MiniMax拥有开源 MCode Stack，工作流程和agent技能，团队用来构建MiniMax代码。 MiniMax MCode Stack agent技能于2026年10月28日以麻省理工学院许可证上市。 这不是一个迅速的集合。 每块是直接安装入MiniMax码的SKILL.md技能或核对表。 对于开发者来说，它将取用工程习惯转化为agent可以实际运行的步。
TL; DR 数据
- 大小：2个堆，17个技能(15个来自Ronny,2个来自Jiachen),5个核对表，5个同伴指出。
- 运行于：任何运行MiniMax代码(Node.js 22.19+)或Claude Code的机器。 没有GPU。 技能是简单的Markdown。
- 性能：没有公布基准测试或eval结果。
- 最佳：长命令检查，对长空壳命令和背景任务进行10分审计。
什么是迷你麦克斯MCode Stack?
MCode Stack是一款由MiniMax的MiniMax代码团队出品的开源回波技术。 MiniMax代码是MiniMax的开源终端编码agent，同样是MIT许可。 该堆栈没有MiniMax代码源代码。 它只有工作流程、技能和清单。
复出相配有一列名为MCode Engineering 指出。 每一个注释都解释了一个想法。 重案组使这个想法可以运行。 已发布指出覆盖子代理，工具上下文，收缩对内存和长击命令。 即将发布关于每起交易的背景税的说明。
龙尼的堆里有什么技能？
MiniMax的堆船 15个技能，按工作日的瞬间分类：
- 计划与分拆(3)：分试剂检查，工程-小四分试，工程-变化描述。
- 背景和工具(2)：工具-预算-审计，上下文-税-审计。
- 长会和命令(3)：长会检查，交接医生，长会检查。
- 审查(4)：独立审查、工程编码审查、工程审查评论、工程审查反馈。
- 实验和热补(3)：利用-实验、轨迹审查、工程-应急改变。
几个想法很突出。 独立审查给审查者提供了精确的3个投入：diff，要求和测试结果。 它故意拒绝提交人的谈话。 推理：继承作者背景的审阅员重复了作者的盲点。
- 子剂- 分解- 检查称只有在上下文是测量到的问题时才能分割工作。 按团队或问题类型划分并不是合理的理由。
- 工具-预算-审计分离2个成本：工具定义和工具结果。 工具搜索主要为首个削减。 程序工具主要将削减称为第二。
- 利用实验每个实验执行一个改变。 它跟踪翻转的任务，每个任务的请求，符号，缓存命中率和墙钟时间。
-长命令清单有10个项目它询问将命令移动到背景是否重新运行进程。 它还询问未读结果是否在收缩后存活。
6个工程*技能由Google工程实践改编而来。 这些材料仍保留在CC-BY 3.0之下
如何安装MCode Stack?
有3条路径，按读取母：
- 一种技能：复制回放，将一个技能文件夹复制到~.agents/技能/。
- 整个堆栈作为本地插件：复制堆栈/ Ronny 输入 MiniMax 代码的本地插件目录。
- 市场：在MiniMax代码桌面上，增加MiniMax-AI/mcode-stack市场。 这一方案正在逐步推出。
同名目录在Claude Code与/plugin market 添加了 MiniMax-AI/mcode-stack. MiniMax代码还写作~.claude/技能/和~/.codex/技能/. 技能使用常见的SKILL.md格式，所以大多数工作在其他支持技能的agents.
MCode Stack如何与其他技能包相比？
MCode Stack比两者都窄。 它针对设计出agent拉带的人，而不仅仅是使用agents的人。 超能力覆盖了今天更多的绳子。
关键外卖
- MiniMax 开源 17 SKILL.md技能和5份麻省理工学院的核对表。
- 技能与MCode Engineering 指出对接，将散文变成可操作的检查。
- 独立审查只向审查者提供diff、要求和测试结果。
- 通过插件市场安装入MiniMax代码或Claude Code。
- 还没有出版杂志，所以把它当作游戏本，不是证据。
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米哈尔·萨特(Michal Sutter)是一名数据科学专业人员，拥有帕多瓦大学的数据科学硕士。 在统计分析、机器学习和数据工程的坚实基础上，Michal出色地将复杂的数据集转化为可操作的见解。
本文由 AI宝库自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
MiniMax has open-sourced MCode Stack, the workflows and agent skills its team uses to build MiniMax Code. The MiniMax MCode Stack agent skills went public on October 28, 2026 under the MIT license. It is not a prompt collection. Each piece is a SKILL.md skill or checklist that installs directly into MiniMax Code. For developers, it turns harness engineering habits into steps an agent can actually run.
TL;DR
- Size: 2 stacks, 17 skills (15 from Ronny, 2 from Jiachen), 5 checklists, 5 companion notes.
- Runs on: Any machine running MiniMax Code (Node.js 22.19+) or Claude Code. No GPU. Skills are plain Markdown.
- Performance: No benchmarks or eval results published.
- Best: long-command-check, a 10-point audit for long shell commands and background tasks.
What is MiniMax MCode Stack?
MCode Stack is an open-source repo of agent skills from MiniMax’s MiniMax Code team. MiniMax Code is the MiniMax’s open-source terminal coding agent, also MIT-licensed. The stack holds no MiniMax Code source code. It holds only workflows, skills and checklists.
The repo pairs with a column called MCode Engineering Notes. Each note explains one idea. The repo makes that idea runnable. Published notes cover subagents, tool context, compaction versus memory and long bash commands. A note on per-turn context tax is marked upcoming.
What skills are inside Ronny’s stack?
MiniMax’s stack ships 15 skills, grouped by moment in the working day:
- Plan and split (3): subagent-split-check, engineering-small-prs, engineering-change-descriptions.
- Context and tools (2): tool-budget-audit, context-tax-audit.
- Long sessions and commands (3): long-session-check, handoff-doc, long-command-check.
- Review (4): independent-review, engineering-code-review, engineering-review-comments, engineering-review-feedback.
- Experiments and hotfixes (3): harness-experiment, trajectory-review, engineering-emergency-changes.
A few ideas stand out. independent-review gives the reviewer exactly 3 inputs: the diff, the requirement and test results. It deliberately withholds the author’s conversation. The reasoning: a reviewer inheriting the author’s context repeats the author’s blind spots.
- subagent-split-check says to split work only when context is the measured problem. Splitting by team or question type is not a valid reason.
- tool-budget-audit separates 2 costs: tool definitions and tool results. Tool search mainly cuts the first. Programmatic tool calling mainly cuts the second.
- harness-experiment enforces 1 change per experiment. It tracks flipped tasks, requests per task, tokens, cache hit rate and wall-clock time.
- The long-command checklist has 10 items. It asks whether moving a command to background re-runs the process. It also asks whether unread results survive compaction.
The 6 engineering-* skills are adapted from Google Engineering Practices. That material stays under CC-BY 3.0, per the stack’s NOTICE.
How do you install MCode Stack?
There are 3 paths, per the README:
- One skill: clone the repo and copy a skill folder into ~/.agents/skills/.
- Whole stack as a local plugin: copy stacks/ronny into MiniMax Code’s local plugin directory.
- Marketplace: in MiniMax Code desktop, add the MiniMax-AI/mcode-stack marketplace. This option is rolling out gradually.
The same catalog works in Claude Code with /plugin marketplace add MiniMax-AI/mcode-stack. MiniMax Code also reads ~/.claude/skills/ and ~/.codex/skills/. The skills use the common SKILL.md format, so most work in other agents that support skills.
How does MCode Stack compare with other skill packs?
MCode Stack is narrower than both. It targets people who design agent harnesses, not only people who use agents. Superpowers covers far more harnesses today.
Key Takeaways
- MiniMax open-sourced 17 SKILL.md skills and 5 checklists under MIT.
- Skills pair with MCode Engineering Notes, turning essays into runnable checks.
- independent-review feeds a reviewer only the diff, requirement and test results.
- Installs into MiniMax Code or Claude Code via a plugin marketplace.
- No published evals yet, so treat it as a playbook, not proof.
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Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.
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