 stars et 39 forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Maintenance récente
Validé100
4 jours depuis le dernier push
Clarté de licence
Validé86
MIT
Complétude README/SKILL.md
Validé86
Les métadonnées incluent suffisamment de contexte d’usage et de workflow
Risque dépendances/runtime
Échoué36
command execution surface, credential or environment access
Disponibilité de l’installation
Validé92
npx skills add amd/skills --skill hyperloom-workload-optimizer
Sécurité de la commande d’installation
Validé92
Chemin d’installation standard de package ou runtime
Surface de permissions
Échoué22
secrets or environment access, shell or command execution
Preuve du dépôt
Validé86
https://github.com/amd/skills/tree/main/skills/hyperloom-workload-optimizer
État de revue
Avertissement46
L’approbation de revue IA est absente
Résultats prouvés par Agent
Info54
Pas encore de données de résultats Agent
Vérifications
Revue d’installation et d’adoption
Chemin d’installation
92
npx skills add amd/skills --skill hyperloom-workload-optimizer
Dépôt
88
https://github.com/amd/skills/tree/main/skills/hyperloom-workload-optimizer
Licence
86
MIT
Maintenance
100
4 jours depuis le dernier push
Revue IA
55
Review approval is missing
Complétude README/SKILL.md
86
Usable description available
Risque de dépendances
36
command execution surface, credential or environment access
Sécurité de la commande d’installation
92
Chemin d’installation standard de package ou runtime
Surface de permissions
22
secrets or environment access, shell or command execution
Activité stars/forks
57
395 stars et 39 forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles
Adoption
68
395 stars GitHub
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Avertissements
- 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
- L’approbation de revue IA est absente
- 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
Méthode
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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