ai-test-orchestration

AI-powered test orchestration skill covering intelligent test selection, risk-based test prioritization, flaky test management, test impact analysis, parallel execution optimization, and predictive test failure detection using machine learning.

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name	AI Test Orchestration
description	AI-powered test orchestration skill covering intelligent test selection, risk-based test prioritization, flaky test management, test impact analysis, parallel execution optimization, and predictive test failure detection using machine learning.
version	1.0.0
author	thetestingacademy
license	MIT
tags	["ai-testing","test-orchestration","test-prioritization","risk-based-testing","test-selection","parallel-testing","ml-testing"]
testingTypes	["integration","e2e","unit"]
frameworks	["playwright","jest","vitest","pytest"]
languages	["typescript","javascript","python"]
domains	["web","api","backend","devops"]
agents	["claude-code","cursor","github-copilot","windsurf","codex","aider","continue","cline","zed","bolt","gemini-cli","amp"]

AI Test Orchestration Skill

You are an expert software engineer specializing in AI-powered test orchestration and intelligent test management. When the user asks you to implement, optimize, or debug test selection, prioritization, or parallel execution strategies, follow these detailed instructions.

Core Principles

Test the changed code first -- Prioritize tests that cover recently modified files and functions.
Learn from history -- Use historical pass/fail data to predict which tests are likely to fail.
Quarantine, don't ignore -- Flaky tests should be isolated and tracked, not deleted or skipped.
Optimize for feedback speed -- Run the most likely-to-fail tests first so developers get fast signals.
Distribute intelligently -- Split test suites across parallel workers based on historical duration, not file count.
Measure and iterate -- Track metrics like time-to-first-failure, false positive rate, and test suite efficiency.
Fail fast, verify thoroughly -- Fast feedback on PR checks, comprehensive verification on merge.

Project Structure

project/
  src/
    orchestrator/
      test-selector.ts
      risk-scorer.ts
      impact-analyzer.ts
      parallel-splitter.ts
      flaky-detector.ts
      prediction-model.ts
    data/
      test-history.ts
      git-analysis.ts
      coverage-map.ts
    reporters/
      orchestration-report.ts
      metrics-collector.ts
    config/
      orchestration.config.ts
  scripts/
    collect-test-data.ts
    train-model.py
    analyze-flakiness.ts
  tests/
    orchestrator/
      test-selector.test.ts
      risk-scorer.test.ts
      impact-analyzer.test.ts

Intelligent Test Selection Based on Code Changes

// src/orchestrator/test-selector.ts
import { execSync } from 'child_process';
import { CoverageMap } from '../data/coverage-map';

interface TestSelection {
  mustRun: string[];      // Tests directly covering changed code
  shouldRun: string[];    
  : [];      
  : ;     
}

  {
  : ;
  : ;
  : ;
  : [];
}

   {
   : ;

  () {
    . = coverageMap;
  }

   (:  = ): <> {
     changedFiles = .(baseBranch);
     allTests = ..();
     mustRun =  <>();
     shouldRun =  <>();

     ( file  changedFiles) {
      
       directTests = ..(file.);
      directTests.( mustRun.(t));

      
       (file.. > ) {
         ( fn  file.) {
           fnTests = ..(file., fn);
          fnTests.( mustRun.(t));
        }
      }

      
       dependents = ..(file.);
       ( dep  dependents) {
         depTests = ..(dep);
        depTests.( {
           (!mustRun.(t)) shouldRun.(t);
        });
      }
    }

     canSkip = allTests.(
       !mustRun.(t) && !shouldRun.(t)
    );

     confidence = ..()
      ? 
      : ; 

     {
      : [...mustRun],
      : [...shouldRun],
      canSkip,
      confidence,
    };
  }

   (: ): [] {
     diffOutput = (
      ,
      { :  }
    ).();

     (!diffOutput)  [];

     diffOutput.().( {
       stat = (
        ,
        { :  }
      ).();

       [additions, deletions] = stat.().();

      
       diffContent = (
        ,
        { :  }
      );
       changedFunctions = .(diffContent);

       { : filePath, additions, deletions, changedFunctions };
    });
  }

   (: ): [] {
     functionPattern = ;
     : [] = [];
     :  | ;

     ((match = functionPattern.(diffContent)) !== ) {
      functions.(match[]);
    }

     [... (functions)];
  }
}

// src/orchestrator/risk-scorer.ts
interface TestRiskScore {
  testId: string;
  score: number;          // 0-100, higher = run first
  factors: RiskFactor[];
}

interface RiskFactor {
  name: string;
  weight: number;
  value: number;
  contribution: number;
}

interface TestHistory {
  testId: string;
  recentResults: ('pass' | 'fail' | 'skip')[];
  averageDuration: number;
  lastFailedAt: Date | null;
  failureRate: number;     // 0-1
  flakinessScore: number;  // 0-1
}

export class RiskScorer {
  private weights = {
    recentFailureRate: 30,
    codeChangeProximity: 25,
    historicalFlakiness: 15,
    timeSinceLastRun: 10,
    testAge: 5,
    complexity: 10,
    criticalPath: 5,
  };

  scoreTests(
    tests: string[],
    history: Map<string, TestHistory>,
    changedFiles: string[],
    coverageMap: Map<string, string[]>
  ): TestRiskScore[] {
    return tests
      .map((testId) => this.scoreTest(testId, history, changedFiles, coverageMap))
      .sort((a, b) => b.score - a.score);
  }

  private scoreTest(
    testId: string,
    history: Map<string, TestHistory>,
    changedFiles: string[],
    coverageMap: Map<string, string[]>
  ): TestRiskScore {
    const testHistory = history.get(testId);
    const factors: RiskFactor[] = [];

    // Factor 1: Recent failure rate
    const failureRate = testHistory?.failureRate ?? 0;
    factors.push({
      name: 'recentFailureRate',
      weight: this.weights.recentFailureRate,
      value: failureRate,
      contribution: failureRate * this.weights.recentFailureRate,
    });

    // Factor 2: Code change proximity
    const coveredFiles = coverageMap.get(testId) || [];
    const overlapCount = coveredFiles.filter((f) => changedFiles.includes(f)).length;
    const proximity = coveredFiles.length > 0 ? overlapCount / coveredFiles.length : 0;
    factors.push({
      name: 'codeChangeProximity',
      weight: this.weights.codeChangeProximity,
      value: proximity,
      contribution: proximity * this.weights.codeChangeProximity,
    });

    // Factor 3: Historical flakiness
    const flakiness = testHistory?.flakinessScore ?? 0;
    factors.push({
      name: 'historicalFlakiness',
      weight: this.weights.historicalFlakiness,
      value: flakiness,
      contribution: flakiness * this.weights.historicalFlakiness,
    });

    // Factor 4: Time since last run (normalized to 0-1)
    const daysSinceRun = testHistory?.lastFailedAt
      ? (Date.now() - testHistory.lastFailedAt.getTime()) / (1000 * 60 * 60 * 24)
      : 30;
    const timeFactor = Math.min(daysSinceRun / 30, 1);
    factors.push({
      name: 'timeSinceLastRun',
      weight: this.weights.timeSinceLastRun,
      value: timeFactor,
      contribution: timeFactor * this.weights.timeSinceLastRun,
    });

    const score = factors.reduce((sum, f) => sum + f.contribution, 0);

    return { testId, score: Math.min(score, 100), factors };
  }
}

// src/orchestrator/flaky-detector.ts
interface FlakyTestReport {
  testId: string;
  flakinessScore: number;
  recentRuns: TestRun[];
  pattern: FlakyPattern;
  recommendation: 'quarantine' | 'retry' | 'investigate' | 'stable';
}

type FlakyPattern =
  | 'timing-dependent'
  | 'order-dependent'
  | 'resource-contention'
  | 'network-dependent'
  | 'random-data'
  | 'unknown';

interface TestRun {
  runId: string;
  result: 'pass' | 'fail';
  duration: number;
  timestamp: Date;
  errorMessage?: string;
  retryCount: number;
}

export class FlakyDetector {
  private readonly FLAKY_THRESHOLD = 0.1;  // 10% failure rate
  private readonly QUARANTINE_THRESHOLD = 0.3;  // 30% failure rate
  private readonly WINDOW_SIZE = 50;  // Last 50 runs

  analyze(testId: string, runs: TestRun[]): FlakyTestReport {
    const recentRuns = runs.slice(-this.WINDOW_SIZE);
    const failCount = recentRuns.filter((r) => r.result === 'fail').length;
    const flakinessScore = failCount / recentRuns.length;
    const pattern = this.detectPattern(recentRuns);

    let recommendation: FlakyTestReport['recommendation'];
    if (flakinessScore >= this.QUARANTINE_THRESHOLD) {
      recommendation = 'quarantine';
    } else if (flakinessScore >= this.FLAKY_THRESHOLD) {
      recommendation = 'investigate';
    } else if (flakinessScore > 0) {
      recommendation = 'retry';
    } else {
      recommendation = 'stable';
    }

    return { testId, flakinessScore, recentRuns, pattern, recommendation };
  }

  private detectPattern(runs: TestRun[]): FlakyPattern {
    const failures = runs.filter((r) => r.result === 'fail');
    if (failures.length === 0) return 'unknown';

    // Check for timing patterns
    const failDurations = failures.map((r) => r.duration);
    const avgFailDuration = failDurations.reduce((a, b) => a + b, 0) / failDurations.length;
    const passDurations = runs.filter((r) => r.result === 'pass').map((r) => r.duration);
    const avgPassDuration = passDurations.reduce((a, b) => a + b, 0) / passDurations.length;

    if (avgFailDuration > avgPassDuration * 3) {
      return 'timing-dependent';
    }

    // Check for network-related error messages
    const networkErrors = failures.filter((r) =>
      r.errorMessage?.match(/ECONNREFUSED|ETIMEDOUT|fetch failed|network/i)
    );
    if (networkErrors.length > failures.length * 0.5) {
      return 'network-dependent';
    }

    // Check for order-dependent patterns (failures cluster together)
    const failIndices = runs
      .map((r, i) => (r.result === 'fail' ? i : -1))
      .filter((i) => i >= 0);
    const clustered = failIndices.some(
      (idx, i) => i > 0 && idx - failIndices[i - 1] === 1
    );
    if (clustered) {
      return 'order-dependent';
    }

    return 'unknown';
  }

  generateQuarantineConfig(reports: FlakyTestReport[]): string {
    const quarantined = reports
      .filter((r) => r.recommendation === 'quarantine')
      .map((r) => r.testId);

    return JSON.stringify(
      {
        quarantinedTests: quarantined,
        retryConfig: {
          maxRetries: 3,
          retryDelay: 1000,
          testsToRetry: reports
            .filter((r) => r.recommendation === 'retry')
            .map((r) => r.testId),
        },
        updatedAt: new Date().toISOString(),
      },
      null,
      2
    );
  }
}

// src/orchestrator/retry-handler.ts
import { FlakyDetector, FlakyTestReport } from './flaky-detector';

interface RetryConfig {
  maxRetries: number;
  backoffMs: number;
  backoffMultiplier: number;
  retryablePatterns: RegExp[];
}

const DEFAULT_RETRY_CONFIG: RetryConfig = {
  maxRetries: 3,
  backoffMs: 500,
  backoffMultiplier: 2,
  retryablePatterns: [
    /ECONNREFUSED/,
    /ETIMEDOUT/,
    /net::ERR_CONNECTION_REFUSED/,
    /Target closed/,
    /Navigation timeout/,
    /waiting for selector/i,
  ],
};

export async function withRetry<T>(
  testFn: () => Promise<T>,
  config: RetryConfig = DEFAULT_RETRY_CONFIG
): Promise<{ result: T; attempts: number }> {
  let lastError: Error | undefined;
  let delay = config.backoffMs;

  for (let attempt = 1; attempt <= config.maxRetries + 1; attempt++) {
    try {
      const result = await testFn();
      return { result, attempts: attempt };
    } catch (error) {
      lastError = error as Error;

      if (attempt > config.maxRetries) break;

      const isRetryable = config.retryablePatterns.some((pattern) =>
        pattern.test(lastError!.message)
      );

      if (!isRetryable) break;

      console.warn(
        `Test attempt ${attempt} failed (retryable): ${lastError.message}. ` +
        `Retrying in ${delay}ms...`
      );

      await new Promise((resolve) => setTimeout(resolve, delay));
      delay *= config.backoffMultiplier;
    }
  }

  throw lastError;
}

// Playwright integration
// playwright.config.ts
import { defineConfig } from '@playwright/test';

export default defineConfig({
  retries: 2,
  use: {
    trace: 'on-first-retry',
    video: 'on-first-retry',
  },
  projects: [
    {
      name: 'stable',
      testMatch: /.*\.spec\.ts/,
      retries: 0,
    },
    {
      name: 'flaky-quarantine',
      testMatch: /.*\.flaky\.spec\.ts/,
      retries: 3,
      use: {
        trace: 'on',
      },
    },
  ],
});

// src/orchestrator/parallel-splitter.ts
interface TestBucket {
  workerId: number;
  tests: string[];
  estimatedDuration: number;
}

interface TestMetadata {
  testId: string;
  averageDuration: number;
  dependencies: string[];  // Tests that must run before this one
  resourceRequirements: string[];  // e.g., ['database', 'redis']
}

export class ParallelSplitter {
  /**
   * Split tests into balanced buckets for parallel execution.
   * Uses a greedy algorithm: assign each test to the bucket with
   * the smallest current total duration (like a min-heap).
   */
  splitByDuration(
    tests: TestMetadata[],
    workerCount: number
  ): TestBucket[] {
    // Sort tests by duration descending (longest first for better balancing)
    const sorted = [...tests].sort(
      (a, b) => b.averageDuration - a.averageDuration
    );

    const buckets: TestBucket[] = Array.from({ length: workerCount }, (_, i) => ({
      workerId: i,
      tests: [],
      estimatedDuration: 0,
    }));

    for (const test of sorted) {
      // Find the bucket with the smallest total duration
      const minBucket = buckets.reduce((min, bucket) =>
        bucket.estimatedDuration < min.estimatedDuration ? bucket : min
      );

      minBucket.tests.push(test.testId);
      minBucket.estimatedDuration += test.averageDuration;
    }

    return buckets;
  }

  /**
   * Split with resource constraints: tests needing the same
   * exclusive resource cannot run in parallel.
   */
  splitWithConstraints(
    tests: TestMetadata[],
    workerCount: number
  ): TestBucket[] {
    const resourceGroups = new Map<string, TestMetadata[]>();
    const noResourceTests: TestMetadata[] = [];

    for (const test of tests) {
      if (test.resourceRequirements.length === 0) {
        noResourceTests.push(test);
      } else {
        const key = test.resourceRequirements.sort().join(',');
        const group = resourceGroups.get(key) || [];
        group.push(test);
        resourceGroups.set(key, group);
      }
    }

    const buckets = this.splitByDuration(noResourceTests, workerCount);

    // Assign resource-constrained tests to the same worker
    for (const [, group] of resourceGroups) {
      const minBucket = buckets.reduce((min, bucket) =>
        bucket.estimatedDuration < min.estimatedDuration ? bucket : min
      );

      for (const test of group) {
        minBucket.tests.push(test.testId);
        minBucket.estimatedDuration += test.averageDuration;
      }
    }

    return buckets;
  }
}

# .github/workflows/parallel-tests.yml
name: Parallel Test Execution
on: [push]

jobs:
  plan:
    name: Plan Test Distribution
    runs-on: ubuntu-latest
    outputs:
      matrix: ${{ steps.split.outputs.matrix }}
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: npm ci
      - id: split
        run: |
          MATRIX=$(node scripts/plan-parallel.js --workers=4)
          echo "matrix=$MATRIX" >> $GITHUB_OUTPUT

  test:
    name: Test Shard ${{ matrix.shard }}
    needs: plan
    runs-on: ubuntu-latest
    strategy:
      matrix: ${{ fromJson(needs.plan.outputs.matrix) }}
      fail-fast: false
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with:
          node-version: 20
      - run: npm ci
      - run: |
          npx vitest run --shard=${{ matrix.shard }}/${{ matrix.total }}
      - uses: actions/upload-artifact@v4
        if: always()
        with:
          name: test-results-${{ matrix.shard }}
          path: test-results/

# scripts/train-model.py
"""
Train a simple model to predict which tests are likely to fail
based on code change features and historical test data.
"""

import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score, recall_score, f1_score
import json
import pickle

def load_training_data(history_path: str) -> pd.DataFrame:
    """Load and prepare training data from test history."""
    with open(history_path) as f:
        records = json.load(f)

    rows = []
    for record in records:
        rows.append({
            'test_id': record['testId'],
            'files_changed': record['filesChanged'],
            'lines_added': record['linesAdded'],
            'lines_deleted': record['linesDeleted'],
            'recent_failure_rate': record['recentFailureRate'],
            'avg_duration': record['avgDuration'],
            'days_since_last_change': record['daysSinceLastChange'],
            'dependency_depth': record['dependencyDepth'],
            'code_complexity': record['codeComplexity'],
            'test_age_days': record['testAgeDays'],
            'failed': 1 if record['result'] == 'fail' else 0,
        })

    return pd.DataFrame(rows)

def train_prediction_model(data: pd.DataFrame):
    """Train a gradient boosting model for test failure prediction."""
    features = [
        'files_changed', 'lines_added', 'lines_deleted',
        'recent_failure_rate', 'avg_duration', 'days_since_last_change',
        'dependency_depth', 'code_complexity', 'test_age_days',
    ]

    X = data[features]
    y = data['failed']

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42, stratify=y
    )

    model = GradientBoostingClassifier(
        n_estimators=100,
        max_depth=5,
        learning_rate=0.1,
        min_samples_split=10,
        random_state=42,
    )

    model.fit(X_train, y_train)

    # Evaluate
    y_pred = model.predict(X_test)
    print(f"Precision: {precision_score(y_test, y_pred):.3f}")
    print(f"Recall:    {recall_score(y_test, y_pred):.3f}")
    print(f"F1 Score:  {f1_score(y_test, y_pred):.3f}")

    # Feature importance
    importance = dict(zip(features, model.feature_importances_))
    print("\nFeature Importance:")
    for feat, imp in sorted(importance.items(), key=lambda x: -x[1]):
        print(f"  {feat}: {imp:.3f}")

    # Save model
    with open('models/test-predictor.pkl', 'wb') as f:
        pickle.dump(model, f)

    return model

if __name__ == '__main__':
    data = load_training_data('data/test-history.json')
    print(f"Training on {len(data)} records ({data['failed'].sum()} failures)")
    train_prediction_model(data)

// src/orchestrator/prediction-model.ts
import { execSync } from 'child_process';

interface PredictionResult {
  testId: string;
  failureProbability: number;
  confidence: number;
  topFactors: { name: string; contribution: number }[];
}

export class TestFailurePredictor {
  private modelPath: string;

  constructor(modelPath: string = 'models/test-predictor.pkl') {
    this.modelPath = modelPath;
  }

  predict(features: Map<string, Record<string, number>>): PredictionResult[] {
    // Call Python model via subprocess
    const input = JSON.stringify(Object.fromEntries(features));
    const output = execSync(
      `python3 scripts/predict.py --model=${this.modelPath} --input='${input}'`,
      { encoding: 'utf-8' }
    );

    return JSON.parse(output);
  }

  /**
   * Simple heuristic fallback when ML model is not available.
   * Uses weighted scoring based on observable features.
   */
  predictHeuristic(
    testId: string,
    recentFailureRate: number,
    filesChanged: number,
    linesChanged: number,
    daysSinceLastRun: number
  ): PredictionResult {
    const failureProbability = Math.min(
      1.0,
      recentFailureRate * 0.4 +
      Math.min(filesChanged / 20, 1) * 0.25 +
      Math.min(linesChanged / 500, 1) * 0.2 +
      Math.min(daysSinceLastRun / 30, 1) * 0.15
    );

    return {
      testId,
      failureProbability,
      confidence: 0.6, // Lower confidence for heuristic
      topFactors: [
        { name: 'recentFailureRate', contribution: recentFailureRate * 0.4 },
        { name: 'filesChanged', contribution: Math.min(filesChanged / 20, 1) * 0.25 },
        { name: 'linesChanged', contribution: Math.min(linesChanged / 500, 1) * 0.2 },
      ],
    };
  }
}

// src/data/git-analysis.ts
import { execSync } from 'child_process';

interface ImpactAnalysis {
  changedFiles: string[];
  impactedModules: string[];
  riskLevel: 'low' | 'medium' | 'high' | 'critical';
  recommendedTestScope: 'unit' | 'integration' | 'e2e' | 'full';
}

export class GitAnalyzer {
  analyzeImpact(baseBranch: string = 'main'): ImpactAnalysis {
    const diff = execSync(
      `git diff --stat ${baseBranch}...HEAD`,
      { encoding: 'utf-8' }
    );

    const changedFiles = execSync(
      `git diff --name-only ${baseBranch}...HEAD`,
      { encoding: 'utf-8' }
    ).trim().split('\n').filter(Boolean);

    const impactedModules = this.identifyModules(changedFiles);
    const riskLevel = this.assessRisk(changedFiles);
    const recommendedTestScope = this.recommendScope(changedFiles, riskLevel);

    return { changedFiles, impactedModules, riskLevel, recommendedTestScope };
  }

  private identifyModules(files: string[]): string[] {
    const modules = new Set<string>();
    for (const file of files) {
      const parts = file.split('/');
      if (parts.length >= 2) {
        modules.add(`${parts[0]}/${parts[1]}`);
      }
    }
    return [...modules];
  }

  private assessRisk(files: string[]): ImpactAnalysis['riskLevel'] {
    const criticalPatterns = [
      /package\.json$/,
      /\.env/,
      /migration/,
      /schema\./,
      /auth\//,
      /middleware/,
    ];

    const highRiskPatterns = [
      /\.config\./,
      /api\//,
      /database\//,
    ];

    if (files.some((f) => criticalPatterns.some((p) => p.test(f)))) {
      return 'critical';
    }
    if (files.some((f) => highRiskPatterns.some((p) => p.test(f)))) {
      return 'high';
    }
    if (files.length > 20) {
      return 'high';
    }
    if (files.length > 5) {
      return 'medium';
    }
    return 'low';
  }

  private recommendScope(
    files: string[],
    riskLevel: ImpactAnalysis['riskLevel']
  ): ImpactAnalysis['recommendedTestScope'] {
    if (riskLevel === 'critical') return 'full';
    if (riskLevel === 'high') return 'e2e';

    const hasApiChanges = files.some((f) => /api\//.test(f));
    if (hasApiChanges) return 'integration';

    return 'unit';
  }
}

// src/reporters/metrics-collector.ts
interface OrchestrationMetrics {
  totalTests: number;
  testsSelected: number;
  testsSkipped: number;
  selectionAccuracy: number;    // Did we catch all failures?
  timeToFirstFailure: number;   // ms
  totalDuration: number;        // ms
  estimatedTimeSaved: number;   // ms (vs running all tests)
  falsePositiveRate: number;    // Skipped tests that would have failed
  parallelEfficiency: number;   // Actual speedup vs theoretical
}

export class MetricsCollector {
  private startTime: number = 0;
  private firstFailureTime: number | null = null;

  start(): void {
    this.startTime = Date.now();
  }

  recordFailure(): void {
    if (!this.firstFailureTime) {
      this.firstFailureTime = Date.now();
    }
  }

  generateReport(
    totalTests: number,
    selectedTests: number,
    fullSuiteDuration: number
  ): OrchestrationMetrics {
    const totalDuration = Date.now() - this.startTime;

    return {
      totalTests,
      testsSelected: selectedTests,
      testsSkipped: totalTests - selectedTests,
      selectionAccuracy: 0, // Filled after verification run
      timeToFirstFailure: this.firstFailureTime
        ? this.firstFailureTime - this.startTime
        : totalDuration,
      totalDuration,
      estimatedTimeSaved: fullSuiteDuration - totalDuration,
      falsePositiveRate: 0, // Filled after verification run
      parallelEfficiency: 0, // Filled after parallel run
    };
  }
}

# Analyze impact of current changes
npx tsx src/data/git-analysis.ts --base=main

# Select tests based on code changes
npx tsx src/orchestrator/test-selector.ts --base=main --output=selected-tests.json

# Score and prioritize tests
npx tsx src/orchestrator/risk-scorer.ts --input=selected-tests.json --output=prioritized.json

# Analyze flaky tests from history
npx tsx scripts/analyze-flakiness.ts --history=data/test-history.json

# Plan parallel execution
npx tsx src/orchestrator/parallel-splitter.ts --workers=4 --input=prioritized.json

# Train prediction model (Python)
python3 scripts/train-model.py --data=data/test-history.json

# Run selected tests only
npx vitest run $(cat selected-tests.json | jq -r '.mustRun[]' | tr '\n' ' ')

# Run with orchestration report
npx tsx src/reporters/orchestration-report.ts --run-id=$(date +%s)