predictive-scaler

Analyze resource usage patterns and predict future scaling needs using trend analysis and forecasting methods for capacity planning and auto-scaling decisions.

Security Scan

The skill's files, instructions, and required resources are coherent with a local forecasting/predictive-scaling utility and do not request unrelated credentials, installs, or external access.

Purpose & Capability

The name/description (predictive scaling, capacity planning) align with the included SKILL.md and index.js implementation: moving averages, linear regression, exponential smoothing, multi-resource prediction, and recommendation logic. The code implements the documented APIs and outputs.

Instruction Scope

SKILL.md instructs loading the local module and calling prediction functions with historical data. The instructions and code operate entirely on input arrays and return recommendations; there are no steps that read unrelated files, environment variables, or send data to external endpoints.

Install Mechanism

There is no install spec and package.json has no dependencies. The skill is instruction-only with an included index.js; nothing is downloaded or executed from remote URLs and no archive extraction is present.

Credentials

The skill does not declare or use environment variables, credentials, or config paths. The code does not reference process.env or other secret sources, so requested access is minimal and proportionate.

Persistence & Privilege

Flags indicate normal invocation (not always: true). The skill does not modify agent/system configuration, does not persist credentials, and requires explicit invocation; no elevated persistence or privileges are requested.

Assessment

This skill appears to be a straightforward local forecasting module. Before installing or using it in production: (1) review and test predictions with your real workloads in a staging environment to tune thresholds and horizons, (2) validate input sanitization if you pass non-normalized values (the code clamps values to 0-1 but you should confirm behavior for your metrics), and (3) if you plan to integrate it into automation that performs scaling actions, ensure you add safeguards (rate limits, cooldowns, manual approvals) because recommendations are based on statistical heuristics and can be wrong during bursty/volatile periods. If you require external integrations or cloud credentials later, ensure those are provided only to modules that clearly need them.

Analyze resource usage patterns and predict future scaling needs.

When to Use

• Capacity planning and resource forecasting
• Auto-scaling decision support
• Predicting CPU, memory, or request load
• Analyzing bursty traffic patterns
• Generating scaling recommendations

Usage

const scaler = require('./skills/predictive-scaler');

// Basic prediction
const prediction = scaler.predict(cpuHistory, {
  horizon: 60,  // Predict 60 minutes ahead
  scaleUpThreshold: 0.8,
  scaleDownThreshold: 0.3
});

console.log(prediction.recommendation);
// { action: 'scale_up', reason: 'Predicted peak 0.85 exceeds threshold 0.8' }

API

predict(data, options)

Predict future resource usage and generate scaling recommendation.

const result = scaler.predict(usageData, {
  horizon: 60,              // Prediction horizon in minutes
  minDataPoints: 5,         // Minimum data points needed
  scaleUpThreshold: 0.8,    // Threshold to recommend scale-up
  scaleDownThreshold: 0.3,  // Threshold to recommend scale-down
  confidenceThreshold: 0.7, // Minimum confidence for recommendations
  smoothingFactor: 0.3,     // Exponential smoothing factor
  windowSize: 10            // Moving average window
});

predictMulti(resources, options)

Predict for multiple resources at once.

const result = scaler.predictMulti({
  cpu: cpuHistory,
  memory: memoryHistory,
  requests: requestHistory
});

console.log(result.combinedRecommendation);
// { action: 'scale_up', scaleUpCount: 1, scaleDownCount: 0 }

predictLinear(data, steps)

Predict using linear regression.

const { predictions, confidence } = scaler.predictLinear(data, 10);

predictExponential(data, steps, factor)

Predict using exponential smoothing.

const { predictions, confidence } = scaler.predictExponential(data, 10, 0.3);

detectTrend(data)

Detect trend direction in data.

const trend = scaler.detectTrend(data);
// 'increasing' | 'decreasing' | 'stable' | 'volatile'

detectBurstyPattern(data)

Detect bursty traffic patterns.

const bursty = scaler.detectBurstyPattern(data);
// { isBursty: true, burstFactor: 0.6, spikeRatio: 0.15 }

calculateCapacityNeeded(current, predicted, targetUtilization)

Calculate capacity needed to handle predicted load.

const capacity = scaler.calculateCapacityNeeded(10, 8.5, 0.7);
// { current: 10, needed: 13, change: 3, changePercent: 30 }

analyzeScalingHistory(events)

Analyze historical scaling events.

const analysis = scaler.analyzeScalingHistory(scalingEvents);
// { scaleUpFrequency: 0.3, averageInterval: 3600000 }

Output Structure

{
  predictions: [0.65, 0.68, 0.72, ...],  // Predicted values
  confidence: 0.85,                       // Prediction confidence
  trend: 'increasing',                    // Trend direction
  bursty: {
    isBursty: false,
    burstFactor: 0.2
  },
  recommendation: {
    action: 'scale_up',                   // 'scale_up' | 'scale_down' | 'maintain'
    reason: 'Predicted peak 0.85 exceeds threshold 0.8',
    current: 0.72,
    predicted: { average: 0.78, max: 0.85, min: 0.70 }
  },
  statistics: {
    mean: 0.65,
    stdDev: 0.1,
    min: 0.45,
    max: 0.82,
    dataPoints: 30
  }
}

Scaling Actions

Action	Description
scale_up	Resource predicted to exceed scale-up threshold
scale_down	Resource predicted below scale-down threshold
maintain	Resource within normal range
unknown	Insufficient data or error

Examples

Basic Scaling Prediction

const scaler = require('./skills/predictive-scaler');

// CPU usage history (0-1 normalized)
const cpuHistory = [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.72, 0.75, 0.78];

const prediction = scaler.predict(cpuHistory, {
  horizon: 30,
  scaleUpThreshold: 0.8
});

if (prediction.recommendation.action === 'scale_up') {
  console.log('Scale up recommended:', prediction.recommendation.reason);
}

Multi-Resource Prediction

const scaler = require('./skills/predictive-scaler');

const resources = {
  cpu: [0.5, 0.55, 0.6, 0.65, 0.7],
  memory: [0.3, 0.32, 0.35, 0.38, 0.4],
  requests: [100, 120, 150, 180, 200]
};

const result = scaler.predictMulti(resources, { horizon: 60 });

console.log('CPU:', result.resources.cpu.recommendation.action);
console.log('Memory:', result.resources.memory.recommendation.action);
console.log('Combined:', result.combinedRecommendation.action);

Trend Analysis

const scaler = require('./skills/predictive-scaler');

const usageData = [0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6];

const trend = scaler.detectTrend(usageData);
console.log('Trend:', trend); // 'increasing'

const bursty = scaler.detectBurstyPattern(usageData);
console.log('Bursty:', bursty.isBursty); // false

Capacity Planning

const scaler = require('./skills/predictive-scaler');

const prediction = scaler.predict(cpuHistory, { horizon: 60 });
const predictedLoad = prediction.predictions[prediction.predictions.length - 1];

const capacity = scaler.calculateCapacityNeeded(
  10,              // Current capacity (instances)
  predictedLoad,   // Predicted load
  0.7              // Target utilization
);

console.log(`Need ${capacity.needed} instances (${capacity.changePercent}% change)`);

Scaling History Analysis

const scaler = require('./skills/predictive-scaler');

const scalingEvents = [
  { action: 'scale_up', timestamp: Date.now() - 3600000 },
  { action: 'scale_down', timestamp: Date.now() - 1800000 },
  { action: 'scale_up', timestamp: Date.now() }
];

const analysis = scaler.analyzeScalingHistory(scalingEvents);
console.log('Scale-up frequency:', analysis.scaleUpFrequency);
console.log('Average interval:', analysis.averageInterval, 'ms');

Prediction Methods

Linear Regression

• Best for: Steady growth/decline patterns
• Output: Trend line with confidence (R²)
• Use when: Data shows clear linear trend

Exponential Smoothing

• Best for: Recent data more important than old
• Output: Smoothed predictions
• Use when: Recent trends are more relevant

Combined Prediction

• Default: Weighted average of both methods
• Weights: Based on each method's confidence
• More robust than single method

Best Practices

1. Minimum data points: Use at least 10-15 data points for reliable predictions
2. Normalize data: Input should be 0-1 range (CPU%, memory%, etc.)
3. Set appropriate thresholds: Scale-up at 80%, scale-down at 30% is typical
4. Consider bursty patterns: Lower confidence for highly variable data
5. Combine with cooldown: Don't scale too frequently based on predictions
6. Validate predictions: Compare predictions with actual values over time

Notes

• Predictions are statistical estimates, not guarantees
• Confidence decreases with prediction horizon
• Bursty patterns reduce prediction reliability
• Multiple resources can be analyzed together
• Historical scaling events inform future decisions