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-predictionsgit 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-predictions)<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-predictions"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-predictions/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-predictions"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-predictions.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 | $0.00060 | $0.03020 |
| Opus 5 | $0.00030 | $0.01510 |
| Sonnet 5 | $0.00012 | $0.00604 |
| Haiku 4.5 | $0.00006 | $0.00302 |
Grade A, and why
datarobot-predictions 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 — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataRobot Predictions Skill
This skill provides comprehensive guidance for working with DataRobot predictions, including real-time predictions, batch scoring, and generating prediction datasets.
Quick Start
Most common use case: Generate predictions for a deployment
- Get deployment features:
get_deployment_features(deployment_id)to understand required columns - Generate template:
generate_prediction_data_template(deployment_id, n_rows)to create CSV structure - Make predictions: Use
deployment.predict_batch(...)(works for both single-row “real-time” and batch scoring)
Example: "Generate a prediction dataset template for deployment abc123 with 10 rows"
To also explain predictions: pass --max-explanations N to make_prediction.py (or the
max_explanations=N kwarg in code). See Prediction Explanations below.
When to use this skill
Use this skill when you need to:
- Make predictions from deployed DataRobot models
- Explain individual predictions from a deployment (SHAP or XEMP, per-row)
- Generate prediction dataset templates
- Validate prediction data before scoring
- Understand deployment feature requirements
- Perform batch predictions on large datasets
- Get sample training data to understand expected formats
For post-hoc explanations against a training project / leaderboard model (not a deployment), use the
datarobot-model-explainabilityskill instead. This skill covers deployment-time explanations returned alongside scoring.
Key capabilities
1. Understanding Deployment Requirements
Before making predictions, you need to understand what features a deployment requires:
- Feature names and types: Know which columns are needed (numeric, categorical, text, date)
- Feature importance: Understand which features matter most
- Target information: Know what you're predicting
- Time series configuration: If applicable, understand datetime columns and series IDs
What ships with it
4 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 · 362 lines · 60 tokens per session scan A dab1a885f8fe
datarobot-predictions is a skill published in the GitHub repository datarobot-oss/datarobot-agent-skills (25 stars, last pushed today), licensed Apache-2.0. It adds 60 tokens to every session and 3,020 once invoked, about $0.0003 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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