Agent Skills Hub

pr-checks

Automate PR quality checks by reviewing CodeRabbit comments, validating PR descriptions, running pre-commit hooks, and executing test suites.

doc-coauthoring

A structured workflow for co-authoring documentation, technical specs, and proposals, guiding users through context gathering, collaborative refinement, and reader verification.

agent-architecture

Implement production-grade AI agents with LangGraph, tool-calling guardrails, SSE streaming, and episodic memory. Includes anti-patterns, fix pairs, and stateful architecture patterns.

product-management

AI-native product management tool for startups. Features automated competitor research, gap analysis using the WINNING filter, PRD generation, and GitHub Issues integration for prioritized, signal-based roadmap planning.

ADK Developer

A comprehensive guide and reference for building, orchestrating, and deploying AI agents using the Google Agent Development Kit (ADK).

lading-optimize-submit

Automates the submission workflow for lading performance optimizations, including branch management, git commits, and PR creation.

api-spec-analyzer

Analyzes OpenAPI specifications to generate TypeScript interfaces, API service patterns, and implementation guidance for backend-integrated frontend development.

ring:using-pmo-team

A team of 6 specialist PMO agents for portfolio governance, resource planning, risk analysis, and executive reporting. Dispatch to handle complex multi-project oversight and strategic coordination.

git-commit

Execute git commits with conventional commit message analysis, intelligent file staging, and automated semantic message generation based on code diffs.

Skills Guide

A comprehensive framework for creating, structuring, and managing reusable AI Agent Skills to standardize instruction-driven workflows.

papi

Manage, search, and extract technical insights from a local paper database. Ideal for developers implementing academic research, verifying code against math, and grounding coding agents in scientific papers.

ai-llm-patterns

Anthropic Claude integration patterns: streaming, RAG with pgvector, tool use, model selection (Haiku/Sonnet/Opus), prompt caching, and cost management for AI-powered engineering.