Works with the AI tools you already use

    CClaude CodeCCursorCCodex CLIGGitHub CopilotGGemini CLIVVS CodeWWindsurfMManus+14 more

    Constraint Negotiator

    by Al1as

    2

    Expose hidden trade-offs and architect feasible operating envelopes for systems under conflicting pressures.

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    See it in action

    You say

    We need to build a RAG-based search tool. It must be 100% accurate, use the cheapest LLMs to save costs, and have sub-second latency, but management also wants it to cite every single source.

    Your agent does

    OBJECTIVE-CONSTRAINT COLLISION MAP

    • Collision: Accuracy vs. Cost (CRITICAL)
    • Tension: Cheap models lack the reasoning for 100% accuracy in complex RAG tasks.
    • Truth: You are implicitly choosing cost over reliability.

    VERDICT

    Impossible brief disguised as ambition.

    What you get

    Identify critical collisions between latent goals and hard resource limits.Define the realistic operating envelope for high-stakes AI agent workflows.Expose the implicit trade-offs being made in a product roadmap.Separate hard technical constraints from soft political preferences.

    About this skill

    What it does

    The Constraint Negotiator is a specialized framework for developers, architects, and product leads who are facing "impossible" briefs. It explicitly identifies the hidden tensions in complex systems—like the trade-off between latency and safety, or customization and maintainability—and forces a structured negotiation to find a feasible path forward.

    Problem it solves

    Most projects fail not due to a lack of talent, but because they are governed by incompatible demands (e.g., "fast, cheap, and secure"). This skill exposes the "negotiation debt" that teams accumulate when they pretend trade-offs don't exist, preventing the delivery of brittle, compromise-heavy solutions that satisfy no one.

    Output & Benefits

    The skill produces a detailed "Negotiation Status" report, including a Collision Map of conflicting objectives, a Dominant Constraint Diagnosis, and a Feasible Operating Envelope. Unlike simple preference matrices, it provides 2-4 viable paths with explicit sacrifices, ensuring you don't commit to something operationally unstable or mathematically impossible.

    Supported Environments

    Ideal for AI agent design, system architecture, product roadmapping, and multi-stakeholder governance. It bridges the gap between high-level ambition and operational reality.

    How to install

    Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.

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    Creator

    Al1as
    Al1as

    40 skills on Agensi

    I create premium niche AI agent skills and prompts on PromptBase, where I’ve been actively building since 2022, focused on high-value systems that turn complex workflows into clear, practical, and reusable outputs.

    Frequently Asked Questions

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