omc

supercent-io/skills-template · updated May 30, 2026

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$npx skills add https://github.com/supercent-io/skills-template --skill omc
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summary

32 specialized agents with smart model routing, persistent execution loops, and real-time visibility for Claude Code workflows.

  • Six orchestration modes (Team, Autopilot, Ralph, Ultrawork, Pipeline, Swarm) handle everything from coordinated multi-stage pipelines to autonomous single-agent execution and maximum parallelism
  • Team mode runs a canonical five-stage pipeline: plan → PRD → execute → verify → fix, with automatic looping until tasks complete
  • Magic keywords (autopilot, ralph, ul
skill.md

omc (oh-my-claudecode) — Claude Code Multi-Agent Orchestration

When to use this skill

  • You want Teams-first multi-agent orchestration inside Claude Code
  • You need 32 specialized agents with smart model routing (Haiku → Opus)
  • Complex tasks that benefit from parallel agent execution with verification loops
  • Any Claude Code workflow that needs persistent, guaranteed-completion execution

1. Installation (3 Steps)

Step 1: Install plugin

/plugin marketplace add https://github.com/Yeachan-Heo/oh-my-claudecode
/plugin install oh-my-claudecode

Step 2: Run setup

/omc:omc-setup

Step 3: Build something

autopilot: build a REST API for managing tasks

npm alternative: npm install -g oh-my-claude-sisyphus


2. Orchestration Modes

Mode What it is Use For
Team (recommended) Staged pipeline: team-plan → team-prd → team-exec → team-verify → team-fix Coordinated agents on shared task list
Autopilot Autonomous single lead agent End-to-end feature work with minimal ceremony
Ultrawork Maximum parallelism (non-team) Burst parallel fixes/refactors
Ralph Persistent mode with verify/fix loops Tasks that must complete fully
Pipeline Sequential staged processing Multi-step transformations
Swarm/Ultrapilot Legacy facades → route to Team Existing workflows

Enable Claude Code native teams in ~/.claude/settings.json:

{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

3. Magic Keywords

Keyword Effect Example
team Canonical Team orchestration /omc:team 3:executor "fix all TypeScript errors"
autopilot Full autonomous execution autopilot: build a todo app
ralph Persistence mode ralph: refactor auth
ulw Maximum parallelism ulw fix all errors
plan Planning interview plan the API
ralplan Iterative planning consensus ralplan this feature
swarm Legacy (routes to Team) swarm 5 agents: fix lint errors
ultrapilot Legacy (routes to Team) ultrapilot: build a fullstack app

Note: ralph includes ultrawork — activating ralph mode automatically includes ultrawork's parallel execution.


4. Team Mode (Canonical)

/omc:team 3:executor "fix all TypeScript errors"

Runs as a staged pipeline:

team-plan → team-prd → team-exec → team-verify → team-fix (loop)

5. Utilities

Rate Limit Wait

Auto-resume Claude Code sessions when rate limits reset:

omc wait          # Check status, get guidance
omc wait --start  # Enable auto-resume daemon
omc wait --stop   # Disable daemon

Notifications (Telegram/Discord)

omc config-stop-callback telegram --enable --token <bot_token> --chat <chat_id>
omc config-stop-callback discord --enable --webhook <url>

6. Updating

# 1. Sync latest version
/plugin marketplace update omc

# 2. Re-run setup
/omc:omc-setup

# If issues after update
/omc:omc-doctor

7. Optional: Multi-AI Orchestration

OMC can optionally orchestrate external AI providers (not required):

Provider Install What it enables
Gemini CLI npm install -g @google/gemini-cli Design review, UI consistency (1M token context)
Codex CLI npm install -g @openai/codex Architecture validation, code review cross-check

Why OMC?

  • Zero configuration — works out of the box with intelligent defaults
  • Team-first orchestration — Team is the canonical multi-agent surface
  • Natural language interface — no commands to memorize
  • Automatic parallelization — complex tasks distributed across 32 specialized agents
  • Persistent execution — won't stop until the job is verified complete
  • Cost optimization — smart model routing saves 30–50% on tokens
  • Real-time visibility — HUD statusline shows what's happening under the hood

Quick Reference

Action Command
Install /plugin marketplace add https://github.com/Yeachan-Heo/oh-my-claudecode
Setup /omc:omc-setup
Team mode /omc:team N:executor "task"
Autopilot autopilot: <task>
Ralph loop ralph: <task>
Ultrawork ulw <task>
Update /plugin marketplace update omc && /omc:omc-setup
Debug /omc:omc-doctor

Full documentation · GitHub

how to use omc

How to use omc on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add omc
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/supercent-io/skills-template --skill omc

The skills CLI fetches omc from GitHub repository supercent-io/skills-template and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/omc

Reload or restart Cursor to activate omc. Access the skill through slash commands (e.g., /omc) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.672 reviews
  • Kwame Kim· Dec 28, 2024

    Keeps context tight: omc is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kwame Khan· Dec 24, 2024

    We added omc from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Ama Gupta· Dec 24, 2024

    I recommend omc for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chaitanya Patil· Dec 12, 2024

    omc reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Kaira Abebe· Dec 8, 2024

    omc fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ama Iyer· Dec 4, 2024

    Registry listing for omc matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Naina Gupta· Nov 27, 2024

    omc has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kwame Johnson· Nov 23, 2024

    Useful defaults in omc — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kiara Chawla· Nov 15, 2024

    omc fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Kwame Smith· Nov 15, 2024

    Solid pick for teams standardizing on skills: omc is focused, and the summary matches what you get after install.

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