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I built a permission-first CLAUDE.md + agent stack for Claude Code (free, MIT)

I've been using Claude Code daily for months. And I kept hitting the same wall: The agent would just start doing things. No plan. No approval. Just... acting. It deleted files I didn't want deleted. It refactored things I didn't ask it…

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I've been using Claude Code daily for months. And I kept hitting the same wall:



The agent would just start doing things. No plan. No approval. Just... acting.



It deleted files I didn't want deleted. It refactored things I didn't ask it to refactor. It made "helpful" assumptions that broke my architecture.



So I built Full Stack HQ — a configuration kit that enforces a permission-first workflow. Here's what I learned.









The core problem with AI coding agents



Most people configure their AI agent once (or never) and just... let it go. The result is an agent that:




  • Makes assumptions about what you want

  • Takes irreversible actions without asking

  • Mixes planning and execution in the same step

  • Has no consistent code style or architectural awareness



The agent is powerful but unpredictable. That's the worst combination in software development.









The solution: permission-first workflow



Nothing happens without your explicit approval. The agent plans, shows you what it intends to do, and waits.




You:    "Add user authentication with JWT"

Agent: Here's my plan:
Phase 1: Create auth module + JWT strategy
Phase 2: Add guards to protected routes
Phase 3: Implement refresh token rotation

[APPROVAL NEEDED] Should I proceed with Phase 1?

You: PLAN APPROVED

Agent: [implements Phase 1 only, then stops and reports]






The only valid approval keywords:




PLAN APPROVED
IMPLEMENTATION APPROVED
PROCEED
DO IT






Anything else — the agent waits. No exceptions.









What's inside Full Stack HQ






































Component Count Description
CLAUDE.md 1 Global rules for Claude Code
GEMINI.md 1 Global rules for Google Antigravity IDE
Agents 10 Specialist AI personas
Skills 28 Domain-specific knowledge modules
Workflows 10 Slash command procedures





10 Specialist Agents



Instead of one generic agent trying to do everything, you get domain experts:




















































Agent What it handles
frontend-specialist React, Next.js, Tailwind
backend-specialist NestJS, Node.js, APIs
database-specialist Prisma, PostgreSQL, migrations
architect System design, trade-offs, ADRs
code-reviewer Quality, patterns, best practices
test-engineer Vitest, Jest, Playwright
security-auditor Auth, OWASP, input validation
performance-optimizer Bundle, queries, rendering
devops-engineer Docker, CI/CD
documentation-writer READMEs, technical writing


Calling them is simple:




Use the database-specialist to design a user schema with soft deletes.









28 Skills



Deep knowledge modules for the tools you actually use:





  • Frontend: nextjs-app-router, react-best-practices, ui-ux-pro-max, frontend-design


  • Backend: nestjs-patterns, prisma-workflow, software-architecture


  • Testing: test-driven-development, systematic-debugging, webapp-testing


  • Meta: brainstorming, prompt-engineering, skill-creator






10 Workflows (Slash Commands)






/plan       → phased breakdown with approval checkpoints
/brainstorm → explore architecture options
/debug → systematic root-cause analysis
/create → implement an approved plan
/enhance → improve existing code quality
/test → generate or fix tests
/orchestrate → coordinate multiple agents












Install in 30 seconds



Mac/Linux:




curl -fsSL https://raw.githubusercontent.com/sabahattink/antigravity-fullstack-hq/main/install.sh | bash






Windows (PowerShell):




irm https://raw.githubusercontent.com/sabahattink/antigravity-fullstack-hq/main/install.ps1 | iex






Options:




./install.sh --only-claude        # Claude Code only
./install.sh --only-antigravity # Antigravity only
./install.sh --force # Overwrite existing configs






The script detects which IDEs you have installed and configures them automatically.









What gets installed where






~/.claude/
├── CLAUDE.md ← global rules (Claude Code)
├── agents/ ← 10 specialist agents
└── skills/ ← 28 skill modules

~/.gemini/
├── GEMINI.md ← global rules (Antigravity)
└── antigravity/
├── agents/
├── skills/
└── workflows/












The CLAUDE.md philosophy



The rules file enforces several things I found critical in practice:



1. Separation of planning and execution



The agent never does both in the same step. First it plans, you approve, then it executes. This alone eliminates 80% of unwanted surprises.



2. Role-based reasoning



Before acting, the agent asks: "Who is the right specialist for this?" A database schema question goes to the database specialist, not the frontend agent pretending to know Prisma.



3. Explicit code style



No semicolons. Single quotes. 2-space indentation. Arrow functions. Named exports. These aren't suggestions — they're enforced rules the agent follows on every file, every time.



4. Security checklist



Before every commit: no hardcoded secrets, all inputs validated, no unbounded queries, rate limiting on public endpoints. The agent checks these automatically.









Why it works



The mental model I was missing: AI agents should behave like senior engineers, not interns with root access.



Senior engineers don't start typing when you describe a problem. They think, propose a plan, get sign-off, then execute — one reversible step at a time.



Full Stack HQ enforces this discipline by default.









Repo



github.com/sabahattink/antigravity-fullstack-hq



MIT license. Open to PRs — especially new agents and skills.



What does your current CLAUDE.md look like? I'd love to see what rules others have found valuable.

CTI Threat Relationship Graph5 Knoten / 4 Relationen
CVE / Incident Software MITRE ATT&CK CWE Weakness IoC
SOC Incident Playbook: Remote Code Execution (RCE) Defense
title: Detect Exploitation - I built a permission-first CLAUDE.md + agent stack for Claude Code (free, MIT)
id: b52b28d9-2390-452c-b018-e986990fc69e
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-24
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
  - attack.t1059
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-24"
        description = "YARA Signature for "
    strings:
        $str = "I built a permission-first CLA" ascii wide
    condition:
        any of them
}
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich I built a permission-first CLAUDE.md + a.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

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