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Ml Pipeline Starter
by
Sunshine-del-ux
· GitHub ↗
· v1.0.0
335
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0
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1
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1
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Install in OpenClaw
/install ml-pipeline-starter
Description
Build and deploy production ML pipelines with data processing, model training, evaluation, and deployment using TensorFlow, PyTorch, or Scikit-learn.
README (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
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install ml-pipeline-starter - After installation, invoke the skill by name or use
/ml-pipeline-starter - Provide required inputs per the skill's parameter spec and get structured output
Version History
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.
Metadata
Frequently Asked Questions
What is Ml Pipeline Starter?
Build and deploy production ML pipelines with data processing, model training, evaluation, and deployment using TensorFlow, PyTorch, or Scikit-learn. It is an AI Agent Skill for Claude Code / OpenClaw, with 335 downloads so far.
How do I install Ml Pipeline Starter?
Run "/install ml-pipeline-starter" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Ml Pipeline Starter free?
Yes, Ml Pipeline Starter is completely free (open-source). You can download, install and use it at no cost.
Which platforms does Ml Pipeline Starter support?
Ml Pipeline Starter is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Ml Pipeline Starter?
It is built and maintained by Sunshine-del-ux (@sunshine-del-ux); the current version is v1.0.0.
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