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    Use your most capable model to audit your codebase

    ...Its core idea is to use a stronger model for understanding, judgment, and planning, then hand the actual implementation to cheaper or separate execution agents. The skill does not modify code by default, because its main output is a self-contained plan. It can run full, quick, deep, security-focused, branch-scoped, and feature-suggestion audits. It maps repository conventions, identifies findings, writes ordered markdown plans, and records verification steps such as lint, build, and test commands. It is useful for teams that want AI-assisted technical planning without letting the planning model directly change production code.
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  • 2
    LiteMultiAgent

    LiteMultiAgent

    The Library for LLM-based multi-agent applications

    LiteMultiAgent is a lightweight and extensible multi-agent reinforcement learning (MARL) platform designed for rapid experimentation. It allows researchers to design and test coordination, competition, and collaboration scenarios in simulated environments.
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  • 3
    Mini Agent

    Mini Agent

    A minimal yet professional single agent demo project

    Mini-Agent is a minimal yet production-minded demo project that shows how to build a serious command-line AI agent around the MiniMax-M2 model. It is designed both as a reference implementation and as a usable agent, demonstrating a full execution loop that includes planning, tool calls, and iterative refinement. The project exposes an Anthropic-compatible API interface and fully supports interleaved thinking, letting the agent alternate between reasoning steps and tool invocations during...
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