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Oma Coordination

OrganizationPopular
first-fluke
oma-coordination

Coordinate assigned specialist tasks and handoffs manually. Use when supervising a multi-agent project step by step.

Overview

Publisherfirst-fluke
Repositoryoh-my-agent
Skill nameoma-coordination
Stars
1.3K
Forks
149
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by first-fluke on GitHub. Read the source before you install it.

Installation

Install the Oma Coordination AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/first-fluke/oh-my-agent.git /tmp/oh-my-agent
mkdir -p .claude/skills
cp -r /tmp/oh-my-agent/skills/oma-coordination .claude/skills/oma-coordination
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Oma Coordination in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Oma Coordination on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Oma Coordination is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Multi-Agent Workflow Guide

Scheduling

Goal

Guide manual multi-agent coordination for complex work that spans PM, frontend, backend, mobile, and QA responsibilities.

Intent signature

  • User wants step-by-step coordination, manual agent spawning, or multi-domain work planning without full automation.
  • Task spans multiple specialist agents and requires contract alignment.

When to use

  • Complex feature spanning multiple domains (full-stack, mobile)
  • Coordination needed between frontend, backend, mobile, and QA
  • User wants step-by-step guidance for multi-agent coordination

When NOT to use

  • Simple single-domain task -> use the specific agent directly
  • User wants automated execution -> use orchestrator
  • Quick bug fixes or minor changes

Expected inputs

  • Complex feature or project goal
  • Required domains and priority tiers
  • Workspace/session constraints and API/data contract needs

Expected outputs

  • Manual coordination sequence
  • PM task decomposition, agent spawn order, monitoring guidance, and QA review step
  • API/data contract alignment checkpoints

Dependencies

  • PM, frontend, backend, mobile, QA, and orchestrator skills
  • CLI oma agent spawn and progress/result memory conventions

Control-flow features

  • Branches by task complexity, priority tiers, dependency ordering, and whether automation is desired
  • Spawns independent same-priority tasks in parallel when appropriate
  • Monitors progress files and contract alignment

Structural Flow

Entry

  1. Confirm the task is complex enough for multi-agent coordination.
  2. Start with PM task decomposition.
  3. Identify priority tiers and shared contracts.

Scenes

  1. PREPARE: Define session, domains, and task decomposition needs.
  2. ACT: Spawn agents by priority with separate workspaces.
  3. VERIFY: Monitor progress and API/data contract alignment.
  4. FINALIZE: Run QA review and coordinate remediation.

Transitions

  • If task is simple, route to one specialist.
  • If user wants automated execution, use orchestrator.
  • If QA finds CRITICAL issues, re-spawn responsible agents.

Failure and recovery

  • If contracts diverge, pause downstream frontend/mobile work until backend/API contract is reconciled.
  • If agent workspaces conflict, split ownership boundaries.
  • If progress stalls, inspect progress files and reissue focused instructions.

Exit

  • Success: specialist outputs are coordinated and QA-reviewed.
  • Partial success: blocked agents, contract conflicts, or QA failures are explicit.

Logical Operations

Actions

ActionSSL primitiveEvidence
Read request and domainsREADUser prompt and project context
Select agent planSELECTPM decomposition and priority tiers
Spawn agentsCALL_TOOLoma agent spawn
Monitor progressREADprogress-{agent}[-{sessionId}].md
Validate contractsVALIDATEAPI/data model alignment
Notify coordination statusNOTIFYFinal coordination summary

Tools and instruments

  • oma agent spawn, PM/frontend/backend/mobile/QA agents
  • Memory/progress/result files
  • Configured code intelligence, with native fallback per ../_shared/core/code-intelligence.md

Canonical command path

bash
oma agent spawn pm <pm-prompt-file> <session-id> --task-id <plan.pm_task.id> -w ./pm
oma agent spawn backend <backend-prompt-file> <session-id> --task-id <plan.backend_task.id> -w ./backend &
oma agent spawn frontend <frontend-prompt-file> <session-id> --task-id <plan.frontend_task.id> -w ./frontend &
wait

When native runtime dispatch is available (per-agent target vendor equals the current runtime vendor), prefer the runtime's native subagent path and use oma agent spawn as the cross-vendor fallback — same resolution rule as oma-orchestration.

Useful agent spawn options: -m/--model <vendor> (CLI vendor override), --isolation worktree (git worktree per spawn, prevents file conflicts), --read-only (non-destructive tools only, e.g. for review/QA passes).

Resource scope

ScopeResource target
LOCAL_FSProgress/result files and workspaces
PROCESSAgent spawn commands
MEMORYSession state and task board
CODEBASEShared contracts and implementation areas

Preconditions

  • Task requires multiple domains.
  • PM decomposition can identify independent priority tiers.

Effects and side effects

  • Spawns or guides multiple agents.
  • Coordinates workspace ownership and QA feedback.

Guardrails

  1. Always start with PM Agent for task decomposition
  2. Spawn independent tasks in parallel (same priority tier)
  3. Define API contracts before frontend/mobile tasks
  4. QA review is always the final step
  5. Assign separate workspaces to avoid file conflicts (or use --isolation worktree for a git worktree per spawn)
  6. Follow ../_shared/core/code-intelligence.md: discover configured tools, do not auto-install or track, and use native scoped search when unavailable or timed out
  7. Never skip steps in the workflow; follow each step sequentially without omission

Workflow

Step 1: Plan with PM Agent

PM Agent analyzes requirements, selects tech stack, creates task breakdown with priorities.

Step 2: Spawn Agents by Priority

Resolve the dispatch path per agent, then spawn:

  1. Resolve the per-agent target vendor from oma-config.yaml (agents: override, else model_preset)
  2. If the target vendor equals the current runtime vendor and a native subagent path exists, use native dispatch
  3. Otherwise use oma agent spawn for that agent
  4. Spawn all same-priority tasks in parallel using background processes
bash
# Example: spawn backend and frontend in parallel
oma agent spawn backend backend-prompt.md session-id --task-id plan.backend_task.id -w ./backend &
oma agent spawn frontend frontend-prompt.md session-id --task-id plan.frontend_task.id -w ./frontend &
wait
Step 3: Monitor & Coordinate
  • Use memory read tool to poll progress-{agent}[-{sessionId}].md files (spawned agents write the session-suffixed form)
  • Verify API contracts align between agents
  • Ensure shared data models are consistent
Step 4: QA Review

Spawn QA Agent last to review all deliverables. Address CRITICAL issues by re-spawning agents.

Automated Alternative

For fully automated execution without manual spawning, use the orchestrator skill instead.

References

Frequently asked questions

What does the Oma Coordination AI skill do?

Coordinate assigned specialist tasks and handoffs manually. Use when supervising a multi-agent project step by step.

Why use Oma Coordination on TypingMind?

Because you install it once and use it with any model. Oma Coordination is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Oma Coordination in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/first-fluke/oh-my-agent/tree/main/skills/oma-coordination. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Oma Coordination?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Oma Coordination?

As many as you like. As long as a model supports skills, you can use Oma Coordination with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Oma Coordination AI skill free?

Yes. It is published on GitHub by first-fluke under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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