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performance-optimization

6
rockcodelabsrockcodelabs

Performance optimization patterns for kw-app - N+1 query prevention, eager loading, caching strategies, and database optimization.

194 days ago

rails-service-object

6
rockcodelabsrockcodelabs

Service object architecture for kw-app using dry-monads Result monad. Covers when to use services, structure patterns, testing, and integration with controllers.

194 days ago

dry-monads-patterns

6
rockcodelabsrockcodelabs

kw-app mandatory pattern for service objects using dry-monads Result monad with Success/Failure and do-notation. Replaces deprecated custom Result classes.

194 days ago

kamal-deployment

6
rockcodelabsrockcodelabs

Zero-downtime deployment with Kamal for kw-app (staging on Raspberry Pi ARM64, production on VPS x86_64). Covers deployment workflows, console access, and troubleshooting.

194 days ago

testing-standards

6
rockcodelabsrockcodelabs

kw-app testing standards using RSpec, FactoryBot, and Docker. Covers test types, patterns, and best practices for TDD workflow.

194 days ago

activerecord-patterns

6
rockcodelabsrockcodelabs

ActiveRecord model patterns and best practices for kw-app - validations, associations, scopes, queries, and when to keep models thin.

194 days ago

FAQ

AgentCC is a discovery hub for AI agent capabilities. We index Agent Skills as the primary dataset, and also organize MCP servers and selected AI tools so developers can quickly find, compare, and evaluate what to integrate into their workflows.
An Agent Skill is a reusable capability package for an AI agent. It can define workflows, tool usage patterns, domain knowledge, or execution rules that help the agent perform more reliably in a specific task or environment.
AgentCC is a resource and discovery layer, not a one-click installer. On each skill page, you should review the repository, file tree, install command, and usage notes, then integrate it according to the runtime or client you are using.
No. AgentCC primarily indexes external repositories and metadata. We help you understand what a skill is, where it comes from, and how it may be used, but execution and security decisions still belong to your own runtime environment.
No directory can guarantee absolute safety. AgentCC can help surface repository links, file structures, and metadata, but you should still verify permissions, external dependencies, API usage, and code quality before using a skill in production or on sensitive machines.
Yes. AgentCC is designed to be an evolving resource graph. As the submission and curation workflow matures, contributors will be able to recommend high-quality skills, MCP servers, and AI tools into the directory.
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