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Dispatching Parallel Agents (Grade A) logo

Dispatching Parallel Agents (Grade A)Security-tested data-ai skill for Claude AI. Grade A. Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies

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Daniel NikulshynReviewed by Daniel Nikulshyn·Updated July 2026

Overview

Dispatching Parallel Agents is a security-tested data-ai skill for Claude AI, designed to be used when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies. The skill is based on the core principle of dispatching one agent per independent problem domain, allowing them to work concurrently. When to use this skill includes scenarios such as: - Multiple failures with different root causes across 3+ test files - Multiple subsystems broken independently - Each problem can be understood without context from others - No shared state between investigations Do not use this skill when: - Failures are related (fixing one might fix others) - Need to understand the full system state - Agents would interfere with each other The pattern includes four steps: identifying independent domains, creating focused agent tasks, dispatching in parallel, and reviewing and integrating the results. The agent prompt structure should be focused, self-contained, and specific about output. This skill can help save time by investigating multiple independent problems in parallel, rather than sequentially.

Key features

  • Dispatching multiple agents in parallel
  • Focused agent tasks with specific scopes and goals
  • Clear constraints and expected output for each agent
  • Review and integration of agent results

Pricing

Model
Free
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Skills
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Use cases

Multiple Test File Failures

Fixing multiple failing test files with different root causes, such as tool approval flow, batch completion behavior, and abort functionality.

Independent Subsystem Issues

Investigating and resolving issues in multiple subsystems that are broken independently, without shared state or dependencies.

Pros & Cons

Pros

  • Saves time by investigating multiple failures in parallel
  • Improves efficiency by focusing on independent problem domains
  • Enhances scalability for handling multiple test files or subsystems

Cons

  • Not suitable for related failures where fixing one might fix others
  • Requires careful consideration to avoid agent interference
  • May not be effective when full system state needs to be understood

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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 Zofia Kaczmarek · Jan 7, 2026

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 Jamal Carter · Dec 9, 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 Winifred Adeyemi · Dec 4, 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 Frank Müller · Nov 21, 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 George Papadakis · Nov 16, 2025

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