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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    Agent Workflow Controller

    2

    Design and audit complex multi-agent workflows with rigorous ownership, evidence gates, and failure recovery policies.

    Free

    29 usesSecurity scanned
    agent-workflow-controller

    agent-workflow-controller

    Example session with this skill installed

    Design a workflow for fixing the JWT leak in /src/auth/ that separates implementation from security review and requires passing test logs for verification.

    • Read your context and instructions
    • Compiled the agent-workflow-controller

    Workflow Type: Implementation -> Review -> Verify

    Roles

    RoleOwnsMust Not Do
    Implementer/src/auth/Edit /tests/
    ReviewerSecurity AuditModify code

    Verification Plan

    ClaimMethodEvidence
    JWT fixnpm testTest pass logs

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

    About this skill

    What it does

    The Agent Workflow Controller is a sophisticated architectural skill designed for developers and AI orchestrators who need to manage complex, multi-step, or multi-agent tasks. It acts as the "operating system" for your agent's logic, transforming vague requests into structured execution plans with defined phases, roles, and boundaries.

    Problem it solves

    Standard LLM prompts often struggle with "agentic drift," where agents duplicate work, provide unverifiable outputs, or fail during handoffs. This skill prevents collaboration breakdown by enforcing ownership, explicit exit gates, and evidence-based verification. It moves beyond simple prompting by introducing professional workflow engineering—ensuring your agents act with the precision of a coordinated team rather than a single black box.

    Supported Workflows

    • Discovery-Execute-Verify: For tasks where the state is unknown.
    • Approval-Gated: For high-risk, destructive, or financial operations.
    • Parallel Slice: For independent sub-tasks requiring disjoint ownership.
    • Failure-Repair Loop: For systematic debugging when a process fails.

    The Output

    The skill produces a comprehensive Workflow Plan. This includes a mission statement, complexity classification, a role/purpose matrix, ownership maps for files and decisions, and concrete verification plans that tie every claim to specific evidence (logs, diffs, or citations).

    • Planning only; does not execute code or launch sub-agents.
    • Best suited for complex logic; adds unnecessary overhead to simple tasks.

    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 6 months ago

    • Free with an Agensi account

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    Listed6 months ago

    What's inside

    Frequently Asked Questions

    This skill functions as an architectural layer that organizes how multiple agents interact. It enforces strict ownership, evidence-based verification gates, and recovery protocols to prevent 'agentic drift' and coordination failures.

    The skill is designed to be model-agnostic and works with any modern LLM or orchestration framework that supports structured planning and multi-agent roles. It is particularly effective for complex environments like AutoGPT, LangGraph, or custom-built agent swarms.

    You receive the complete workflow logic, specialized templates for different execution cycles (like Discovery-Execute-Verify), and the 'Operating System' prompts used to enforce these boundaries. It also includes comprehensive documentation on how to integrate these gates into your existing stack.

    Unlike linear automations, this skill includes a 'Failure-Repair Loop' that allows agents to systematically debug their own errors. It uses evidence gates to ensure an agent cannot proceed until the previous step's output is verified against specific logs or artifacts.

    Setup involves mapping your agents to the provided role/purpose matrix and defining your 'exit gates.' Most developers can integrate the controller into their existing agentic pipelines in under an hour by following the included implementation guide.

    New in Agents & Orchestration