agentic-os

# 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 | Layer | Purpose | Persistence | |---|---|---| | Kernel (`CLAUDE.md`) | Identity, routing, model policies, agent registry | Git-tracked | | Agents (`agents/`) | Specialist identities with scoped tools and memory | Git-tracked | | Commands (`.claude/commands/`) | User-facing slash commands (`/daily-sync`, `/outreach`) | Git-tracked | | Scripts (`scripts/`) | Python/JS daemons triggered by cron or webhooks | Git-tracked | | State (`data/`) | Append-only logs, project state, decision records | Git-ignored or tracked | ## 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 ```markdown # 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/.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 appr