Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    Agent to Agent Handoff

    2

    Standardize agent collaboration with high-density mission packets, strict authority boundaries, and return contracts.

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    agent-to-agent-handoff

    agent-to-agent-handoff

    Example session with this skill installed

    I've finished the auth research. Create a mission packet for a coder to implement the password-reset API in src/api/auth.ts. Don't touch the DB. It needs to pass auth tests.

    • Read your context and instructions
    • Compiled the agent-to-agent-handoff

    Mission

    Implement the password-reset API endpoint.

    Scope

    • In scope: src/api/auth.ts
    • Do not touch: Database schema

    Acceptance Criteria

    • Returns 200 on success.
    • Test with npm run test:auth.

    Return Contract

    Provide the diff, test logs, and any remaining security risks.

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

    About this skill

    Eliminate Context Drift in Multi-Agent Workflows

    In complex AI development workflows, moving tasks between specialized agents—like transitioning from a researcher to a coder, or a developer to a reviewer—often leads to "context rot." Information is lost, secrets are accidentally leaked, and receiving agents waste tokens re-discovering facts the sender already knew.

    The Agent-To-Agent Handoff skill provides a professional framework for creating high-density mission packets. It ensures that when one agent hands off to another, the receiver has a clear mission, strict authority boundaries, and verifiable acceptance criteria. This is specifically designed for developers building agentic loops, sub-agent architectures, or long-running tasks that span multiple sessions.

    What it does

    • Context Packing: Strips away chat noise and summarizes only the verified facts and state required for the next step.
    • Authority Routing: Explicitly defines what the receiving agent is allowed to change, delete, or execute.
    • Structured Return Contracts: Defines exactly how the receiver must report back, ensuring results are based on evidence rather than "vibes."
    • Failure Recovery: Generates specialized packets to help repair agents recover from tool errors or failed runs.

    Whether you are using Claude Code, Cursor, or custom LangChain/AutoGPT runtimes, this skill ensures your agentic ecosystem operates with the precision of a professional engineering team.

    • Does not execute the subagent calls; only generates the handoff packet.
    • Relies on user-provided state accuracy.
    • Optimal for technical workflows over creative writing.

    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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    1 use

    Times used to date

    Money-back guarantee

    Doesn't do what its page promises? You get your money back.

    Trust & safety

    Security scan

    Security scan passed 5 months ago

    Permissions

    No special permissions declared or detected

    Listed5 months ago

    What's inside

    Frequently Asked Questions

    This skill solves 'context rot' by replacing messy chat histories with high-density mission packets that focus solely on verified facts, state data, and strict authority boundaries.

    It is designed to be platform-agnostic, working seamlessly with Claude Code, Cursor, and popular frameworks like LangChain or AutoGPT.

    You receive the core implementation logic for generating mission packets, templates for authority routing, and standardized return contract structures for your agents.

    Setup is straightforward; you integrate the provided logic into your agent's system prompt or workflow controller to start generating structured packets immediately.

    Yes, customers receive periodic updates that include optimizations for newer LLM models and refined failure recovery templates as agentic best practices evolve.

    Unlike generic prompts, this skill uses 'Return Contracts' that force the receiving agent to provide evidence-based results, eliminating guesswork and 'vibe-based' reporting.

    New in Agents & Orchestration