Agent skill

Agentic Os

by affaan-m in affaan-m/ECC

Build persistent multi-agent operating systems on Claude Code.

MITAuto-check: notesAgent Workflows

Install Agentic Os

skills CLI
$ npx skills add affaan-m/ECC --skill agentic-os -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC agentic-os --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agentic-os .claude/skills/agentic-os && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agentic-os
GitHub stars
276k
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
697 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

Build persistent multi-agent operating systems on Claude Code.

  • Building a persistent multi-agent system on Claude Code with its own memory
  • SKILL.md covers When to Activate, Architecture Overview, The Kernel and Specialist Agents, plus 5 more sections
  • Needs API_KEY
  • Tasks that involve Hooks and plugins

What it does

Agentic Os is an agent skill from affaan-m/ECC. Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Hooks and plugins, Multi-agent orchestration and State management. It works with Git. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Building a persistent multi-agent system on Claude Code with its own memory
  • Tasks that involve Hooks and plugins
  • Tasks that involve Multi-agent orchestration

Example prompts

  • “/agentic-os”

Requirements

  • A credential in API_KEY

What it can do on your machine

Read from SKILL.md and the folder at commit 4eb71d9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown, ini, json, xml and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agentic Os loads about 3.1k tokens when it runs. Until then it costs about 85 tokens; SKILL.md has 697 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~85
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:358
    Use environment variables or a `.env` file loaded by scripts. Agents reference `process.env.API_KEY`.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 4eb71d9, republished under its MIT licence (© affaan-m). 697 words, ~3,071 tokens.

Download SKILL.mdSave it as .claude/skills/agentic-os/SKILL.md (or your agent's skills folder).
name
agentic-os
description
Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.
metadata.origin
ECC

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
LayerPurposePersistence
Kernel (CLAUDE.md)Identity, routing, model policies, agent registryGit-tracked
Agents (agents/)Specialist identities with scoped tools and memoryGit-tracked
Commands (.claude/commands/)User-facing slash commands (/daily-sync, /outreach)Git-tracked
Scripts (scripts/)Python/JS daemons triggered by cron or webhooksGit-tracked
State (data/)Append-only logs, project state, decision recordsGit-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/<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
markdown
# @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
markdown
# /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
CommandPurpose
/daily-syncMorning briefing: status, blockers, priorities
/outreachRun outreach workflow (email, LinkedIn, etc.)
/research <topic>Deep research with citation tracking
/apply-jobsTailor resume + cover letter for a target role
/analyticsPull metrics from Stripe, GitHub, or custom sources
/interview-prepGenerate 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
markdown
# 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:

markdown
## 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.

Show full SKILL.md (271 more words)Show less

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).

macOS: LaunchAgent
xml
<!-- ~/Library/LaunchAgents/com.agentic.daily-sync.plist -->
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" ...>
<plist version="1.0">
<dict>
    <key>Label</key>
    <string>com.agentic.daily-sync</string>
    <key>ProgramArguments</key>
    <array>
        <string>/claude</string>
        <string>--cwd</string>
        <string>/path/to/project</string>
        <string>--command</string>
        <string>/daily-sync</string>
    </array>
    <key>StartCalendarInterval</key>
    <dict>
        <key>Hour</key>
        <integer>8</integer>
        <key>Minute</key>
        <integer>0</integer>
    </dict>
    <key>StandardOutPath</key>
    <string>/tmp/agentic-daily-sync.log</string>
</dict>
</plist>
Linux: systemd Timer
ini
# ~/.config/systemd/user/agentic-daily-sync.service
[Unit]
Description=Agentic OS Daily Sync

[Service]
Type=oneshot
ExecStart=/usr/local/bin/claude --cwd /path/to/project --command /daily-sync
ini
# ~/.config/systemd/user/agentic-daily-sync.timer
[Unit]
Description=Run daily sync every morning

[Timer]
OnCalendar=*-*-* 8:00:00
Persistent=true

[Install]
WantedBy=timers.target
Cross-Platform: pm2
bash
# ecosystem.config.js
module.exports = {
  apps: [{
    name: 'agentic-daily-sync',
    script: 'claude',
    args: '--cwd /path/to/project --command /daily-sync',
    cron_restart: '0 8 * * *',
    autorestart: false
  }]
};

Data Layer

The data layer is your filesystem. Use JSON for structured data and markdown for narrative content.

JSON for Structured State
json
// data/projects/website-v2.json
{
  "name": "Website v2",
  "status": "in-progress",
  "milestone": "beta-launch",
  "agents_involved": ["@dev", "@writer"],
  "files": {
    "spec": "docs/website-v2-spec.md",
    "design": "designs/website-v2.fig"
  },
  "metrics": {
    "commits": 47,
    "last_session": "2026-04-22T11:30:00Z"
  }
}
Markdown for Narrative

Use markdown for anything a human reads: decisions, logs, research notes, contact records.

Schema Evolution

Never rename existing fields. Add new fields and mark old ones deprecated:

json
{
  "name": "Website v2",
  "status": "in-progress",
  "milestone": "beta-launch",
  "_deprecated_priority": "high",
  "priority_v2": { "level": "high", "rationale": "Blocks investor demo" }
}

This keeps historical data readable without migration scripts.

Anti-Patterns

Monolithic Single Agent
markdown
# BAD - One agent does everything
You are a full-stack developer, writer, researcher, and DevOps engineer.

Split into specialist agents. The kernel handles routing.

Stateless Sessions
markdown
# BAD - No memory between sessions
Starting fresh every time Claude Code opens.

Always read data/ at session start and write back at session end.

Hardcoded Credentials
markdown
# BAD - API keys in agent files or CLAUDE.md
Your OpenAI API key is sk-xxxxxxxx

Use environment variables or a .env file loaded by scripts. Agents reference process.env.API_KEY.

External Database for Simple State
markdown
# BAD - PostgreSQL for a solo user's agentic OS

Use JSON/markdown files until you have multiple concurrent users or GBs of data.

Over-Engineered Routing
markdown
# BAD - Routing logic in code instead of markdown tables
if (intent.includes('deploy')) { agent = opsAgent; }

Keep routing declarative in CLAUDE.md markdown tables. It is inspectable, editable, and debuggable.

Best Practices

  • CLAUDE.md is under 200 lines and fits in context window
  • Each agent file is under 100 lines and focused on one domain
  • data/ is git-ignored for sensitive logs, git-tracked for decisions and specs
  • Commands use imperative names: /daily-sync, not /run-daily-sync
  • Logs are append-only; never edit past daily logs
  • Every agent has a Memory Scope section defining what files it reads
  • Reflections are written at the end of every session
  • Scheduled tasks use external cron (LaunchAgent, systemd, pm2), not Claude Code's session cron
  • Cost tracking: log API spend per session in data/logs/<date>-costs.json
  • One project = one Agentic OS. Do not share a single CLAUDE.md across unrelated projects.

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/agentic-os of affaan-m/ECC.

Open the folder on GitHubat commit 4eb71d9

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Agentic Os

What does Agentic Os do?

Build persistent multi-agent operating systems on Claude Code. Agentic Os is an agent skill from affaan-m/ECC. Build persistent multi-agent operating systems on Claude Code.

When should I use Agentic Os?

Agentic Os fits situations like: building a persistent multi-agent system on Claude Code with its own memory; tasks that involve Hooks and plugins; tasks that involve Multi-agent orchestration.

How do I install Agentic Os in Claude Code?

Run `npx skills add affaan-m/ECC --skill agentic-os -a claude-code`. Or copy the skill folder (skills/agentic-os in affaan-m/ECC) into .claude/skills/agentic-os in your project. Claude Code loads it when a task matches its description.

How do I install Agentic Os in Codex?

Run `npx skills add affaan-m/ECC --skill agentic-os -a codex`. Or copy the skill folder (skills/agentic-os in affaan-m/ECC) into .agents/skills/agentic-os in your project. Codex loads it when a task matches its description.

Can I use Agentic Os in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill agentic-os -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agentic-os, .gemini/skills/agentic-os, .github/skills/agentic-os and .opencode/skills/agentic-os in your project.

What does Agentic Os need to run?

Going by SKILL.md and its folder, Agentic Os needs credentials named API_KEY. Our summary lists: A credential in API_KEY.

Does Agentic Os access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agentic Os safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Agentic Os use?

Agentic Os is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agentic Os use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agentic Os?

Skills that share tags, products or a category with Agentic Os: O2 Review Loop (openobserve/openobserve, 22k stars), ClawTeam Multi-Agent Swarm (win4r/ClawTeam-OpenClaw, 1.5k stars), Harness Evolution Feedback Loop (revfactory/harness, 9.1k stars) and Compound Engineering Setup (EveryInc/compound-engineering-plugin, 25k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agentic Os?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,111 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 10, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.