ensemble

Orchestrates multi-agent problem solving with consensus via staged workflows.

Updated Aug 23, 2026

Search Tags:#templates #multi-agent #orchestration #consensus #pipeline #human-in-the-loop #ensemble

Ready to run

npx skills add https://github.com/objectiveSee/claude-skills --skill ensemble

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ensemble
Source: https://github.com/objectiveSee/claude-skills/tree/main/ensemble
Command: npx skills add https://github.com/objectiveSee/claude-skills --skill ensemble

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Ensemble coordinates multi-agent problem solving by dispatching independent agents to work on the same task, then uses a review phase to reach consensus.

Core Features & Use Cases

Fan-out and fan-in orchestration across four stage types (build, research, review, synthesize) with structured, comparable outputs using standard templates.
Supports a human-in-the-loop escalation path and a formal consensus algorithm to determine results.
Use cases include code task pipelines, research questions, and any scenario where parallel exploration benefits from consensus-driven merging.

Quick Start

Configure and run a 5-build, 3-review, 1-synth ensemble pipeline for the given task, then await user confirmation before dispatch.

Frequently Asked Questions about ensemble

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is multi-agent consensus orchestration and when is it needed?▼

Multi-agent orchestration coordinates independent agents to work on the same task in parallel, then uses a review phase to reach consensus. It is needed for tasks that benefit from parallel exploration, independent outputs, and formal evaluation by reviewers.

How do I build a multi-agent pipeline with build, research, review, and synthesize stages?▼

You build a multi-agent pipeline by configuring fan-out and fan-in workflows across four stage types: build, research, review, and synthesize. Standard templates structure comparable outputs before a formal consensus algorithm determines the final results.

Can I add human-in-the-loop approval to a multi-agent consensus workflow?▼

Yes, you can add human-in-the-loop approval to a multi-agent consensus workflow. The orchestration supports a formal escalation path that awaits user confirmation before dispatching or finalizing the pipeline's synthesized output.

Does multi-agent orchestration work for code generation pipelines?▼

Multi-agent orchestration works for code generation pipelines by dispatching multiple build agents to explore the task independently. A review stage then formally evaluates these parallel outputs to reach a consensus before final synthesis.

What is the best way to merge independent research outputs from parallel agents?▼

The best way to merge independent research outputs is using a formal consensus algorithm during a dedicated review stage. Template-based outputs ensure the independent results are structured and comparable before the synthesis phase.

What are the limitations of fan-out and fan-in agent orchestration?▼

The main limitation of fan-out and fan-in agent orchestration is the overhead of running multiple agents and a formal review phase. Tasks that do not benefit from parallel exploration or independent evaluation may find this staged consensus workflow excessive.