惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

V
V2EX
aimingoo的专栏
aimingoo的专栏
S
SegmentFault 最新的问题
博客园_首页
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
博客园 - 【当耐特】
月光博客
月光博客
C
Check Point Blog
T
The Blog of Author Tim Ferriss
罗磊的独立博客
博客园 - Franky
MongoDB | Blog
MongoDB | Blog
H
Help Net Security
Microsoft Security Blog
Microsoft Security Blog
B
Blog
阮一峰的网络日志
阮一峰的网络日志
腾讯CDC
美团技术团队
N
Netflix TechBlog - Medium
Stack Overflow Blog
Stack Overflow Blog
Y
Y Combinator Blog
L
LangChain Blog
The Cloudflare Blog

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
I was using Claude Code wrong — here's the workflow that ...
David Perez Vicens · 2026-06-18 · via DEV Community

I've been using Claude Code on production projects for several months. It's genuinely impressive at writing code. But I kept shipping features that technically worked and architecturally misfired.

The problem wasn't the code quality. It was that I was handing the AI a task and letting it decide — implicitly, mid-implementation — what the system should actually do. Those decisions were buried in commits, invisible to review, and untraceable to any requirement.

I was using Claude Code as a replacement for a developer. It works better as a replacement for a compiler: something that takes a precise specification and produces correct output.

That realization led me to build opsx.

The core principle: Spec → Plan → Code

Code is the last artifact produced, never the first.

Before any implementation starts, there's a change document: a proposal that explains what's changing and why, delta specs that describe the new behavior, a design section, and an implementation task list. Only when that document is reviewed and approved does the agent write a single line of code.

This isn't a new idea — it's how safety-critical engineering works. The interesting part is applying it to agentic AI workflows.

Two planes, never confused

opsx enforces a clean separation between:

Management plane (Jira) — what work exists and who's doing it. Tasks, priorities, estimates. This is where product decisions live.

Governance plane (OpenSpec) — how the system must behave. This is the source of truth that authorizes code. A Jira ticket, no matter how detailed, does not authorize implementation. A reviewed OpenSpec change does.

This sounds bureaucratic. In practice, it means you catch design conflicts before they're in the code, and every commit traces back to an explicit decision.

What the workflow looks like

bashnpx @davidpv/opsx init # scaffold the workflow on any existing project
npx @davidpv/opsx doctor # verify tooling

Inside Claude Code:

/opsx:propose speed-up-search # write change: proposal + specs + design + tasks
/review-change speed-up-search # spec-reviewer audit before any code
/opsx:apply # implement task by task, traced to specs
/git-commit # semantic commit with Change:/Task:/Jira: footers
/ship # validate + archive specs + merge

The workflow is guided: /start routes new work, /next tells you where you are if you get lost. Every command ends by suggesting the next step.

What you get in practice

No silent design decisions. The proposal step forces you to articulate why before the agent executes how.
Reviewable specs. Delta specs are Markdown files in your repo. They go through your normal code review process.
Full traceability. Every commit has footers pointing to the change, the task step, and the Jira ticket. /ship archives the delta specs into the living spec.
Drift detection. If you make a hotfix that doesn't match the spec, /opsx:sync surfaces and resolves the conflict explicitly.

Stack-agnostic

opsx works with Claude Code, opencode, and Codex. workflow.yaml configures branches (git-flow or trunk-based), commit convention, and Jira project key. The commands are just Markdown files in .claude/, .opencode/, or .codex/ — readable and editable.

Try it

bashnpx @davidpv/opsx init

Node >= 18, requires the OpenSpec CLI and at least one agent CLI.

GitHub: github.com/davidpv/opsx-spec-driven-development-toolkit

npm: @davidpv/opsx