predictive-intelligence

A ServiceNow machine-learning toolkit for sorting records, finding related items, grouping data, predicting numbers, and suggesting actions. ServiceNow is a platform for managing IT and business work; machine learning lets software learn patterns from existing records.

In plain words
What is it for?
Use it to configure, train, and retrain classification, similarity, clustering, regression, and recommendation models in ServiceNow.
Why use it?
It helps teams handle large numbers of incidents and other records consistently, while tracking prediction results and accuracy feedback.

Skill for Claude CodeCodex

Part of the skills plugin — 56 skills shipped together

Install

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.

agentmods
npx agentmods add skills/serac-labs/serac/predictive-intelligence
Any agent
npx skills add serac-labs/serac --skill predictive-intelligence
Clone the repo
git clone --depth 1 https://github.com/serac-labs/serac

Made for: Claude Code, Codex.

Or install skills, the plugin that ships this one along with the rest of its 56 skills.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,516 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.02516
Opus 5 $0.00024 $0.01258
Sonnet 5 $0.00010 $0.00503
Haiku 4.5 $0.00005 $0.00252

Measured 3d ago against content hash 6a7c0dd5a16a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d 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.

packages/skills/predictive-intelligence/SKILL.md · 417 lines

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
  }
}

Read the full file on GitHub · 417 lines

Changes

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.

  1. 3d ago First seen · 417 lines · 48 tokens per session scan A 6a7c0dd5a16a

Subscribe to this mod's changes

predictive-intelligence is a skill published in the GitHub repository serac-labs/serac (78 stars, last pushed 8d 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. 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.

Related

Other skills, from other repositories

opik-instrument

Add Opik tracing to an existing app and verify a real trace lands. Installs the Opik package, detects the language and LLM framework, adds the minimum tracing, runs a safe representative path, confirms a trace in Opik, and returns the trace link. Use for "instrument my code", "add opik tracing", "add observability"…

comet-ml/opik-mcp · 101 tokens

opik-evaluate

Build an LLM evaluation and run it against your app, returning an experiment with scores. Covers datasets, LLM judges, RAG evaluation, synthetic data, error analysis, and validating evaluators against human labels. Use when the user wants to measure or improve AI product quality, or asks about evals, judges, or…

comet-ml/opik-mcp · 73 tokens

opik

Reference for the Opik SDK — tracing, span types, framework integrations, threads, and the prompt library (Python, TypeScript, REST). Use for "what span types exist", "how do I flush", "trackopenai", "add OpikTracer", "version a prompt". To instrument a repo end to end, use the opik-instrument skill.

comet-ml/opik-mcp · 80 tokens

local-rag-search

Efficiently perform web searches using the mcp-local-rag server with semantic similarity ranking. Use this skill when you need to search the web for current information, research topics across multiple sources, or gather context from the internet without using external APIs. This skill teaches effective use of…

nkapila6/mcp-local-rag · 80 tokens

luma-vision

多模型视觉理解能力。通过 /skill luma-vision 激活时,执行 vision.js 脚本调用外部视觉模型分析图片。.

JochenYang/luma-mcp · 37 tokens

knowledge-base

Set up a knowledge base with search for an MCP project. Creates FAQ tool and ingestion script using the Waniwani KB API via @waniwani/sdk.

WaniWani-AI/sdk · 34 tokens