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 serac-labs/serac --skill predictive-intelligencegit clone --depth 1 https://github.com/serac-labs/seracWrote 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/serac-labs/serac/predictive-intelligence)<a href="https://agentmods.dev/skills/serac-labs/serac/predictive-intelligence"><img src="https://agentmods.dev/badge/skills/serac-labs/serac/predictive-intelligence/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/serac-labs/serac/predictive-intelligence"><img src="https://agentmods.dev/badge/skills/serac-labs/serac/predictive-intelligence.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 | $0.02516 |
| Opus 5.5 | $0.00019 | $0.01006 |
| Sonnet 5.5 | $0.00010 | $0.00503 |
| Haiku 4.5 | $0.00005 | $0.00252 |
Grade A, and why
predictive-intelligence 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 1mo 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 — 417 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Predictive Intelligence for ServiceNow
Predictive Intelligence uses machine learning to automate categorization, routing, and recommendations.
PI Capabilities
| Capability | Use Case |
|---|---|
| Classification | Auto-categorize incidents, cases |
| Similarity | Find similar records |
| Clustering | Group related items |
| Regression | Predict numeric values |
| Recommendation | Suggest next actions |
Key Tables
| Table | Purpose |
|---|---|
ml_solution |
ML solution definitions |
ml_solution_definition |
Solution configuration |
ml_capability_definition |
Capability settings |
ml_model |
Trained models |
ml_prediction_result |
Prediction results |
Classification (ES5)
Configure Classification Solution
// Create classification solution (ES5 ONLY!)
// Note: Usually done via UI, shown for understanding
var solution = new GlideRecord("ml_solution")
solution.initialize()
solution.setValue("name", "Incident Category Classifier")
solution.setValue("label", "Incident Category Classifier")
solution.setValue("table", "incident")
solution.setValue("active", true)
// Capability type
solution.setValue("capability", "classification")
// Target field to predict
solution.setValue("target_field", "category")
// Input fields for training
solution.setValue("input_fields", "short_description,description")
solution.insert()
Get Classification Prediction
// Get classification prediction for record (ES5 ONLY!)
function getClassificationPrediction(tableName, recordSysId, solutionName) {
var predictor = new sn_ml.ClassificationPredictor(solutionName)
var gr = new GlideRecord(tableName)
if (!gr.get(recordSysId)) {
return null
}
try {
var result = predictor.predict(gr)
return {
predicted_value: result.getPredictedValue(),
confidence: result.getConfidence(),
top_predictions: result.getTopPredictions(5),
}
} catch (e) {
gs.error("Prediction failed: " + e.message)
return null
}
}
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.
- 1mo ago First seen · 417 lines · 48 tokens per session scan A 6a7c0dd5a16a
predictive-intelligence is a skill published in the GitHub repository serac-labs/serac (78 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 2,516 once invoked, about $0.0002 per session on Opus 5.5. 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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