Message Bus (Grade A) logo

Message Bus (Grade A)Security-tested data-ai skill for Claude AI. Grade A. File-based message queue for inter-agent coordination. Used by workers AND board directors to communicate. Provides: progress updates, task comple

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Daniel NikulshynReviewed by Daniel Nikulshyn·Updated July 2026

Overview

Message Bus (Grade A) is a file-based message queue designed for inter-agent coordination. It allows workers and board directors to communicate and share state, facilitating parallel execution and board deliberation. The message queue is structured as a directory with various files, including a message log, lock mutex, current file locks, worker heartbeats, and session metadata. Messages can be either worker or board messages. Worker messages include task progress updates, task completion signals, file locks, and dependency blocking. Board messages cover director assessments, discussion messages, final votes, and aggregated decisions. The Message Bus protocol defines functions for posting and reading messages, as well as polling for events and acquiring file locks. While designed for inter-agent coordination, this tool holds potential for more general use cases, such as distributed computing or workflow management.

Key features

  • File-based message queue
  • Progress updates
  • Task completion signals
  • File locking
  • Board deliberation sessions
  • Event polling

Pricing

Model
Free
Category
Skills
Rating
No reviews yet

Use cases

Parallel Task Execution

Message Bus coordinates worker tasks, providing progress updates and task completion signals to ensure efficient parallel execution.

Board Decision-Making

Message Bus facilitates board deliberation sessions, enabling directors to discuss and vote on proposals, with features like assessments, discussions, and final votes.

Pros & Cons

Pros

  • Enables efficient inter-agent communication
  • Provides robust message queuing and event handling
  • Supports parallel execution with file locking and task completion signals

Cons

  • Relies on file-based storage, which may have performance limitations
  • Requires careful implementation of file locking and event handling protocols

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Q&A

Can I use this alongside other Claude Code plugins?

Yes. SupaConductor uses the /orchestrator-supaconductor: namespace and doesn't conflict with any other plugins, built-in commands, or MCP servers.

Asked by Greta Nowak · Oct 4, 2025

Is this overkill for small tasks?

For a quick one-file fix — yes, just use Claude Code directly. Use SupaConductor when: The work touches 3 or more files; You'd normally plan before coding; You want automated quality checks; You want the work done right the first time. You can also use individual commands without the full loop.

Asked by Fumiko Sato · Sep 25, 2025

Does this work with Cursor, Windsurf, or other AI tools?

No — SupaConductor is a Claude Code plugin that requires Claude Code's plugin system (agents, skills, slash commands, hooks). However, the conductor/ directory it creates is just Markdown files. Any AI tool can read them. If you start with SupaConductor and switch tools later, your specs, plans, and documentation remain useful.

Asked by Henrik Dahl · Sep 16, 2025

How much of my context window does this use?

Skills use progressive disclosure — only ~100 tokens each for metadata. Full instructions load only when activated (typically under 5,000 tokens each). The 39 skills are not loaded all at once. Agents run as separate conversations with their own context windows, so they don't fill up your main conversation. | Component | Context Used | When | |-----------|-------------|------| | Orchestrator | ~4,000 tokens | Active during /go | | Planner | ~3,000 tokens | During planning only | | Evaluator | ~2,500 tokens each | Only the active evaluator loads | | Board meeting | ~5,000 tokens | On-demand only | | Idle | ~500 tokens | Between steps |

Asked by Beatriz Costa · Sep 1, 2025

How much does this cost in API credits?

SupaConductor uses the same Claude API as normal Claude Code — it just structures the work more carefully. Because it runs multiple agents (planning, execution, evaluation), it uses roughly 3-5x the API calls compared to doing everything manually in one conversation. SupaConductor optimizes costs automatically: it uses Opus (the most capable model) for planning and evaluation, and Sonnet (faster, cheaper) for execution tasks. Ways to reduce cost: Use /orchestrator-supaconductor:implement if you write specs yourself; skip board meetings for small features (they're opt-in); use human-in-the-loop mode to stay in control of scope.

Asked by Dmitri Volkov · Aug 18, 2025

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