aRustyDev/dotfiles

This repository also configures its own agents. See what dotfiles tells them →

1Stars on the repository
176Mods indexed here, across every type
4mo agoLast push, which is what freshness is scored on
noneNo LICENSE: all rights reserved, so bodies are not copied

aRustyDev/dotfiles

Skill Claude CodeCodex

Master ES6+ features including async/await, destructuring, spread operators, arrow functions, promises, modules, iterators, generators, and functional programming patterns for writing clean, efficient JavaScript code. Use when refactoring legacy code, implementing modern patterns, or optimizing JavaScript applications.

not rated 1 4mo ago A 64 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

Build production-ready Node.js backend services with Express/Fastify, implementing middleware patterns, error handling, authentication, database integration, and API design best practices. Use when creating Node.js servers, REST APIs, GraphQL backends, or microservices architectures.

not rated 1 4mo ago A 58 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

Master TypeScript's advanced type system including generics, conditional types, mapped types, template literals, and utility types for building type-safe applications. Use when implementing complex type logic, creating reusable type utilities, or ensuring compile-time type safety in TypeScript projects.

not rated 1 4mo ago A 57 tokens

gitops-workflow

28

aRustyDev/dotfiles

Skill Claude CodeCodex

Implement GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps practices, automating Kubernetes deployments, or setting up declarative infrastructure management.

not rated 1 4mo ago D 46 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

Design, organize, and manage Helm charts for templating and packaging Kubernetes applications with reusable configurations. Use when creating Helm charts, packaging Kubernetes applications, or implementing templated deployments.

not rated 1 4mo ago A 41 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

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.

not rated 1 4mo ago A 50 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

Implement Kubernetes security policies including NetworkPolicy, PodSecurityPolicy, and RBAC for production-grade security. Use when securing Kubernetes clusters, implementing network isolation, or enforcing pod security standards.

not rated 1 4mo ago A 43 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

Design LLM applications using the LangChain framework with agents, memory, and tool integration patterns. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.

not rated 1 4mo ago A 42 tokens

llm-evaluation

33

aRustyDev/dotfiles

Skill Claude CodeCodex

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

not rated 1 4mo ago A 40 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

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.

not rated 1 4mo ago A 41 tokens

rag-implementation

35

aRustyDev/dotfiles

Skill Claude CodeCodex

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

not rated 1 4mo ago A 49 tokens

aRustyDev/dotfiles

Skill Claude CodeCodex

Build 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.

not rated 1 4mo ago A 48 tokens

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