Initial commit

.claude-plugin/plugin.json

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+{
+  "name": "model-deployment-helper",
+  "description": "Deploy ML models to production",
+  "version": "1.0.0",
+  "author": {
+    "name": "Claude Code Plugins",
+    "email": "[email protected]"
+  },
+  "skills": [
+    "./skills"
+  ],
+  "commands": [
+    "./commands"
+  ]
+}

README.md

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+# model-deployment-helper
+
+Deploy ML models to production

commands/deploy-model.md

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+---
+description: Execute AI/ML task with intelligent automation
+---
+
+# AI/ML Task Executor
+
+You are an AI/ML specialist. When this command is invoked:
+
+1. Analyze the current context and requirements
+2. Generate appropriate code for the ML task
+3. Include data validation and error handling
+4. Provide performance metrics and insights
+5. Save artifacts and generate documentation
+
+Support modern ML frameworks and best practices.

plugin.lock.json

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+{
+  "$schema": "internal://schemas/plugin.lock.v1.json",
+  "pluginId": "gh:jeremylongshore/claude-code-plugins-plus:plugins/ai-ml/model-deployment-helper",
+  "normalized": {
+    "repo": null,
+    "ref": "refs/tags/v20251128.0",
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+    "generatedAt": "2025-11-28T10:18:34.936252Z",
+    "toolVersion": "publish_plugins.py@0.2.0"
+  },
+  "origin": {
+    "remote": "git@github.com:zhongweili/42plugin-data.git",
+    "branch": "master",
+    "commit": "aa1497ed0949fd50e99e70d6324a29c5b34f9390",
+    "repoRoot": "/Users/zhongweili/projects/openmind/42plugin-data"
+  },
+  "manifest": {
+    "name": "model-deployment-helper",
+    "description": "Deploy ML models to production",
+    "version": "1.0.0"
+  },
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+    "flags": []
+  }
+}

skills/model-deployment-helper/SKILL.md

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+---
+name: deploying-machine-learning-models
+description: |
+  This skill enables Claude to deploy machine learning models to production environments. It automates the deployment workflow, implements best practices for serving models, optimizes performance, and handles potential errors. Use this skill when the user requests to deploy a model, serve a model via an API, or put a trained model into a production environment. The skill is triggered by requests containing terms like "deploy model," "productionize model," "serve model," or "model deployment."
+allowed-tools: Read, Write, Edit, Grep, Glob, Bash
+version: 1.0.0
+---
+
+## Overview
+
+This skill streamlines the process of deploying machine learning models to production, ensuring efficient and reliable model serving. It leverages automated workflows and best practices to simplify the deployment process and optimize performance.
+
+## How It Works
+
+1. **Analyze Requirements**: The skill analyzes the context and user requirements to determine the appropriate deployment strategy.
+2. **Generate Code**: It generates the necessary code for deploying the model, including API endpoints, data validation, and error handling.
+3. **Deploy Model**: The skill deploys the model to the specified production environment.
+
+## When to Use This Skill
+
+This skill activates when you need to:
+- Deploy a trained machine learning model to a production environment.
+- Serve a model via an API endpoint for real-time predictions.
+- Automate the model deployment process.
+
+## Examples
+
+### Example 1: Deploying a Regression Model
+
+User request: "Deploy my regression model trained on the housing dataset."
+
+The skill will:
+1. Analyze the model and data format.
+2. Generate code for a REST API endpoint to serve the model.
+3. Deploy the model to a cloud-based serving platform.
+
+### Example 2: Productionizing a Classification Model
+
+User request: "Productionize the classification model I just trained."
+
+The skill will:
+1. Create a Docker container for the model.
+2. Implement data validation and error handling.
+3. Deploy the container to a Kubernetes cluster.
+
+## Best Practices
+
+- **Data Validation**: Implement thorough data validation to ensure the model receives correct inputs.
+- **Error Handling**: Include robust error handling to gracefully manage unexpected issues.
+- **Performance Monitoring**: Set up performance monitoring to track model latency and throughput.
+
+## Integration
+
+This skill can be integrated with other tools for model training, data preprocessing, and monitoring.

skills/model-deployment-helper/assets/README.md

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+# Assets
+
+Bundled resources for model-deployment-helper skill
+
+- [ ] model_deployment_template.yaml A YAML template for defining the model deployment configuration, including resource requirements, scaling parameters, and monitoring settings.
+- [ ] dockerfile_example.txt An example Dockerfile for containerizing the model serving application.
+- [ ] example_model_config.json An example JSON configuration file for deploying a sample model.

skills/model-deployment-helper/references/README.md

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+# References
+
+Bundled resources for model-deployment-helper skill
+
+- [ ] model_deployment_best_practices.md A comprehensive guide to best practices for deploying machine learning models, covering topics like containerization, scaling, monitoring, and security.
+- [ ] supported_model_formats.md Documentation on the supported model formats and frameworks, including instructions on how to convert models to compatible formats.
+- [ ] api_endpoint_configuration.md Detailed instructions on configuring the API endpoint for serving the deployed model, including authentication, authorization, and rate limiting.
+- [ ] error_handling_guide.md A guide to handling common errors during model deployment and serving, including troubleshooting tips and debugging techniques.
+- [ ] model_config_schema.json JSON schema defining the structure and requirements for the model configuration file.

skills/model-deployment-helper/scripts/README.md

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+# Scripts
+
+Bundled resources for model-deployment-helper skill
+
+- [ ] deploy_model.py Automates the model deployment process, handling environment setup, dependency installation, and model serving configuration.
+- [ ] monitor_model.py Monitors model performance in production, tracking metrics like latency, throughput, and accuracy, and triggers alerts for anomalies.
+- [ ] rollback_model.py Provides a mechanism to quickly rollback to a previous model version in case of issues with the deployed model.
+- [ ] validate_model_config.py Validates the model configuration file to ensure it adheres to the required schema and contains all necessary parameters.