Ever had your AI coding agent confidently invent a file that doesn’t exist? Yeah, me too. I was working on a Hugo-based static site with Claude Code, asked it to map the routing structure, and it hallucinated a config file from scratch. It wasn’t the agent’s fault — Claude could only see the one file I’d dropped into the chat. Still, it had zero context on the project’s actual structure. ...
Ever had three AI coding agents installed — Claude Code for backend, Codex for prototyping, Cursor for frontend — and realized they each have their own configs, skills, memory, and security posture? You’re not running three tools. You’re managing three separate operating systems, and none of them talk to each other. But that’s the gap ECC fills. And with its v2.0.0 release eight days ago — rebranding from “Everything Claude Code” to ECC: The Agent Harness OS — it’s no longer just a Claude Code setup pack. It’s a universal layer that standardizes skills, security, memory, and workflow across every major AI coding harness. ...
Ever looked at your codebase knowing there’s dead code, stale imports, and logic holes — but assigning a senior dev to audit it costs $200/hr and using a cheap LLM to “just look at everything” returns AI-fluffed noise? Yeah, me too. That’s the exact gap shadcn/improve aims to fill. So the idea is deceptively simple: let your most expensive, most capable LLM do the hard thinking — audit, root-cause analysis, architecture calls — then hand its plan to a cheap model to execute. Different costs, different roles, one npx pipeline. And it’s from the same person who brought us shadcn/ui — already sitting at 4,900 stars in under a week. ...
You’ve been using Claude Code, Codex, Cursor, and maybe half a dozen other AI coding agents. Each one leaves session files scattered across your machine. Your monthly API bill shows a lump sum you can’t break down by agent. And when someone asks “how many tokens did last week’s refactor cost?”, you shrug. Yeah, me too. And that’s exactly the problem AgentsView solves. I spent a full afternoon digging into this one — reading the docs, tracing the codebase, and mapping out exactly what it can do. Still, it was worth every minute. Here’s what I found. ...
AI coding agents are incredible at generating code fast. But ask them to write a spec before touching the keyboard? Review their own output before shipping? Crickets. The problem isn’t the models — it’s that agents lack structured engineering workflows. They jump straight to code. No plan. No tests. No review. It works for small scripts, but for anything production-grade, that shortcut burns you sooner or later. That’s exactly what agent-skills (52.5k★ on GitHub, trending #1 as I type this) is built to fix. Addy Osmani — yes, the Google Chrome engineering manager who wrote the book on JavaScript patterns — put together 7 slash commands that encode senior engineer workflows for Claude Code, Cursor, Gemini CLI, and OpenCode. Install once, and your agent suddenly knows how to spec-first, test-drive, and review-before-merge. Like having a staff engineer sitting next to your terminal. ...
Ever finished a long coding session with Claude Code, come back the next day, and had to re-explain your entire project from scratch? Yeah, me too. And it drives me crazy. Every new session is a fresh conversation. Your agent doesn’t remember the architecture decisions you made yesterday, the bug you were tracking, or the test patterns you agreed on. So you burn the first 20-30 minutes of each session re-feeding context. Every. Single. Time. ...