bentoml-model-packager
BentoML skill for model packaging, serving, and containerization.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bentoml-model-packager/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bentoml-model-packager Compares
| Feature / Agent | bentoml-model-packager | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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'
}
}
}
}
```Related Skills
model
Inspect or change Babysitter model-routing policy by phase.
threat-modeler
Generate threat models using STRIDE, PASTA, or VAST methodologies
urdf-sdf-model
Expert skill for robot model creation and validation in URDF and SDF formats. Generate URDF files with proper link-joint hierarchy, create Xacro macros, calculate inertial properties, configure joint types, and validate models.
topic-modeling-text-mining
Apply LDA, NMF, and other computational methods to discover patterns in large text corpora with appropriate parameter tuning
systems-dynamics-modeler
Skill for building and simulating systems dynamics models
noise-modeler
Quantum noise modeling skill for simulation and hardware characterization
pymc-bayesian-modeler
PyMC probabilistic programming skill for hierarchical Bayesian models in physics data analysis
comsol-multiphysics-modeler
COMSOL finite element skill for multiphysics simulations including electromagnetics, heat transfer, and fluid dynamics
environmental-fate-modeler
Environmental nanosafety skill for modeling nanomaterial environmental fate and transport
cad-modeling
Expert skill for parametric 3D CAD model development with design intent and configuration management
stan-bayesian-modeling
Stan probabilistic programming for Bayesian inference
linear-program-modeler
Mathematical programming skill for formulating and solving linear programming models for resource allocation, production planning, and capacity optimization.