ryanlewis

Callout Rendering Comparison

GitHub-native alerts — no titles

Note

No title — baseline.

Tip

No title — baseline.

ryanlewis

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

ryanlewis

{
  "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:*)",

ryanlewis

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.

ryanlewis

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)

ryanlewis

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.

ryanlewis

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

ryanlewis

-- Redis script to implement a leaky bucket
-- see https://medium.com/callr-techblog/rate-limiting-for-distributed-systems-with-redis-and-lua-eeea745cb260
-- (c) Florent CHAUVEAU <florent.chauveau@gmail.com>

local ts  = tonumber(ARGV[1])
local cps = tonumber(ARGV[2])
local key = KEYS[1]

-- remove tokens < min (older than now() -1s)
local min = ts -1

ryanlewis

kubectl get rs,secrets -o json --namespace old | jq '.items[].metadata.namespace = "new"' | kubectl create-f  -

ryanlewis

public class ContourFormRefreshPayload
{
    public Guid Id { get; set; }
}

public class ContourFormCacheRefresher : JsonCacheRefresherBase<ContourFormCacheRefresher>
{
    public static string Id = "AA2970FD-8785-42C2-A289-A7A6614CAE45";
    protected override ContourFormCacheRefresher Instance => this;
    public override Guid UniqueIdentifier => new Guid(Id);