Build persistent multi-agent operating systems on Claude Code, covering kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and database-free state. USE WHEN designing a multi-agent system, kernel, or file-based memory and automation layer on Claude Code.
Build persistent multi-agent operating systems on Claude Code, covering kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and database-free state. USE WHEN designing a multi-agent system, kernel, or file-based memory and automation layer on Claude Code.
origin
ECC
cluster
ai-agents-meta
version
1.0.0
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
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 Rules1. 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:
1. @dev - "Build a landing page with [requirements]"
2. @writer - "Write a launch blog post for [product] using the landing page copy"
3. Kernel synthesizes both outputs into a unified response
For parallel execution, use Claude Code's background task capability or shell scripts that invoke Claude Code with specific agent contexts.
Commands and Daily Workflows
Slash commands are markdown files in .claude/commands/. They define reusable workflows.
Command Structure
# /daily-sync
Run the morning briefing:
1. Read `data/logs/last-sync.md` for context
2. Check project status: `git status`, pending PRs, CI health
3. Review `data/inbox/` for new tasks or decisions needed
4. Generate a summary of blockers, priorities, and next actions
5. Append the briefing to `data/logs/daily/<date>.md`
Standard Command Set
Command
Purpose
/daily-sync
Morning briefing: status, blockers, priorities
/outreach
Run outreach workflow (email, LinkedIn, etc.)
/research <topic>
Deep research with citation tracking
/apply-jobs
Tailor resume + cover letter for a target role
/analytics
Pull metrics from Stripe, GitHub, or custom sources
/interview-prep
Generate flashcards or mock interview questions
/decision <topic>
Log a decision with pros/cons and chosen path
Activating Commands
Place command files in .claude/commands/<command-name>.md. Claude Code auto-discovers them. Users invoke them with /<command-name>.
Persistent Memory
Memory is file-based. No vector DB, no Redis, no PostgreSQL. JSON and markdown files in data/ are the database.
Memory Directory Structure
data/
├── daily-logs/ # Append-only daily activity logs
├── projects/ # Per-project context files
├── decisions/ # Architectural and business decisions (ADR format)
├── inbox/ # New tasks or ideas awaiting triage
├── contacts/ # People, companies, relationship notes
└── templates/ # Reusable prompts and formats
Daily Log Format
# 2026-04-22 - Daily Log## Sessions- 09:00 - Session 1: Refactored auth module (@dev)
- 11:30 - Session 2: Drafted investor update (@writer)
## Decisions- Switched from JWT to session cookies (see `data/decisions/2026-04-22-auth.md`)
## Blockers- Waiting on API key from vendor (follow up 2026-04-24)
## Next Actions- [ ] Merge auth refactor PR
- [ ] Send investor update for review
Auto-Reflection Pattern
At the end of each session, the kernel appends a reflection:
## Reflection - Session 3- What worked: Parallel agent execution saved 20 minutes
- What didn't: @researcher hit a paywalled source, need better source ranking
- What to change: Add `source-tier` field to research notes (A/B/C credibility)
This creates a feedback loop that improves the system over time without code changes.
Scheduled Automation
Agentic OS tasks run on a schedule using external cron, not Claude Code's built-in cron (which dies when the session ends).
