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Ml Pipeline Starter

Name: Ml Pipeline Starter
Author: sunshine-del-ux

作者 Sunshine-del-ux · GitHub ↗ · v1.0.0

cross-platform ⚠ suspicious

335

总下载

当前安装

版本数

在 OpenClaw 中安装

/install ml-pipeline-starter

功能描述

Build and deploy production ML pipelines with data processing, model training, evaluation, and deployment using TensorFlow, PyTorch, or Scikit-learn.

使用说明 (SKILL.md)

ML Pipeline Starter

Build production ML pipelines.

Features

Data Processing

Data validation
Feature engineering
Data augmentation

Model Training

Hyperparameter tuning
Cross-validation
Model versioning

Evaluation

Metrics tracking
Bias detection
Performance monitoring

Deployment

Model serving
A/B testing
Rollback support

Quick Start

# Create pipeline
./ml-pipeline.sh create my-model

# Train
./ml-pipeline.sh train my-model

# Deploy
./ml-pipeline.sh deploy my-model production

Frameworks

TensorFlow
PyTorch
Scikit-learn

Requirements

Python 3.8+
Docker

Author

Sunshine-del-ux

安全使用建议

This skill is suspicious because it tells you to run ./ml-pipeline.sh but does not include that script or state where it comes from. Before installing or following its instructions: (1) ask the author for the actual ml-pipeline.sh source or a trusted repository link and review its contents; (2) never run unknown shell scripts on your host — inspect them and run them in an isolated environment (container or VM) first; (3) prefer skills that include their tooling or point to official releases (GitHub, PyPI, Docker Hub) and provide installation steps you can audit; (4) if you must run a script from an external source, verify its integrity (checksum/signature) and review for network/exfiltration or privileged system operations. If the author cannot provide the script or a verifiable source, treat the skill as unsafe to run.

功能分析

Type: OpenClaw Skill Name: ml-pipeline-starter Version: 1.0.0 The skill bundle contains standard documentation and metadata for a machine learning pipeline starter kit. The SKILL.md file outlines typical ML workflows such as data processing and model training without any evidence of malicious instructions, prompt injection, or suspicious code patterns. While it references an external script (ml-pipeline.sh) not included in the provided files, the documentation itself is consistent with its stated purpose.

能力评估

⚠ Purpose & Capability

The stated purpose (build/deploy ML pipelines) matches the features listed and required tools (Python, Docker). However, the SKILL.md instructs running a local script (./ml-pipeline.sh) that is not provided, linked, or explained; an instruction-only skill that requires an external script without direction is incoherent and raises questions about how the capability is delivered.

⚠ Instruction Scope

The runtime instructions directly call ./ml-pipeline.sh (create/train/deploy). Because the skill bundle contains no script, the instructions implicitly rely on a script on the user's system or elsewhere. That gives the agent/user a broad implicit permission to execute arbitrary shell operations via that script; SKILL.md provides no details of the script's behavior, no safety checks, and no source to verify.

✓ Install Mechanism

No install spec is present (instruction-only), so nothing will be written to disk by the skill itself. This is low-risk in terms of automatic installation, but combined with missing script content it shifts risk to whatever ./ml-pipeline.sh the user runs.

✓ Credentials

The skill requests no environment variables, credentials, or config paths. That is proportionate to the described functionality and is a positive signal.

✓ Persistence & Privilege

The skill is not always-included and uses normal user-invocable/autonomous settings. It does not request elevated persistence or modify other skills/configurations.

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 ml-pipeline-starter
安装完成后，直接呼叫该 Skill 的名称或使用 /ml-pipeline-starter 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

v1.0.0

Initial release of ml-pipeline-starter. - Provides data validation, feature engineering, and augmentation. - Supports hyperparameter tuning, cross-validation, and model versioning. - Includes metrics tracking, bias detection, and performance monitoring. - Enables model serving, A/B testing, and rollback capabilities. - Compatible with TensorFlow, PyTorch, and Scikit-learn. - Requires Python 3.8+ and Docker.

元数据

Slug ml-pipeline-starter

版本 1.0.0

许可证 —

累计安装 1

当前安装数 1

历史版本数 1

常见问题