bentoml-model-packager

BentoML skill for model packaging, serving, and containerization.

509 stars

Best use case

bentoml-model-packager is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

BentoML skill for model packaging, serving, and containerization.

Teams using bentoml-model-packager should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/bentoml-model-packager/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/specializations/data-science-ml/skills/bentoml-model-packager/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/bentoml-model-packager/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How bentoml-model-packager Compares

Feature / Agentbentoml-model-packagerStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

BentoML skill for model packaging, serving, and containerization.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# bentoml-model-packager

## Overview

BentoML skill for model packaging, serving, and containerization with support for multiple ML frameworks.

## Capabilities

- Bento creation and versioning
- Multi-framework model support (sklearn, PyTorch, TensorFlow, etc.)
- API endpoint definition with validation
- Docker containerization
- Kubernetes deployment YAML generation
- Adaptive batching configuration
- Model signatures and runners
- Service composition

## Target Processes

- Model Deployment Pipeline with Canary Release
- Model Training Pipeline
- ML System Integration Testing

## Tools and Libraries

- BentoML
- Docker
- Kubernetes

## Input Schema

```json
{
  "type": "object",
  "required": ["action"],
  "properties": {
    "action": {
      "type": "string",
      "enum": ["save", "build", "serve", "containerize", "push", "list"],
      "description": "BentoML action to perform"
    },
    "modelConfig": {
      "type": "object",
      "properties": {
        "name": { "type": "string" },
        "framework": { "type": "string" },
        "modelPath": { "type": "string" },
        "signatures": { "type": "object" }
      }
    },
    "serviceConfig": {
      "type": "object",
      "properties": {
        "servicePath": { "type": "string" },
        "port": { "type": "integer" },
        "workers": { "type": "integer" },
        "batchConfig": {
          "type": "object",
          "properties": {
            "maxBatchSize": { "type": "integer" },
            "maxLatencyMs": { "type": "integer" }
          }
        }
      }
    },
    "buildConfig": {
      "type": "object",
      "properties": {
        "bentoName": { "type": "string" },
        "version": { "type": "string" },
        "includeFiles": { "type": "array", "items": { "type": "string" } },
        "pythonRequirements": { "type": "string" }
      }
    },
    "containerConfig": {
      "type": "object",
      "properties": {
        "imageName": { "type": "string" },
        "registry": { "type": "string" },
        "dockerOptions": { "type": "object" }
      }
    }
  }
}
```

## Output Schema

```json
{
  "type": "object",
  "required": ["status", "action"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "action": {
      "type": "string"
    },
    "modelTag": {
      "type": "string"
    },
    "bentoTag": {
      "type": "string"
    },
    "imageTag": {
      "type": "string"
    },
    "endpoint": {
      "type": "string"
    },
    "kubernetesYaml": {
      "type": "string"
    }
  }
}
```

## Usage Example

```javascript
{
  kind: 'skill',
  title: 'Package and containerize model',
  skill: {
    name: 'bentoml-model-packager',
    context: {
      action: 'containerize',
      modelConfig: {
        name: 'fraud_classifier',
        framework: 'sklearn',
        modelPath: 'models/fraud_model.pkl'
      },
      buildConfig: {
        bentoName: 'fraud-service',
        version: '1.0.0',
        pythonRequirements: 'requirements.txt'
      },
      containerConfig: {
        imageName: 'fraud-service',
        registry: 'gcr.io/my-project'
      }
    }
  }
}
```

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