ai-orchestration

Multi-model AI collaboration via orchestrator MCP. Use when seeking second opinions, debugging complex issues, building consensus on architectural decisions, conducting code reviews, or needing external validation on analysis.

16 stars

Best use case

ai-orchestration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Multi-model AI collaboration via orchestrator MCP. Use when seeking second opinions, debugging complex issues, building consensus on architectural decisions, conducting code reviews, or needing external validation on analysis.

Teams using ai-orchestration should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/ai-orchestration/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/development/ai-orchestration/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/ai-orchestration/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How ai-orchestration Compares

Feature / Agentai-orchestrationStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Multi-model AI collaboration via orchestrator MCP. Use when seeking second opinions, debugging complex issues, building consensus on architectural decisions, conducting code reviews, or needing external validation on analysis.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# AI CLI Orchestration

Query external AI models (claude, codex, gemini) for second opinions, debugging, consensus building, and expert validation.

## Tools Overview

| Tool        | Mode        | Description                                  |
| ----------- | ----------- | -------------------------------------------- |
| `ai_call`   | Synchronous | Call AI and wait for result                  |
| `ai_spawn`  | Async       | Start AI in background, get job ID           |
| `ai_fetch`  | Async       | Get result from spawned AI (with timeout)    |
| `ai_list`   | Utility     | List all running/completed AI jobs           |
| `ai_review` | Convenience | Spawn all 3 AIs in parallel with same prompt |

## Role Hierarchy

| CLI    | Role        | Mode      | Capabilities                       |
| ------ | ----------- | --------- | ---------------------------------- |
| claude | Worker/Peer | Full      | Can execute any tool/command       |
| codex  | Reviewer    | Read-only | Code review, analysis, suggestions |
| gemini | Researcher  | Read-only | Web search, documentation lookup   |

## Parallel Execution (Recommended)

```python
# Spawn all 3 models in parallel
claude_job = ai_spawn(cli="claude", prompt="Analyze this code for bugs...")
codex_job = ai_spawn(cli="codex", prompt="Review this code for patterns...")
gemini_job = ai_spawn(cli="gemini", prompt="Research best practices for...")

# All running simultaneously! Fetch results:
claude_result = ai_fetch(job_id=claude_job.job_id, timeout=120)
codex_result = ai_fetch(job_id=codex_job.job_id, timeout=120)
gemini_result = ai_fetch(job_id=gemini_job.job_id, timeout=120)

# Total time = slowest model (~60s) instead of sum (~180s)
```

Or use `ai_review` for convenience:

```python
review = ai_review(prompt="Analyze this architecture decision...", files=["src/"])
claude_result = ai_fetch(job_id=review.jobs["claude"].job_id, timeout=120)
```

## When to Use External Models

**Do use when:** Stuck on complex bugs, architectural decisions with tradeoffs, need validation before major refactoring, security-sensitive code, want diverse perspectives

**Don't use when:** Simple work, already confident, just executing known solution

## References

- **Tool parameters**: See [references/tools.md](references/tools.md)
- **Usage patterns**: See [references/patterns.md](references/patterns.md)
- **Sub-agents**: See [references/sub-agents.md](references/sub-agents.md)

## Tips

- **Use parallel for multi-model**: `ai_spawn` + `ai_fetch` is 3x faster than sequential
- **Be specific**: Include file paths, error messages, and context
- **Use appropriate CLI**: codex for code review, gemini for web search
- **Delegate complex work**: Use sub-agents for structured analysis
- **Remember read-only**: Codex and Gemini cannot execute commands or modify files
- **Include files**: Use the `files` parameter to provide code context
- **Monitor jobs**: Use `ai_list()` to check status of all running jobs

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