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在 OpenClaw 中安装
/install mhc-layer-impl-mhc-algorithm
功能描述
Implement mHC (Manifold-Constrained Hyper-Connections) for stabilizing deep network training. Use when implementing residual connection improvements with dou...
安全使用建议
This skill appears internally consistent and focused on implementing mHC in PyTorch. Before using: (1) Review the code snippets and references to ensure they fit your model and framework versions; (2) install PyTorch via the official channel appropriate for your OS/GPU (avoid arbitrary wheel URLs); (3) run the examples in an isolated environment (virtualenv/container) because mHC multiplies memory usage by num_streams; (4) confirm the referenced paper(s) if you need research provenance. If you need broader audits (license, benchmark results, or GPU/CUDA compatibility), request the author's complete implementation or test on a small toy model first.
功能分析
Type: OpenClaw Skill
Name: mhc-layer-impl-mhc-algorithm
Version: 0.1.0
The skill bundle provides a legitimate implementation of the Manifold-Constrained Hyper-Connections (mHC) algorithm for PyTorch, based on DeepSeek research. The code in SKILL.md and the reference files (module-implementation.md, sinkhorn-knopp.md) focuses entirely on mathematical operations and neural network architecture, using standard libraries like torch and einops. There are no indicators of data exfiltration, malicious execution, or prompt injection.
能力评估
Purpose & Capability
The name/description (mHC for stabilizing deep nets) aligns with the contents: PyTorch code snippets, Sinkhorn projection, and GPT integration patterns. The only external dependency suggested (torch, einops, numpy) is appropriate for the stated goal.
Instruction Scope
SKILL.md contains concrete implementation guidance, example code, and algorithm notes. Instructions are confined to model-code concerns (tensor shapes, Sinkhorn iterations, wrapping layers) and do not instruct reading arbitrary files, accessing environment variables, or contacting external endpoints beyond citing arXiv links.
Install Mechanism
No install spec is embedded; the doc recommends 'pip install torch einops numpy'. This is expected for a PyTorch implementation but be aware 'pip install torch' can be large and platform-specific (CUDA variants). There are no downloads from untrusted URLs or archive/extract steps.
Credentials
The skill requests no environment variables, credentials, or config paths. All required resources are typical Python packages needed to run the examples.
Persistence & Privilege
The skill is instruction-only, does not request 'always: true', and does not instruct changing agent-wide configuration or storing credentials. It does not grant persistent or elevated privileges.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install mhc-layer-impl-mhc-algorithm - 安装完成后，直接呼叫该 Skill 的名称或使用
/mhc-layer-impl-mhc-algorithm触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v0.1.0
Bulk publish from all-task-skills-dedup
元数据
常见问题
mhc-algorithm 是什么？
Implement mHC (Manifold-Constrained Hyper-Connections) for stabilizing deep network training. Use when implementing residual connection improvements with dou... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 76 次。
如何安装 mhc-algorithm？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install mhc-layer-impl-mhc-algorithm」即可一键安装，无需额外配置。
mhc-algorithm 是免费的吗？
是的，mhc-algorithm 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
mhc-algorithm 支持哪些平台？
mhc-algorithm 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 mhc-algorithm？
由 lnj22（@lnj22）开发并维护，当前版本 v0.1.0。
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