Agent Skills Hub

parallel-agents

Orchestrate complex workflows by coordinating multiple specialized AI agents for multi-perspective code analysis, feature implementation, and system-wide reviews.

implementation

Structured, template-driven workflow for end-to-end feature development including coding, automated testing, verification, and session-based improvement.

multi-agent-patterns

Architect multi-agent systems to overcome context limits, using patterns like supervisor, swarm, and hierarchical models to manage complex workflows.

plan

Structured 6-phase workflow for planning and implementing features, skills, and architectural changes with automated tool discovery and safety verification.

webapp-testing

Toolkit for testing local web applications using Playwright, featuring server lifecycle management, automated DOM inspection, and browser automation workflows.

chatkit-frontend

Implement production-ready AI chat interfaces using OpenAI ChatKit React components. Features include hook configuration, streaming, theming, conversation history, and custom tool integration for Next.js applications.

markdown-token-optimizer

Analyzes markdown files to identify token-wasting patterns, providing actionable suggestions to optimize documentation for LLM consumption and token efficiency.

customer-research

Multi-source research tool for customer inquiries, bug investigations, and account history synthesis with source attribution and confidence scoring.

deep-reading-analyst

A comprehensive framework for deep analysis of articles, papers, and long-form content using 10+ thinking models like SCQA, First Principles, and Systems Thinking.

jira-communication

Comprehensive Jira interaction suite for managing issues, sprints, boards, and worklogs via CLI. Supports searching, updating, transitioning, and attachment handling. Triggers on Jira URLs and issue keys.

skill-template

A standardized template for creating and documenting modular Agent Skills to ensure consistent, efficient context engineering across AI agent systems.

rag-implementation

Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.