model-training
De la autor
Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.
Instalare
npx skills add seb1n/awesome-ai-agent-skills --skill model-training -a claude-code
Se instalează în .claude/skills/model-training/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 82
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- evaluating-machine-learning-modelsforyourhealth111-pixel/Vibe-SkillsEvaluate trained machine learning models with the right metrics and comparison logic. Use for benchmark review, threshold selection, calibration, validation, and model comparison; not for feature engineering or leakage auditing.Inginerie★ 3.8K
- explaining-machine-learning-modelsforyourhealth111-pixel/Vibe-SkillsExplain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.Productivitate★ 3.8K
- ml-pipeline-workflowwshobson/agentsBuild end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.Productivitate★ 41.6K
- AI/ML Model TestingPramodDutta/qaskillsTesting machine learning models including accuracy validation, bias detection, drift monitoring, A/B testing, and model regression testing.Testare★ 240
- planningawslabs/agent-pluginsDiscovers user intent and generates a structured, step-by-step plan for model customization workflows. This skill must always be activated alongside any other skill when the user's request relates to model customization — including fine-tuning, training, building, customizing, reviewing data, or getting advice on approach, regardless of domain. Do not skip this skill even if the immediate ask is narrow (e.g., reviewing data format or a single workflow step), because planning discovers the full scope of work needed. Also activate when the user wants to resume, continue, or modify an existing plan.Planificare★ 927
- model-deploymentseb1n/awesome-ai-agent-skillsDeploy trained machine learning models as production-ready services using REST APIs, containers, serverless functions, and orchestration platforms. Use when the user requests model deployment or provides relevant inputs for this workflow.DevOps★ 228
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