blackboard-coordinator

Coordinate multi-agent problem solving with a shared blackboard knowledge space.

1|Updated Apr 23, 2026

Search Tags:#collaboration #multi-agent #consensus #coordination #voting #knowledge-base #blackboard

Ready to run

npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill blackboard-coordinator

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: blackboard-coordinator
Source: https://github.com/mtsatryan/openclaw-ai-agents/tree/main/blackboard-coordinator
Command: npx skills add https://github.com/mtsatryan/openclaw-ai-agents --skill blackboard-coordinator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Blackboard Coordinator enables coordinated problem-solving across multiple agents by providing a shared knowledge space where findings, hypotheses, and solutions can be contributed, tracked, and evolved into consensus decisions.

Core Features & Use Cases

Blackboard Pattern: manage a shared knowledge base with problem definitions, constraints, and solution hypotheses.
Collaborative Intelligence: support multi-agent perspectives, conflict resolution, and consensus building to synthesize high-quality solutions.
Use Case: Orchestrate architecture decision processes, session initializations, and cross-domain reasoning in complex projects.

Quick Start

Initialize a blackboard-coordinator session with your agents to begin collaborative problem solving.

Frequently Asked Questions about blackboard-coordinator

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

FAQPage Schema

What is the blackboard pattern for multi-agent coordination?▼

The blackboard pattern is a shared knowledge space where multiple agents contribute findings, hypotheses, and constraints to collaboratively solve complex problems and reach consensus decisions.

How do I coordinate multiple agents for architecture decision making?▼

You coordinate architecture decision making by initializing a shared blackboard session that structures problem and solution spaces, allowing agents to propose hypotheses and vote on consensus decisions.

How does voting-based consensus work in multi-agent problem solving?▼

Voting-based consensus in multi-agent problem solving allows agents to evaluate contributed hypotheses within the shared knowledge base, resolving conflicts and synthesizing high-quality collaborative solutions.

Can I use this multi-agent coordination approach for cross-domain engineering tasks?▼

Yes, this multi-agent coordination approach supports collaborative intelligence tasks across engineering domains, enabling cross-domain reasoning and knowledge synthesis in complex project environments.

What's the best way to structure a shared knowledge base for collaborative intelligence?▼

The best way to structure a shared knowledge base for collaborative intelligence is to separate problem definitions, constraints, and solution hypotheses into dedicated spaces for agent contribution and tracking.