Ryan Lewis ryanlewis

name

discovery

description

Interactive interview that deeply explores a plan, feature, decision, bug, architecture change, migration, or any topic until shared understanding is reached. Use PROACTIVELY when the user says "discovery", "grill me", "let's explore this", "help me think through", "what am I missing", "poke holes", "challenge this", "sanity check this", "before I start building", "flesh this out", "talk me through", "interview me about", "rubber duck this", "think this through with me", "I have an idea", "I'm thinking about", or describes a plan, decision, or approach they haven't fully explored yet. Also trigger when the user presents a half-formed idea and seems uncertain, or when they're about to jump into implementation on something that hasn't been properly examined. This skill asks probing questions across multiple dimensions — scope, constraints, edge cases, dependencies, trade-offs — until both sides have a complete, shared picture.

You are the interviewer. Your job is to ask sha

Ryan Lewis — work GPG key

For sending me encrypted things in a work context ([email protected]).

Key

Field	Value
Email	`[email protected]`
Key ID	`68A698ADACB3C08D`

Ryan Lewis — personal GPG key

For sending me encrypted things in a personal context ([email protected]).

Key

Field	Value
Email	`[email protected]` (also `[email protected]`)
Key ID	`C4CF8705BF891FBC`

Callout Rendering Comparison

GitHub-native alerts — no titles

Note

No title — baseline.

Tip

No title — baseline.

mermaid-clip

Render a Mermaid diagram from your clipboard to a PNG image and copy it back. Useful for quickly turning copied Mermaid source into shareable images for docs, Slack, presentations, etc.

macOS only — uses pbpaste and osascript for clipboard access.

Dependencies

brew install mermaid-cli

name	cursor-code-orchestrator
description	Agent that uses GPT-5 (via cursor-agent) for analysis and problem identification (code review), then returns insights to Claude for safe code implementation. Use it for getting a code review for iterative improvement and to get final quality checks before a feature can be considered finalized.
tools	Bash, Glob, Grep, Read, Edit
model	sonnet
color	purple

You are an elite AI orchestration specialist bridging Cursor and Claude for seamless code review and implementation workflows. Your expertise lies in coordinating multi-agent interactions to deliver comprehensive code analysis and actionable improvements.

Product Requirements Document: Figgo - Modern FIGlet Library for Go

Executive Summary

Product Vision

Build a high-performance, specification-compliant FIGlet text rendering library for Go that prioritizes production readiness, modern development practices, and developer experience.

Problem Statement

Existing Go FIGlet libraries suffer from:

Poor error handling (panic/fatal on invalid input)

Creating a PRD with AI Assistance: A Case Study

How we used AI to streamline Product Requirements Document creation for an MCP server proof of concept

Introduction

We all know that Product Requirements Documents (PRDs) are essential for any software project - they define expectations, technical specs, and success criteria. But let's be honest: creating comprehensive PRDs can be pretty time-consuming, especially when you're trying to make sure you haven't missed any technical details or edge cases.

This blog post walks through our real-world experience creating a PRD for an MCP (Model Context Protocol) dice roll server - a proof-of-concept project we needed to validate our organisation's adoption of MCP technology. What made this interesting was how we used AI assistance to speed up the whole process while keeping the quality high.

	{
	"allow": [
	"Bash(git status:*)",
	"Bash(git log:*)",
	"Bash(git diff:*)",
	"Bash(git show:*)",
	"Bash(git branch:*)",
	"Bash(git tag:*)",
	"Bash(git remote:*)",
	"Bash(git stash list:*)",

	const points = {
	'immortal': 7,
	'diamond': 6,
	'platinum': 5,
	'gold': 4,
	'silver': 3,
	'bronze': 2,
	'iron': 1,
	};