Agent Skills Hub

ray-skill

Sends debugging data, logs, and visual output to the Ray desktop application via its local API for real-time developer feedback.

enterprise-agent-builder

Architects enterprise AI agents from structured specs, generating production-ready code, data flow diagrams, and platform-specific logic for ServiceNow, Salesforce, and Snowflake.

Verification & Quality Assurance

A robust verification and QA system for software agents featuring real-time truth scoring, automated code validation, and instant rollback capabilities to maintain high reliability.

Investigating Code Patterns

Systematically trace code flows, locate implementations, diagnose performance issues, and map system architecture to understand complex codebases.

Swarm Orchestration

Orchestrate multi-agent swarms using agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Ideal for building distributed AI systems and scaling complex development workflows.

ring:dev-sre

Gate 2 development cycle skill that validates observability implementation, including structured logging, OpenTelemetry tracing, and instrumentation coverage, without modifying code.

canvas

Interactive terminal UI toolkit for Claude Code. Spawn and control calendar, document, and flight booking interfaces directly within tmux panes.

subagents-orchestration-guide

Orchestrates multi-agent development workflows, managing task decomposition, requirement analysis, and quality assurance for complex software projects.

agent-sdk

Expert skill for building and maintaining AI agents using the Claude Agent SDK, covering architecture, tool integration, MCP servers, and agentic workflows.

vertesia-plugin

Framework for building Vertesia plugins with a dual tool-server and UI architecture, featuring Hot Module Replacement, build-tools, and asset management.

upstash-vector-db-skills

Upstash Vector DB setup, semantic search, namespaces, and embedding models. Ideal for building high-performance vector search features in Next.js 16/Vercel projects.

basic-usage

Essential guide to llmemory for document storage and search: installation, database setup with pgvector, document ingestion, hybrid/semantic retrieval, and building RAG systems with multi-tenant support.