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 RelationalAI/rai-agent-skills --skill rai-predictive-traininggit clone --depth 1 https://github.com/RelationalAI/rai-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/relationalai/rai-agent-skills/rai-predictive-training)<a href="https://agentmods.dev/skills/relationalai/rai-agent-skills/rai-predictive-training"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-predictive-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/relationalai/rai-agent-skills/rai-predictive-training"><img src="https://agentmods.dev/badge/skills/relationalai/rai-agent-skills/rai-predictive-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00065 | $0.07326 |
| Opus 5 | $0.00032 | $0.03663 |
| Sonnet 5 | $0.00013 | $0.01465 |
| Haiku 4.5 | $0.00006 | $0.00733 |
Grade A, and why
rai-predictive-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 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 — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Predictive Training
Early access. The RAI predictive reasoner (GNN) is in early access — APIs, engine requirements, and behavior may change. Confirm the latest surface with the RelationalAI team before production use.
Summary
What: Training, evaluation, and model management workflow for GNN pipelines.
When to use:
- Configuring the GNN estimator and hyperparameters
- Training models with
fit() - Generating predictions on test data
- Evaluating and debugging results
- Registering or loading saved models
When NOT to use:
- Defining concepts, loading data, building graphs -- see
rai-predictive-modeling
Overview: 4 steps: configure GNN -> train -> predict/evaluate -> optional: register/load.
By user intent — sections to focus on:
- Train + read validation metric → Quick Reference + GNN Constructor +
gnn.fit() -
- predict + downstream rule / optimization → also Predictions + Using Predictions Downstream
-
- register + reload across sessions → also Model Management
Quick Reference
Node Classification (minimal)
gnn = GNN(
exp_database="DB", exp_schema="EXPERIMENTS",
graph=gnn_graph, property_transformer=pt,
train=Train, validation=Val,
task_type="binary_classification", eval_metric="roc_auc",
has_time_column=True, device="cuda", seed=42,
)
gnn.fit()
User.predictions = gnn.predictions(domain=Test)
Default Metrics
| Task Type | Suggested Metric |
|---|---|
| binary_classification | roc_auc |
| multiclass_classification | accuracy |
| multilabel_classification | multilabel_auprc_macro |
| regression | rmse |
| link_prediction | link_prediction_precision@5 |
| repeated_link_prediction | link_prediction_precision@5 |
Prediction Attributes
| Task Type | Attributes |
|---|---|
binary_classification, multiclass_classification, multilabel_classification |
.probs, .predicted_labels |
regression |
.predicted_value |
link_prediction, repeated_link_prediction |
.rank, .scores, .predicted_<target> |
What ships with it
9 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.
- examples/register_and_load.py 1.6 KB runs code
- examples/train_link_prediction.py 1.4 KB runs code
- examples/train_node_classification.py 1.4 KB runs code
- examples/train_regression.py 1.5 KB runs code
- references/evaluation-debugging.md 8.8 KB
- references/hyperparameters.md 8.5 KB
- references/known-limitations.md 7.1 KB
- references/prediction-attributes.md 2.0 KB
- references/task-types-and-metrics.md 3.3 KB
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 · 500 lines · 65 tokens per session scan A cd57962cefca
rai-predictive-training is a skill published in the GitHub repository RelationalAI/rai-agent-skills (4 stars, last pushed yesterday), licensed Apache-2.0. It adds 65 tokens to every session and 7,326 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 2026-08-31.
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