model-deployment
Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
Profile →npx skills add awslabs/agent-plugins --skill model-deployment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Model Deployment Identifies the correct deployment pathway based on model characteristics and generates deployment code. ## Scope This skill supports deploying Nova and OSS models that were fine-tuned through **SageMaker Serverless Model Customization** only. **Not supported:** - Base models (not fine-tuned) - Models fine-tuned through other processes - Full Fine-Tuning (FFT) — only LoRA fine-tuned models are supported ## Prerequisites - The SDK environment has been verified (SDK version, region, execution role). If not done, activate the `sdk-getting-started` skill first. --- ## Principles 1. **One thing at a time.** Each response advances exactly one decision. 2. **Confirm before proceeding.** Wait for the user to agree before moving on. But don't re-ask questions already answered in the conversation — use what you know. 3. **Don't read files until you need them.** Only read pathway references after the pathway is confirmed. 4. **Use what you know.** If conversation history or artifacts already answer a question, confirm your understanding instead of asking again. ## Workflow ### Step 1: Identify the Training Job You need the training job name or ARN. Check the conversation his
- Scope
- Prerequisites
- Principles
- Workflow
- Step 1: Identify the Training Job
- Step 2: Determine Eligible Deployment Targets
- Step 3: Let the User Choose a Deployment Target
- Step 4: Display License Agreement
- Step 5: Follow Pathway Workflow
- Step 6: Post-Deployment Summary
- Troubleshooting
- How to check if a model was LoRA or FFT fine-tuned
What does the model-deployment skill do?
Generates code that deploys fine-tuned models from SageMaker Serverless Model Customization to SageMaker endpoints or Bedrock. Use when the user says "deploy my model", "create an endpoint", "make it available", or asks about deployment options. Identifies the correct deployment pathway (Nova vs OSS), generates deployment code, and handles endpoint configuration.
How do I install it?
Run `npx skills add awslabs/agent-plugins --skill model-deployment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From awslabs/agent-plugins, a repository with 850 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.