Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/datarobot-oss/datarobot-agent-skillsnpx agentmods add skills/datarobot-oss/datarobot-agent-skills/datarobot-model-trainingWrote 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-training)<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-training"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-training/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-training"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-training.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 | $0.00048 | $0.02551 |
| Opus 5 | $0.00024 | $0.01275 |
| Sonnet 5 | $0.00010 | $0.00510 |
| Haiku 4.5 | $0.00005 | $0.00255 |
Grade A, and why
datarobot-model-training 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 12d 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 — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataRobot Model Training Skill
This skill provides guidance for the complete model training workflow in DataRobot, from project creation through model selection and validation.
Quick Start
Most common use case: Create a project and train models
- Create or reuse a Use Case: ask the user if they have an existing Use Case ID to reuse (
dr.UseCase.get(use_case_id)); otherwise create a new one (dr.UseCase.create(name)). Every project needs one linked in Workbench - Upload dataset:
upload_dataset(file_path, dataset_name, use_case_id)to upload training data, associated with the Use Case - Create project:
create_project(dataset_id, project_name, target_column, use_case_id)to create new project, associated with the same Use Case - Start training:
start_automl(project_id, mode)to begin AutoML training
Example: "Create a new project under a 'Sales Forecasting' Use Case with sales_data.csv, set 'revenue' as target, and start Quick AutoML training"
When to use this skill
Use this skill when you need to:
- Create new DataRobot projects
- Upload training datasets
- Configure AutoML experiments
- Monitor training progress
- Select and compare models
- Understand feature engineering results
- Export trained models
Key capabilities
1. Project Management
- Create new projects with appropriate settings
- Upload datasets (CSV, Parquet, database connections)
- Configure project settings (target, partitioning, time series)
- Manage multiple projects and experiments
2. AutoML Configuration
- Set training modes (Quick, Manual, Comprehensive)
- Configure feature engineering options
- Set time limits and resource constraints
- Choose algorithms and model types
3. Training Execution
- Start AutoML training runs
- Monitor training progress
- Handle training errors and warnings
- Pause/resume training if needed
4. Model Analysis
- Compare model performance metrics
- Review feature importance
- Analyze model insights and explanations
- Select best models for deployment
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 299 lines · 48 tokens per session scan A a2caf7a3d562
datarobot-model-training is a skill published in the GitHub repository datarobot-oss/datarobot-agent-skills (25 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 2,551 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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