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-explainabilitygit 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-explainability)<a href="https://agentmods.dev/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-explainability"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-explainability/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-explainability"><img src="https://agentmods.dev/badge/skills/datarobot-oss/datarobot-agent-skills/datarobot-model-explainability.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 | $0.00063 | $0.02613 |
| Opus 5 | $0.00032 | $0.01307 |
| Sonnet 5 | $0.00013 | $0.00523 |
| Haiku 4.5 | $0.00006 | $0.00261 |
Grade A, and why
datarobot-model-explainability 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 — 271 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataRobot Model Explainability Skill
This skill covers SHAP insights, XEMP prediction explanations, anomaly explanations, and model diagnostics.
SDK version: Use
datarobot>=3.6.0for the full API set in this skill (ShapDistributionswas added in 3.6;ShapMatrix,ShapImpact, andShapPrevieware available indatarobot>=3.4.0). Usefrom datarobot.insights import ShapMatrix, ...withentity_id=model_id— not legacydatarobot.models.ShapMatrix(project_id/dataset_id).ShapMatrix,ShapImpact,ShapPreview, andShapDistributionsare the canonical SHAP API. The olderdr.PredictionExplanations(XEMP-based) remains available but is the secondary path.
Quick Start
| Goal | API to use | Prerequisites |
|---|---|---|
| SHAP values for all features, all rows | ShapMatrix.create(entity_id=model_id) |
None - universal SHAP |
| Per-row top-feature explanations | ShapPreview.create(entity_id=model_id) |
None |
| Aggregated feature importance via SHAP | ShapImpact.create(entity_id=model_id) |
None |
| SHAP value distributions across features | ShapDistributions.create(entity_id=model_id) |
None |
| SHAP for a filtered segment | dr.DataSlice.create(...) + ShapMatrix.create(..., data_slice_id=...) |
Data slice definition |
| XEMP-based prediction explanations | dr.PredictionExplanations.create(...) |
Feature Impact; PE initialization; dataset uploaded |
| Anomaly explanations (time series) | AnomalyAssessmentRecord.compute(project_id, model_id, ...) |
Anomaly model |
| ROC / lift / confusion (insights) | RocCurve.create(...) / LiftChart.create(...) / ConfusionMatrix.create(...) |
Validation data |
| ROC / lift / confusion (Model helpers) | model.get_roc_curve() / model.get_lift_chart() / model.get_confusion_chart() |
Validation data |
Universal SHAP is the preferred path - no dataset pre-upload or Feature Impact step required.
When to use this skill
Use this skill when you need to explain leaderboard model behavior, compute SHAP insights, use XEMP prediction explanations, analyze anomaly explanations, or retrieve model diagnostics.
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.
- 11d ago First seen · 271 lines · 63 tokens per session scan A e7e2eabfba2e
datarobot-model-explainability is a skill published in the GitHub repository datarobot-oss/datarobot-agent-skills (25 stars, last pushed today), licensed Apache-2.0. It adds 63 tokens to every session and 2,613 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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