Optimize CI test pipelines through intelligent test splitting, parallelization, caching strategies, and selective test execution based on code changes.
npx skills add https://github.com/PramodDutta/qaskills --skill CI Pipeline Optimizer
Slow CI pipelines are one of the most common productivity killers in software teams. A test suite that takes 30 minutes to run means developers context-switch away from their work, batch multiple changes into fewer PRs to avoid waiting, and eventually start skipping CI altogether. This skill addresses CI pipeline performance through four complementary strategies: intelligent test splitting across parallel workers, selective test execution based on code changes, aggressive caching of dependencies and build artifacts, and pipeline architecture that minimizes total wall-clock time. The techniques apply to any CI system but use GitHub Actions as the primary example, with patterns that transfer to GitLab CI, CircleCI, Jenkins, and other platforms.
CPU time, billable minutes, and total test count are secondary metrics. The developer waits for wall-clock time. A pipeline that uses 60 CPU-minutes across 10 parallel workers in 6 minutes is vastly preferable to a pipeline that uses 20 CPU-minutes sequentially in 20 minutes. Optimize for the duration the developer experiences.
If a PR changes only documentation files, running the full test suite is waste. If a PR modifies only the frontend, running backend integration tests is waste. Selective test execution identifies the minimum set of tests needed to validate a specific change, with a safety net that defaults to running everything when the analysis is uncertain.
Dependencies, build artifacts, Docker layers, and browser binaries are the same across most CI runs. Downloading and building them from scratch on every run is unnecessary. Aggressive caching can eliminate minutes from every pipeline execution.
Naive test splitting distributes files evenly across workers. But if one file contains a 5-minute test and another contains a 5-second test, the distribution is severely unbalanced. Intelligent splitting uses historical duration data to distribute tests so that all workers finish at approximately the same time.
Run the fastest tests first. If unit tests catch a bug in 30 seconds, there is no reason to wait 10 minutes for e2e tests to also catch it. Structure the pipeline so the cheapest checks (lint, type-check) run first to provide the fastest possible feedback, but still collect complete results from all shards for thorough reporting.
ci-config/
scripts/
split-tests.ts
detect-changes.ts
cache-manager.ts
timing-collector.ts
pipeline-analyzer.ts
config/
test-groups.json
change-map.json
cache-config.json
.github/
workflows/
ci-optimized.yml
ci-selective.yml
cache-warmup.yml
package.json
tsconfig.json
The scripts/ directory contains automation tools for test splitting, change detection, cache management, and pipeline analysis. The config/ directory holds the mapping from file changes to test groups, cache key definitions, and test group specifications.
Split tests across parallel workers based on historical execution times so that each worker runs for approximately the same duration:
// scripts/split-tests.ts
import { readFileSync, writeFileSync, existsSync } from 'fs';
import { execSync } from 'child_process';
interface TestTiming {
file: string;
duration: number;
lastRun: string;
}
interface SplitResult {
shardIndex: number;
files: string[];
estimatedDuration: number;
}
export function splitTestsByDuration(
testFiles: string[],
shardCount: number,
timingsFile: string = 'test-timings.json'
): SplitResult[] {
// Load historical timings
const timings = loadTimings(timingsFile);
// Create a list of files with their estimated duration
const fileTimings: Array<{ file: string; duration: number }> = testFiles.map((file) => ({
file,
duration: timings.get(file) || estimateDefaultDuration(file),
}));
// Sort by duration descending (greedy algorithm: assign largest jobs first)
fileTimings.sort((a, b) => b.duration - a.duration);
// Initialize shards
const shards: SplitResult[] = Array.from({ length: shardCount }, (_, i) => ({
shardIndex: i,
files: [],
estimatedDuration: 0,
}));
// Greedy assignment: always assign to the shard with the least total duration
for (const fileTiming of fileTimings) {
const lightest = shards.reduce((min, shard) =>
shard.estimatedDuration < min.estimatedDuration ? shard : min
);
lightest.files.push(fileTiming.file);
lightest.estimatedDuration += fileTiming.duration;
}
return shards;
}
function loadTimings(timingsFile: string): Map<string, number> {
const timings = new Map<string, number>();
if (!existsSync(timingsFile)) {
return timings;
}
try {
const data: TestTiming[] = JSON.parse(readFileSync(timingsFile, 'utf-8'));
for (const entry of data) {
timings.set(entry.file, entry.duration);
}
} catch {
console.warn(`Failed to parse timings file: ${timingsFile}`);
}
return timings;
}
function estimateDefaultDuration(file: string): number {
// Heuristic estimates based on test type when no historical data exists
if (file.includes('.e2e.') || file.includes('e2e/')) return 30000;
if (file.includes('.integration.') || file.includes('integration/')) return 10000;
if (file.includes('.spec.')) return 5000;
return 3000; // Default estimate for unit tests
}
// CLI entry point for use in GitHub Actions
function main(): void {
const shardIndex = parseInt(process.env.SHARD_INDEX || '0', 10);
const shardCount = parseInt(process.env.SHARD_COUNT || '1', 10);
// Discover all test files
const testFilesOutput = execSync(
'find . -name "*.test.ts" -o -name "*.spec.ts" | grep -v node_modules',
{ encoding: 'utf-8' }
);
const testFiles = testFilesOutput.trim().split('\n').filter(Boolean);
const shards = splitTestsByDuration(testFiles, shardCount);
const myShard = shards[shardIndex];
if (!myShard) {
console.error(`Invalid shard index ${shardIndex} for ${shardCount} shards`);
process.exit(1);
}
console.log(
`Shard ${shardIndex + 1}/${shardCount}: ${myShard.files.length} files, ~${(myShard.estimatedDuration / 1000).toFixed(1)}s`
);
// Write shard files list for the test runner to consume
writeFileSync('shard-files.txt', myShard.files.join('\n'), 'utf-8');
}
main();
After each CI run, collect and store test execution timings for future split optimization:
// scripts/timing-collector.ts
import { readFileSync, writeFileSync, existsSync } from 'fs';
interface JestResult {
testResults: Array<{
testFilePath: string;
perfStats: {
runtime: number;
};
}>;
}
interface PlaywrightResult {
suites: Array<{
file: string;
specs: Array<{
tests: Array<{
results: Array<{
duration: number;
}>;
}>;
}>;
}>;
}
interface TestTiming {
file: string;
duration: number;
lastRun: string;
}
export function collectJestTimings(resultsFile: string): TestTiming[] {
const results: JestResult = JSON.parse(readFileSync(resultsFile, 'utf-8'));
return results.testResults.map((test) => ({
file: test.testFilePath.replace(process.cwd() + '/', ''),
duration: test.perfStats.runtime,
lastRun: new Date().toISOString(),
}));
}
export function collectPlaywrightTimings(resultsFile: string): TestTiming[] {
const results: PlaywrightResult = JSON.parse(readFileSync(resultsFile, 'utf-8'));
const timings: TestTiming[] = [];
for (const suite of results.suites) {
let totalDuration = 0;
for (const spec of suite.specs) {
for (const test of spec.tests) {
for (const result of test.results) {
totalDuration += result.duration;
}
}
}
timings.push({
file: suite.file,
duration: totalDuration,
lastRun: new Date().toISOString(),
});
}
return timings;
}
export function mergeTimings(
existing: TestTiming[],
latest: TestTiming[]
): TestTiming[] {
const map = new Map<string, TestTiming>();
for (const timing of existing) {
map.set(timing.file, timing);
}
// Merge with exponential moving average to smooth out outliers
for (const timing of latest) {
const prev = map.get(timing.file);
if (prev) {
// EMA with alpha = 0.3 gives 70% weight to history, 30% to new data
const smoothedDuration = prev.duration * 0.7 + timing.duration * 0.3;
map.set(timing.file, {
file: timing.file,
duration: Math.round(smoothedDuration),
lastRun: timing.lastRun,
});
} else {
map.set(timing.file, timing);
}
}
return Array.from(map.values());
}
function main(): void {
const timingsFile = 'test-timings.json';
const existing: TestTiming[] = existsSync(timingsFile)
? JSON.parse(readFileSync(timingsFile, 'utf-8'))
: [];
let latest: TestTiming[] = [];
if (existsSync('jest-results.json')) {
latest = collectJestTimings('jest-results.json');
} else if (existsSync('playwright-results.json')) {
latest = collectPlaywrightTimings('playwright-results.json');
}
if (latest.length > 0) {
const merged = mergeTimings(existing, latest);
writeFileSync(timingsFile, JSON.stringify(merged, null, 2), 'utf-8');
console.log(`Updated timings for ${latest.length} test files (${merged.length} total)`);
} else {
console.warn('No test results found to collect timings from');
}
}
main();
Detect which files changed and determine which test groups need to run:
// scripts/detect-changes.ts
import { execSync } from 'child_process';
import { readFileSync, existsSync } from 'fs';
interface ChangeMap {
patterns: Array<{
glob: string;
testGroups: string[];
description: string;
}>;
testGroups: Record<
string,
{
command: string;
files?: string[];
description: string;
}
>;
}
interface DetectedChanges {
changedFiles: string[];
testGroupsToRun: Set<string>;
skipReason?: string;
}
export function detectChanges(baseBranch: string = 'main'): DetectedChanges {
let changedFiles: string[];
try {
const diffOutput = execSync(
`git diff --name-only origin/${baseBranch}...HEAD`,
{ encoding: 'utf-8' }
);
changedFiles = diffOutput.trim().split('\n').filter(Boolean);
} catch {
const diffOutput = execSync('git diff --name-only HEAD~1', {
encoding: 'utf-8',
});
changedFiles = diffOutput.trim().split('\n').filter(Boolean);
}
if (changedFiles.length === 0) {
return {
changedFiles: [],
testGroupsToRun: new Set(),
skipReason: 'No files changed',
};
}
const changeMap = loadChangeMap();
const testGroups = new Set<string>();
for (const file of changedFiles) {
for (const pattern of changeMap.patterns) {
if (matchGlob(file, pattern.glob)) {
for (const group of pattern.testGroups) {
testGroups.add(group);
}
}
}
}
// Safety net: if no patterns matched, run all tests
if (testGroups.size === 0) {
testGroups.add('all');
}
return {
changedFiles,
testGroupsToRun: testGroups,
};
}
function loadChangeMap(): ChangeMap {
const configPath = 'ci-config/config/change-map.json';
if (!existsSync(configPath)) {
return getDefaultChangeMap();
}
return JSON.parse(readFileSync(configPath, 'utf-8'));
}
function getDefaultChangeMap(): ChangeMap {
return {
patterns: [
{
glob: 'src/api/**',
testGroups: ['unit', 'api-integration'],
description: 'API source changes trigger unit and integration tests',
},
{
glob: 'src/components/**',
testGroups: ['unit', 'component'],
description: 'UI component changes trigger unit and component tests',
},
{
glob: 'src/pages/**',
testGroups: ['unit', 'e2e'],
description: 'Page-level changes trigger unit and E2E tests',
},
{
glob: 'src/lib/**',
testGroups: ['unit'],
description: 'Library changes trigger unit tests',
},
{
glob: 'src/db/**',
testGroups: ['unit', 'api-integration', 'e2e'],
description: 'Database changes trigger all test types',
},
{
glob: 'package.json',
testGroups: ['all'],
description: 'Dependency changes require full test run',
},
{
glob: '*.config.*',
testGroups: ['all'],
description: 'Config changes require full test run',
},
{
glob: '**/*.md',
testGroups: ['docs-only'],
description: 'Documentation-only changes skip tests',
},
{
glob: '.github/**',
testGroups: ['ci-only'],
description: 'CI configuration changes need validation',
},
],
testGroups: {
all: { command: 'npm test', description: 'Full test suite' },
unit: { command: 'npm run test:unit', description: 'Unit tests only' },
'api-integration': {
command: 'npm run test:integration',
description: 'API integration tests',
},
component: { command: 'npm run test:components', description: 'Component tests' },
e2e: { command: 'npx playwright test', description: 'End-to-end tests' },
'docs-only': {
command: 'echo "No tests needed for docs-only changes"',
description: 'Skip tests',
},
'ci-only': {
command: 'echo "CI config changed - validate workflow syntax only"',
description: 'Validate CI config',
},
},
};
}
function matchGlob(filePath: string, pattern: string): boolean {
const regexPattern = pattern
.replace(/\*\*/g, '<<<GLOBSTAR>>>')
.replace(/\*/g, '[^/]*')
.replace(/<<<GLOBSTAR>>>/g, '.*')
.replace(/\?/g, '.');
return new RegExp(`^${regexPattern}$`).test(filePath);
}
function main(): void {
const baseBranch = process.env.BASE_BRANCH || 'main';
const result = detectChanges(baseBranch);
console.log(`Changed files: ${result.changedFiles.length}`);
console.log(`Test groups to run: ${Array.from(result.testGroupsToRun).join(', ')}`);
if (result.skipReason) {
console.log(`Skip reason: ${result.skipReason}`);
}
// Output for GitHub Actions
const groups = Array.from(result.testGroupsToRun);
const shouldRun = groups.length > 0 && !groups.includes('docs-only');
console.log(`::set-output name=test-groups::${JSON.stringify(groups)}`);
console.log(`::set-output name=should-run-tests::${shouldRun}`);
}
main();
// scripts/cache-manager.ts
import { readFileSync, existsSync } from 'fs';
import { createHash } from 'crypto';
interface CacheLayer {
name: string;
paths: string[];
keyFiles: string[];
fallbackKeys: string[];
maxAge: number;
}
interface CacheConfig {
layers: CacheLayer[];
}
export function generateCacheKeys(config: CacheConfig): Array<{
name: string;
key: string;
restoreKeys: string[];
paths: string[];
}> {
const platform = process.env.RUNNER_OS || process.platform;
return config.layers.map((layer) => {
const fileHashes = layer.keyFiles
.filter((f) => existsSync(f))
.map((f) => hashFile(f))
.join('-');
const key = `${platform}-${layer.name}-${fileHashes}`;
const restoreKeys = layer.fallbackKeys.map(
(fallback) => `${platform}-${layer.name}-${fallback}`
);
return {
name: layer.name,
key,
restoreKeys,
paths: layer.paths,
};
});
}
function hashFile(filePath: string): string {
const content = readFileSync(filePath);
return createHash('sha256').update(content).digest('hex').substring(0, 16);
}
export const DEFAULT_CACHE_CONFIG: CacheConfig = {
layers: [
{
name: 'node-modules',
paths: ['node_modules', '~/.pnpm-store'],
keyFiles: ['pnpm-lock.yaml', 'package.json'],
fallbackKeys: [''],
maxAge: 604800000, // 7 days
},
{
name: 'playwright-browsers',
paths: ['~/.cache/ms-playwright'],
keyFiles: ['package.json'],
fallbackKeys: [''],
maxAge: 2592000000, // 30 days
},
{
name: 'build-cache',
paths: ['.next/cache', 'dist', '.turbo'],
keyFiles: ['tsconfig.json', 'next.config.js'],
fallbackKeys: [''],
maxAge: 86400000, // 1 day
},
{
name: 'test-timings',
paths: ['test-timings.json'],
keyFiles: [],
fallbackKeys: [''],
maxAge: 2592000000, // 30 days
},
],
};
# .github/workflows/ci-optimized.yml
name: Optimized CI
on:
push:
branches: [main]
pull_request:
branches: [main]
concurrency:
group: ci-${{ github.ref }}
cancel-in-progress: true
jobs:
# Step 1: Detect changes and determine what to test
detect-changes:
runs-on: ubuntu-latest
outputs:
backend: ${{ steps.changes.outputs.backend }}
frontend: ${{ steps.changes.outputs.frontend }}
config: ${{ steps.changes.outputs.config }}
docs-only: ${{ steps.changes.outputs.docs }}
shard-count: ${{ steps.shards.outputs.count }}
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Detect changed file categories
id: changes
uses: dorny/paths-filter@v3
with:
filters: |
backend:
- 'src/api/**'
- 'src/db/**'
- 'src/lib/**'
frontend:
- 'src/components/**'
- 'src/pages/**'
- 'src/styles/**'
config:
- 'package.json'
- 'pnpm-lock.yaml'
- '*.config.*'
docs:
- '**/*.md'
- 'docs/**'
- name: Determine optimal shard count
id: shards
run: |
if [[ "${{ steps.changes.outputs.config }}" == "true" ]]; then
echo "count=4" >> $GITHUB_OUTPUT
elif [[ "${{ steps.changes.outputs.backend }}" == "true" && "${{ steps.changes.outputs.frontend }}" == "true" ]]; then
echo "count=4" >> $GITHUB_OUTPUT
elif [[ "${{ steps.changes.outputs.backend }}" == "true" || "${{ steps.changes.outputs.frontend }}" == "true" ]]; then
echo "count=2" >> $GITHUB_OUTPUT
else
echo "count=1" >> $GITHUB_OUTPUT
fi
# Step 2: Lint and type-check (fastest feedback)
lint:
runs-on: ubuntu-latest
needs: detect-changes
if: needs.detect-changes.outputs.docs-only != 'true'
steps:
- uses: actions/checkout@v4
- uses: pnpm/action-setup@v2
with:
version: 9
- uses: actions/setup-node@v4
with:
node-version: 20
cache: 'pnpm'
- run: pnpm install --frozen-lockfile
- name: Run lint
run: pnpm lint
- name: Run type check
run: pnpm tsc --noEmit
# Step 3: Unit tests (fast, parallel shards)
unit-tests:
runs-on: ubuntu-latest
needs: [detect-changes, lint]
if: needs.detect-changes.outputs.docs-only != 'true'
strategy:
fail-fast: false
matrix:
shard: [1, 2]
steps:
- uses: actions/checkout@v4
- uses: pnpm/action-setup@v2
with:
version: 9
- uses: actions/setup-node@v4
with:
node-version: 20
cache: 'pnpm'
- run: pnpm install --frozen-lockfile
- name: Restore test timings
uses: actions/cache@v4
with:
path: test-timings.json
key: test-timings-${{ github.ref }}
restore-keys: |
test-timings-refs/heads/main
test-timings-
- name: Run unit tests (shard ${{ matrix.shard }}/2)
run: |
pnpm vitest run \
--reporter=json \
--outputFile=vitest-results.json \
--shard=${{ matrix.shard }}/2
- name: Collect test timings
if: always()
run: npx tsx ci-config/scripts/timing-collector.ts
- name: Save test timings
if: always()
uses: actions/cache/save@v4
with:
path: test-timings.json
key: test-timings-${{ github.ref }}-${{ github.run_id }}-shard-${{ matrix.shard }}
- name: Upload test results
if: always()
uses: actions/upload-artifact@v4
with:
name: unit-results-shard-${{ matrix.shard }}
path: vitest-results.json
retention-days: 7
# Step 4: Integration tests (medium speed, conditional)
integration-tests:
runs-on: ubuntu-latest
needs: [detect-changes, lint]
if: |
needs.detect-changes.outputs.backend == 'true' ||
needs.detect-changes.outputs.config == 'true'
services:
postgres:
image: postgres:16
env:
POSTGRES_PASSWORD: test
POSTGRES_DB: test
ports:
- 5432:5432
options: >-
--health-cmd pg_isready
--health-interval 10s
--health-timeout 5s
--health-retries 5
steps:
- uses: actions/checkout@v4
- uses: pnpm/action-setup@v2
with:
version: 9
- uses: actions/setup-node@v4
with:
node-version: 20
cache: 'pnpm'
- run: pnpm install --frozen-lockfile
- name: Run integration tests
run: pnpm run test:integration
env:
DATABASE_URL: postgresql://postgres:test@localhost:5432/test
# Step 5: E2E tests (slowest, most shards, conditional)
e2e-tests:
runs-on: ubuntu-latest
needs: [detect-changes, lint]
if: |
needs.detect-changes.outputs.frontend == 'true' ||
needs.detect-changes.outputs.config == 'true'
strategy:
fail-fast: false
matrix:
shard: [1, 2, 3, 4]
steps:
- uses: actions/checkout@v4
- uses: pnpm/action-setup@v2
with:
version: 9
- uses: actions/setup-node@v4
with:
node-version: 20
cache: 'pnpm'
- run: pnpm install --frozen-lockfile
- name: Cache Playwright browsers
uses: actions/cache@v4
with:
path: ~/.cache/ms-playwright
key: playwright-${{ runner.os }}-${{ hashFiles('pnpm-lock.yaml') }}
- name: Install Playwright browsers
run: npx playwright install --with-deps chromium
- name: Build application
run: pnpm build
- name: Run E2E tests (shard ${{ matrix.shard }}/4)
run: npx playwright test --shard=${{ matrix.shard }}/4
- name: Upload failure artifacts
if: failure()
uses: actions/upload-artifact@v4
with:
name: e2e-results-shard-${{ matrix.shard }}
path: test-results/
retention-days: 7
Pre-populate caches on a schedule so the first PR of the day gets warm caches:
# .github/workflows/cache-warmup.yml
name: Cache Warmup
on:
schedule:
- cron: '0 6 * * 1-5' # Weekdays at 6 AM UTC
workflow_dispatch:
jobs:
warmup:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: pnpm/action-setup@v2
with:
version: 9
- uses: actions/setup-node@v4
with:
node-version: 20
- name: Install dependencies
run: pnpm install --frozen-lockfile
- name: Cache node_modules
uses: actions/cache/save@v4
with:
path: |
node_modules
~/.pnpm-store
key: pnpm-${{ runner.os }}-${{ hashFiles('pnpm-lock.yaml') }}
- name: Install Playwright browsers
run: npx playwright install --with-deps chromium
- name: Cache Playwright
uses: actions/cache/save@v4
with:
path: ~/.cache/ms-playwright
key: playwright-${{ runner.os }}-${{ hashFiles('pnpm-lock.yaml') }}
- name: Build application
run: pnpm build
- name: Cache build artifacts
uses: actions/cache/save@v4
with:
path: |
.next/cache
dist
.turbo
key: build-${{ runner.os }}-${{ hashFiles('tsconfig.json') }}-${{ github.sha }}
Track and analyze pipeline performance over time to identify regressions and optimization opportunities:
// scripts/pipeline-analyzer.ts
interface PipelineRun {
runId: string;
branch: string;
timestamp: string;
totalDuration: number;
jobs: Array<{
name: string;
duration: number;
status: 'success' | 'failure' | 'cancelled';
steps: Array<{
name: string;
duration: number;
}>;
}>;
}
interface PipelineAnalysis {
averageDuration: number;
medianDuration: number;
p95Duration: number;
slowestJobs: Array<{ name: string; avgDuration: number }>;
slowestSteps: Array<{ jobName: string; stepName: string; avgDuration: number }>;
cacheSavings: number;
recommendations: string[];
}
export function analyzePipeline(runs: PipelineRun[]): PipelineAnalysis {
if (runs.length === 0) {
return {
averageDuration: 0,
medianDuration: 0,
p95Duration: 0,
slowestJobs: [],
slowestSteps: [],
cacheSavings: 0,
recommendations: [],
};
}
const durations = runs.map((r) => r.totalDuration).sort((a, b) => a - b);
const average = durations.reduce((a, b) => a + b, 0) / durations.length;
const median = durations[Math.floor(durations.length / 2)];
const p95 = durations[Math.floor(durations.length * 0.95)];
// Aggregate job durations across runs
const jobDurations = new Map<string, number[]>();
const stepDurations = new Map<string, number[]>();
for (const run of runs) {
for (const job of run.jobs) {
if (!jobDurations.has(job.name)) {
jobDurations.set(job.name, []);
}
jobDurations.get(job.name)!.push(job.duration);
for (const step of job.steps) {
const key = `${job.name}::${step.name}`;
if (!stepDurations.has(key)) {
stepDurations.set(key, []);
}
stepDurations.get(key)!.push(step.duration);
}
}
}
const slowestJobs = Array.from(jobDurations.entries())
.map(([name, times]) => ({
name,
avgDuration: times.reduce((a, b) => a + b, 0) / times.length,
}))
.sort((a, b) => b.avgDuration - a.avgDuration)
.slice(0, 5);
const slowestSteps = Array.from(stepDurations.entries())
.map(([key, times]) => {
const [jobName, stepName] = key.split('::');
return {
jobName,
stepName,
avgDuration: times.reduce((a, b) => a + b, 0) / times.length,
};
})
.sort((a, b) => b.avgDuration - a.avgDuration)
.slice(0, 10);
const recommendations = generateOptimizationRecommendations(
average,
slowestJobs,
slowestSteps
);
return {
averageDuration: Math.round(average),
medianDuration: Math.round(median),
p95Duration: Math.round(p95),
slowestJobs,
slowestSteps,
cacheSavings: estimateCacheSavings(stepDurations),
recommendations,
};
}
function generateOptimizationRecommendations(
avgDuration: number,
slowestJobs: Array<{ name: string; avgDuration: number }>,
slowestSteps: Array<{ jobName: string; stepName: string; avgDuration: number }>
): string[] {
const recommendations: string[] = [];
if (avgDuration > 600000) {
recommendations.push(
'Pipeline average exceeds 10 minutes. Consider increasing parallelization or enabling selective test execution.'
);
}
for (const job of slowestJobs) {
if (job.avgDuration > 300000) {
recommendations.push(
`Job "${job.name}" averages ${(job.avgDuration / 60000).toFixed(1)}min. Consider splitting into shards.`
);
}
}
for (const step of slowestSteps) {
if (step.stepName.toLowerCase().includes('install') && step.avgDuration > 60000) {
recommendations.push(
`Step "${step.stepName}" in "${step.jobName}" takes ${(step.avgDuration / 1000).toFixed(0)}s. Verify dependency caching is working correctly.`
);
}
if (step.stepName.toLowerCase().includes('build') && step.avgDuration > 120000) {
recommendations.push(
`Build step in "${step.jobName}" takes ${(step.avgDuration / 1000).toFixed(0)}s. Consider incremental builds or build caching with Turborepo.`
);
}
}
return recommendations;
}
function estimateCacheSavings(stepDurations: Map<string, number[]>): number {
let savings = 0;
for (const [key, times] of stepDurations) {
if (key.toLowerCase().includes('install') || key.toLowerCase().includes('cache')) {
if (times.length > 1) {
const first = times[0];
const subsequent = times.slice(1);
const avgSubsequent =
subsequent.reduce((a, b) => a + b, 0) / subsequent.length;
if (first > avgSubsequent * 2) {
savings += (first - avgSubsequent) * subsequent.length;
}
}
}
}
return Math.round(savings);
}
The critical path is the longest sequential chain of jobs in your pipeline. Parallelizing jobs off the critical path does not reduce total duration. Focus optimization on the critical path:
// scripts/critical-path.ts
interface JobNode {
name: string;
estimatedDuration: number;
dependencies: string[];
canParallelize: boolean;
shardable: boolean;
}
export function findCriticalPath(jobs: JobNode[]): {
path: JobNode[];
duration: number;
parallelizableSavings: number;
} {
const jobMap = new Map(jobs.map((j) => [j.name, j]));
const memo = new Map<string, number>();
function longestPath(jobName: string): number {
if (memo.has(jobName)) return memo.get(jobName)!;
const job = jobMap.get(jobName);
if (!job) return 0;
let maxDepDuration = 0;
for (const dep of job.dependencies) {
maxDepDuration = Math.max(maxDepDuration, longestPath(dep));
}
const total = maxDepDuration + job.estimatedDuration;
memo.set(jobName, total);
return total;
}
// Find the endpoint of the critical path
let maxDuration = 0;
let criticalEndJob = '';
for (const job of jobs) {
const duration = longestPath(job.name);
if (duration > maxDuration) {
maxDuration = duration;
criticalEndJob = job.name;
}
}
// Reconstruct the critical path by walking backwards
const path: JobNode[] = [];
let current = criticalEndJob;
while (current) {
const job = jobMap.get(current);
if (!job) break;
path.unshift(job);
let nextJob = '';
let nextDuration = 0;
for (const dep of job.dependencies) {
const depDuration = memo.get(dep) || 0;
if (depDuration > nextDuration) {
nextDuration = depDuration;
nextJob = dep;
}
}
current = nextJob;
}
const totalSequentialDuration = jobs.reduce(
(sum, job) => sum + job.estimatedDuration,
0
);
const criticalPathDuration = path.reduce(
(sum, job) => sum + job.estimatedDuration,
0
);
return {
path,
duration: criticalPathDuration,
parallelizableSavings: totalSequentialDuration - criticalPathDuration,
};
}
concurrency key with cancel-in-progress: true handles this automatically.10. Monitor pipeline duration trends over time. Track P50 and P95 pipeline durations weekly. A gradual increase of 30 seconds per week adds up to 25 minutes over a year. Catch regressions early with automated alerting on duration spikes.
11. Use service containers for integration test dependencies. Instead of installing PostgreSQL or Redis in your workflow steps, use GitHub Actions service containers. They start in parallel with job setup and are ready when your tests need them.
12. Keep Docker images minimal for CI runners. If your CI uses custom Docker images, minimize their size. Every megabyte of image that needs to be pulled adds to every pipeline run. Use multi-stage builds and slim base images.
Running all tests on every PR regardless of changes. A documentation-only PR does not need E2E tests. A frontend-only change does not need backend integration tests. Implement change detection to skip unnecessary work and save both time and compute resources.
Using sequential job execution when parallelism is possible. If unit tests and E2E tests have no dependency on each other, run them in parallel. Restructure your pipeline so that only actual data dependencies create sequential chains.
Caching too broadly or too narrowly. Caching node_modules without including the lock file hash in the cache key means stale dependencies persist. Caching only node_modules but not Playwright browsers means downloading 100+ MB of browser binaries on every run.
Splitting tests by file count instead of duration. Ten test files may take 10 seconds or 10 minutes depending on their content. Always use duration-based splitting when timing data is available to ensure balanced shard execution times.
Ignoring the critical path. Parallelizing jobs that are already off the critical path does not reduce total pipeline duration. Identify and focus optimization on the jobs that form the longest sequential chain.
Not canceling redundant pipeline runs. Without concurrency controls, every push creates a new pipeline run. Three pushes in 5 minutes create three full runs, wasting resources and delaying results for other developers waiting in the queue.
Hard-coding shard counts. A static 4-shard configuration may be wasteful for small PRs and insufficient for full test runs on main. Dynamically determine shard counts based on the scope of changes detected.
Skipping tests to speed up the pipeline. Disabling tests is not optimization; it is risk accumulation. Optimize the tests themselves (reduce flakiness, parallelize, cache) rather than removing them from the pipeline.
Cache misses on every run. Verify that the cache key includes only deterministic inputs. Environment variables, timestamps, and random values in cache keys cause every run to miss. Use hashFiles() on lock files and configuration files for consistent keys.
Sharded tests produce inconsistent results. Test isolation issues become visible under sharding because tests run in different orders and different processes. Identify tests that depend on shared state (global variables, database records, file system artifacts) and fix them to be truly independent.
Selective test execution misses a regression. Review your change-to-test mapping configuration. Common gaps include: shared utility files that are not mapped to all their consumers, configuration changes that should trigger full runs, and transitive dependencies that are not tracked in the change map.
Pipeline duration increases after adding caching. This can happen when cache save and restore steps take longer than the operation they are caching. Very small caches (under 10 MB) may not be worth the overhead. Profile the cache steps themselves to verify they provide net savings.
Test timings file grows unbounded. Prune entries for test files that no longer exist. When files are renamed or deleted, their timing entries remain in the file indefinitely. Add a cleanup step that removes entries for files not present in the current codebase.
GitHub Actions job matrix generates too many combinations. Matrix strategies with multiple dimensions (OS x Node version x shard) can produce dozens of jobs. Use include and exclude to limit combinations to only those that provide unique value.
Jobs fail with "No space left on device." CI runners have limited disk space. Large caches, Docker images, and build artifacts can exhaust the available storage. Add cleanup steps between resource-intensive operations, use slimmer base images, and prune unnecessary artifacts before running tests.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take pramoddutta/ci pipeline optimizer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference npm, npx.
Without those the skill loads but fails at the first command.