Agentic OS

Treat Claude Code as a persistent runtime / operating system rather than a chat session. This skill codifies the architecture used by production agentic setups: a kernel config that routes tasks to specialist agents, persistent file-based memory, scheduled automation, and a JSON/markdown data layer.

When to Activate

• Building a multi-agent workflow inside Claude Code
• Setting up persistent Claude Code automation that survives session restarts
• Creating a "personal OS" or "agentic OS" for recurring tasks
• User says "agentic OS", "personal OS", "multi-agent", "agent coordinator", "persistent agent"
• Structuring long-running projects where context must survive across sessions

Architecture Overview

The Agentic OS has four layers. Each layer is a directory in your project root.

project-root/
├── CLAUDE.md          # Kernel: identity, routing rules, agent registry
├── agents/            # Specialist agent definitions (markdown prompts)
├── .claude/commands/  # Slash commands: user-facing CLI
├── scripts/           # Daemon scripts: scheduled or event-driven tasks
└── data/              # State: JSON/markdown filesystem, no external DB

Layer Responsibilities

The Kernel

CLAUDE.md is the kernel. It acts as the COO / orchestrator. Claude reads it at session start and uses it to route work.

Kernel Structure

# CLAUDE.md - Agentic OS Kernel

## Identity
You are the COO of [project-name]. You route tasks to specialist agents.
You never write code directly. You delegate to the right agent and synthesize results.

## Agent Registry

| Agent | Role | Trigger |
|---|---|---|
| @dev | Code, architecture, debugging | User says "build", "fix", "refactor" |
| @writer | Documentation, content, emails | User says "write", "draft", "blog" |
| @researcher | Research, analysis, fact-checking | User says "research", "analyze", "compare" |
| @ops | DevOps, deployment, infrastructure | User says "deploy", "CI", "server" |

## Routing Rules
1. Parse the user request for intent keywords
2. Match to the Agent Registry trigger column
3. Load the corresponding agent file from `agents/<name>.md`
4. Hand off execution with full context
5. Synthesize and present the result back to the user

## Model Policies
- Default model: use the repository or harness default.
- @dev tasks: prefer a higher-reasoning model for complex architecture.
- @researcher tasks: use the configured research-capable model and approved search tools.
- Cost ceiling: warn before exceeding the project's configured spend threshold.

Key Principle

The kernel should be small and declarative. Routing logic lives in plain markdown tables, not code. This makes the system inspectable and editable without debugging.

Specialist Agents

Each agent is a standalone markdown file in agents/. Claude loads the relevant agent file when routing a task.

Agent Definition Format

# @dev - Software Engineer

## Identity
You are a senior software engineer. You write clean, tested, production-grade code.
You prefer simple solutions. You ask clarifying questions when requirements are ambiguous.

## Memory Scope
- Read `data/projects/<current-project>.md` for context
- Read `data/decisions/` for architectural decisions
- Append execution logs to `data/logs/<date>-@dev.md`

## Tool Access
- Full filesystem access within project root
- Git operations (status, diff, commit, branch)
- Test runner access
- MCP servers as configured in `.claude/mcp.json`

## Constraints
- Always write tests for new features
- Never commit directly to `main`; use feature branches
- Prefer editing existing files over creating new ones
- Keep functions under 50 lines when possible

Multi-Agent Collaboration Pattern

When a task spans multiple agents, the kernel runs them sequentially or in parallel:

User: "Build a landing page and write the launch blog post"

Kernel routing:

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