Workflow

A month ago I wrote about making Claude Code actually ship software. The workflow has been through at least six revisions since I started, but this latest version has been stable. Now it’s packaged as momentum. What This Actually Is Not another “make AI smarter” attempt. This is a workflow that assumes you know what you’re doing and gives you tools to do it faster. A recent post about building Log Carver at C-speed nailed it: “AI is no longer just a productivity booster. It’s a compiler for ideas. But like any compiler, it needs constraints, tests, and a damn good operator behind the keyboard.” ...

TL;DR Claude Code is powerful, like really.. but chaotic without some structure. Many people probably use it an ad-hoc fashion and there’s nothing wrong with that. I built a systematic workflow that turns it into a shipping machine. The magic is in the command structure. /plan-iteration → /plan-task → implement → /complete-task → /complete-iteration. Each command has specific responsibilities and quality gates. Evidence-based completion prevents the “is it done?” problem. /complete-iteration requires concrete proof that users can access working software. No more marking things “complete” when they’re just coded. ...

TL;DR Context creation was still ad-hoc even with the three-layer memory system in place. Every new language meant manually crafting another CLAUDE.local.md file and repeating the same research process. The research process was repeatable. Looking at what made my Python context effective, I noticed the same pattern: current tools, common mistakes, workflow problems. The structure was consistent across languages. Prompt template generates research-backed contexts. Feed it “python” or “golang” and get comprehensive development contexts through 12-15 targeted searches of authoritative sources. ...

You ask AI to implement something seemingly straightforward. It builds exactly what you asked for. Then you realize it’s not what you actually needed - wrong UX behavior, missing security considerations, breaks existing functionality. There’s a pattern emerging to address this: let the AI interview you first. TL;DR Assumptions hide in seemingly straightforward requests. Even simple tasks like “add email validation” contain multiple implementation decisions that aren’t obvious until you start building. ...