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

让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
V
V2EX
WordPress大学
WordPress大学
U
Unit 42
I
InfoQ
A
About on SuperTechFans
宝玉的分享
宝玉的分享
J
Java Code Geeks
博客园 - 司徒正美
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
G
Google Developers Blog
人人都是产品经理
人人都是产品经理
小众软件
小众软件
Microsoft Security Blog
Microsoft Security Blog
L
LangChain Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Hugging Face - Blog
Hugging Face - Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
aimingoo的专栏
aimingoo的专栏
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Last Week in AI
Last Week in AI
腾讯CDC
Recent Announcements
Recent Announcements

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 built a CLI that scaffolds agentic workflows for Claude...
Patrick Sardinha · 2026-05-28 · via DEV Community
Cover image for I built a CLI that scaffolds agentic workflows for Claude Code

Patrick Sardinha

Most developers using Claude Code work the same way: one long conversation, manual prompting between tasks, context that grows until the model loses track of what it was doing. It works — but it doesn't scale.

I built AgentKit to fix this. It's a CLI that generates the orchestration layer for any project: the files that tell Claude Code who to be, what to build, and how to hand off work between specialized agents — automatically.

How it works

You write a plain-language blueprint describing what you want to build:

# Todo App — Blueprint

## Goal
A simple web todo app with task creation, completion, and filtering.

## Tech constraints
- Next.js 14 + TypeScript + Tailwind
- localStorage for persistence, no backend

You run one command:

npx @patricksardinha/agentkit-cli init --blueprint PROJECT_BLUEPRINT.md

AgentKit generates four files:

  • CLAUDE.md — the standing brief every agent reads before starting
  • AGENT_WORKFLOW.md — a placeholder Claude Code fills during Phase 0
  • PLAYBOOK.md — the autonomous execution engine
  • README.md — project documentation extracted from your blueprint

Then you open Claude Code and type one instruction:

Read PLAYBOOK.md and execute the procedure.

What happens next

Claude Code runs in three phases automatically.

Phase 0 — Decomposition: it reads your blueprint and proposes a list of specialized agents. You validate before anything gets built.

Skills enrichment pause: Claude Code creates the agent folders and waits. You can drop API docs, schemas, or any context files into the relevant agent folder before execution starts.

Phase 1 — Execution: each agent runs in sequence, validates its success criterion, retries on failure, and escalates to you only when genuinely blocked.

A real example

I used AgentKit to build DevLog Desktop — a Tauri v2 app with local RAG via Ollama.

Result after one Claude Code session:

  • 6 agents executed autonomously
  • 68 tests passing
  • Windows installer produced (MSI + NSIS)
  • Full EN/FR i18n, dark mode, semantic search

Design choices

No AI inside the tool. AgentKit is purely structural — it generates markdown files. The intelligence lives in Claude Code, not in AgentKit. This means no API key, no costs, no vendor lock-in.

Phase 0 always runs. Most developers don't know how to optimally split work into agents. AgentKit delegates that decision to Claude Code every time, whether or not you provide a blueprint. You describe intent, Claude Code designs the structure.

Stack detection first, interactive fallback. AgentKit reads your project directory to detect the stack. If nothing is found (new empty project), it asks you interactively from a list of supported stacks: React, Next.js, Tauri, FastAPI, Express, Node.js.

Try it

# On any project (empty or existing)
npx @patricksardinha/agentkit-cli init

# With a blueprint
npx @patricksardinha/agentkit-cli init --blueprint PROJECT_BLUEPRINT.md

# Add a feature to an existing project
npx @patricksardinha/agentkit-cli add --feature "add dark mode"

GitHub: https://github.com/patricksardinha/agentkit-cli

npm: https://www.npmjs.com/package/@patricksardinha/agentkit-cli

Feedback welcome.