Agent Skills Hub

subagent-driven-development

Execute implementation plans using isolated subagents for each task, featuring a rigorous two-stage review process for spec compliance and code quality.

laravel-11-12-app-guidelines

Standardized workflow and guidelines for Laravel 11/12 application development, including stack detection, dependency management, and integration with Laravel Boost tools.

cicd-pipeline-qe-orchestrator

Orchestrate end-to-end quality engineering across CI/CD pipelines, from commit-stage unit testing and shift-left strategies to production-stage synthetic monitoring and compliance gates.

mcp-builder

A guide for building high-quality MCP (Model Context Protocol) servers in Python or TypeScript to integrate external APIs and services into LLM workflows.

llm-integration

A toolkit for building robust LLM integrations: API patterns, streaming, function calling, RAG pipelines, and cost-effective model routing.

moai-essentials-debug

Advanced multi-language debugging support with stack trace analysis, runtime error triage, and automated diagnostic tools for containerized and distributed systems.

iterate

Autonomous improvement loop for codebase optimization. Automatically modifies, measures, and iterates on code based on a specific goal and mechanical metric.

maestro

Intelligent RAG-based gateway that routes coding tasks to specialized Swift/iOS expertise without context window bloat. Uses MCP to retrieve precise patterns from 100+ indexed skills.

gemini-api

Google Gemini Image Generation API interface for text-to-image, editing, style templates, and automated retry workflows.

llmintegration

Multi-model LLM integration patterns for Claude, GPT, Gemini, and Ollama. Features API handling, prompt engineering, token management, and model-agnostic orchestration.

context-optimization

Optimize agent context windows through KV-caching, observation masking, summarization-based compaction, and context partitioning to reduce costs and latency.

ReasoningBank Intelligence

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement in AI agents.