Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datarobot-oss/datarobot-agent-skills --skill datarobot-model-deploymentgit clone --depth 1 https://github.com/datarobot-oss/datarobot-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-deployment)<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-deployment"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-deployment/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-deployment"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-deployment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00042 | $0.01501 |
| Opus 5 | $0.00021 | $0.00750 |
| Sonnet 5 | $0.00008 | $0.00300 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
datarobot-model-deployment scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataRobot Model Deployment Skill
This skill provides comprehensive guidance for deploying models, managing deployment configurations, and operating production deployments.
Quick Start
Most common use case: Deploy a trained model to production
- Get best model: Find the best model from a project (highest metric score)
- Create deployment:
create_deployment(model_id, deployment_name)to deploy model - Get endpoint:
get_deployment_endpoint(deployment_id)to retrieve prediction URL
Example: "Deploy the best model from project abc123 as 'Sales Prediction v1'"
When to use this skill
Use this skill when you need to:
- Deploy trained models to production
- Configure deployment settings and environments
- Manage multiple deployments
- Replace a deployment’s champion model with a new model version
- Configure prediction servers and environments
- Monitor deployment health and status
- Manage deployment access and permissions
Key capabilities
1. Deployment Creation
- Deploy models from projects or registered models
- Choose prediction environment (DataRobot Serverless, external)
- Configure deployment settings (challenger models, A/B testing)
- Set up deployment metadata and descriptions
2. Deployment Configuration
- Configure prediction servers and environments
- Set up batch prediction settings
- Configure real-time prediction endpoints
- Manage deployment credentials and access
3. Deployment Management
- Replace deployment champion model (model swap)
- Enable/disable deployments
- Manage challenger models for A/B testing
- Configure replacement policies
4. Deployment Operations
- Get deployment information and status
- Retrieve deployment endpoints
- Manage deployment settings
- Handle deployment errors and issues
Workflow examples
Example 1: Deploy a model to production
User request: "Deploy the best model from project abc123 to production with the name 'Sales Prediction v1'."
Agent workflow:
- Get the best model from the project (highest metric score)
- Create a new deployment with the model
- Configure deployment settings (name, description, environment)
- Set up prediction environment (DataRobot Serverless recommended)
- Retrieve deployment endpoint and credentials
- Verify deployment is active and ready for predictions
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 217 lines · 42 tokens per session scan A 852dc4b5ba4f
datarobot-model-deployment is a skill published in the GitHub repository datarobot-oss/datarobot-agent-skills (25 stars, last pushed today), licensed Apache-2.0. It adds 42 tokens to every session and 1,501 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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