gpu-ml-trainer
Specialized skill for ML training workflows on cloud GPUs. Fine-tune LLMs with LoRA/QLoRA, train image LoRAs, build classifiers, and run custom training jobs. Generates production-ready training pipelines with checkpointing, logging, and optimal GPU selection.
create-expert-skill
Create production-ready skills from expert knowledge. Extracts domain expertise and system ontologies, uses scripts for deterministic work, loads knowledge progressively. Use when building skills that must work reliably in production.
test-strategy
Production-grade test strategy skill with risk-based testing, coverage analysis, quality gates, and resource optimization
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
backend-patterns
Production-grade backend patterns for Node.js, Python, Go, and Java/Spring frameworks
dockerfile-basics
Learn Dockerfile fundamentals and best practices for building production-ready container images
ml-monitoring
Production-grade ML model monitoring, drift detection, and observability
prompt-engineering-patterns
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.
k8s-manifest-generator
Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.
promptup
Transform vague requirements into production-grade prompts using evidence-based principles. Diagnose prompt issues, define boundaries, and iterate to quality.
n8n-production-readiness
Dynamic tier system for right-sizing n8n workflow hardening. Use this skill on ANY n8n workflow request to determine appropriate validation, logging, and error handling levels. Adapts to user needs — from quick prototypes to mission-critical production systems.
reviewing-ai-papers
Analyze AI/ML technical content (papers, articles, blog posts) and extract actionable insights filtered through enterprise AI engineering lens. Use when user provides URL/document for AI/ML content analysis, asks to "review this paper", or mentions technical content in domains like RAG, embeddings, fine-tuning, prompt engineering, LLM deployment.
deployment-procedures
Production deployment principles and decision-making. Safe deployment workflows, rollback strategies, and verification. Teaches thinking, not scripts.
enterprise-readiness
Assess and enhance software projects for enterprise-grade security, quality, and automation. Use when evaluating projects for production readiness, implementing supply chain security (SLSA, signing, SBOMs), hardening CI/CD pipelines, or establishing quality gates. Aligned with OpenSSF Scorecard, Best Practices Badge (all levels), SLSA, and S2C2F. By Netresearch.
