Multi Agent Coordinator

    4

    Coordinate specialized AI agent roles for complex planning, implementation, and verification workflows.

    $7

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    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    Multi Agent Coordinator

    Example session with this skill installed

    Our auth service is experiencing intermittent failures during high load. Coordinate a full investigation to find the race condition, implement a fix, and verify the patch.

    • Read your context and instructions
    • Compiled the multi agent coordinator

    Roles: Planner, Researcher, Implementer, Verifier.
    Action: Isolated race condition in auth.ts.
    Verified: log_output_v2.txt confirms 4ms timeout resolution.
    Inferred: Possible regression in legacy node-14 clusters.
    Unknown: Exact memory leak floor.
    Next: Deploy patch to staging-v12.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Orchestrate Highly Complex AI Workflows

    Modern AI agents often struggle with "tunnel vision" when tackling large-scale engineering tasks. The Multi-Agent Coordinator skill solves this by transforming your agent into a project manager capable of breaking down complex, ambiguous, or high-risk tasks into specialized, narrow agent roles. Instead of one agent trying to do everything at once, this skill provides a framework for parallel processing and independent validation.

    What it does

    The skill provides a rigorous methodology for triaging tasks and deploying specialized "roles"—such as Planners, Researchers, Implementers, Reviewers, and Verifiers. It ensures that work is sequenced correctly, evidence is shared without corruption, and conflicts are resolved through evidence-backed synthesis.

    • Role Selection: Dynamically assigns the smallest effective team (from Planner to Debugger) based on task risk.
    • Parallel Execution: Optimized logic for running independent research and reviews simultaneously.
    • Conflict Resolution: Specialized debug modes to handle disagreements between agent outputs.
    • Rigorous Synthesis: Merges multi-agent evidence into a single, verified report.

    Why use this skill?

    Directly prompting an AI to "be a team" often leads to role confusion and hallucination. This skill implements strict handoff rules and state management, ensuring agents don't duplicate work or reason from outdated artifacts. It is ideal for cross-file refactoring, complex bug hunting, and architectural planning where the cost of error is high.

    Output format

    Every execution concludes with a standardized report including: roles used, specific actions taken per role, verified evidence (commands/files), inferences made, and remaining unknowns.

    • High token consumption due to multiple role context windows.
    • Latency increases when sequencing more than 3 interdependent agents.
    • Not suitable for trivial, single-file edits.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Trust & safety

    Security scan

    Security scan passed 7 months ago

    Permissions

    Network Access
    Listed7 months ago

    What's inside

    Frequently Asked Questions

    This skill prevents 'tunnel vision' and hallucinations by breaking complex tasks into specialized roles like Planners and Verifiers, ensuring each stage of a project is independently validated.

    The Multi-Agent Coordinator is designed to integrate with most major LLM-based agents that support tool-calling and autonomous workflow execution.

    You will receive the core orchestration logic, role definition templates, standardized handoff protocols, and the final reporting framework.

    No, the skill is designed to scale dynamically; it assesses the task's complexity and risk to assign the smallest effective team required, saving on token costs.

    Yes, the skill includes specialized logic for parallel execution, allowing independent research and review tasks to be performed simultaneously for faster results.

    The skill produces a standardized report containing actions taken by each role, verified evidence, inferences made, and any remaining unknowns for full transparency.

    New in Agents & Orchestration