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.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-orchestration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-orchestration Compares
| Feature / Agent | ai-orchestration | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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
Related Skills
agent-orchestration-multi-agent-optimize
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
workflow-orchestration-patterns
Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running ...
design-orchestration
No description provided.
task-orchestration
Execute repo work one task at a time using a strict plan → execute → iterate loop tracked in .copilot-todo.yaml.
orchestration
MANDATORY - Your default operating system. Adaptive workflow that routes simple tasks to direct execution and complex tasks to PRD iterations with agent swarms. Auto-creates skills when patterns emerge.
beads-orchestration
Multi-agent orchestration for GitHub Issues using BEADS task tracking
apache-airflow-orchestration
Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment
agent-orchestration-improve-agent
Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
workflow-orchestration
Design and implement DAG-based workflows with parallel execution, retries, and error handling. Use when building complex multi-step agent workflows.
ptc-orchestration
Activate when user needs multi-URL scraping, browser automation pipelines, or efficient tool orchestration to reduce API round-trips and context usage.
ai-orchestration-feedback-loop
Multi-AI engineering loop orchestrating Claude, Codex, and Gemini for comprehensive validation. USE WHEN (1) mission-critical features requiring multi-perspective validation, (2) complex architectural decisions needing diverse AI viewpoints, (3) security-sensitive code requiring deep analysis, (4) user explicitly requests multi-AI review or triple-AI loop. DO NOT USE for simple features or single-file changes. MODES - Triple-AI (full coverage), Dual-AI Codex-Claude (security/logic), Dual-AI Gemini-Claude (UX/creativity).
agentic-orchestration
Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.