---

name: apify-ci-integration description: | Configure CI/CD pipelines for Apify Actor builds and deployments. Use when automating Actor deployment via GitHub Actions, running integration tests against the live Apify API, or building CI/CD for scrapers that push to the Apify platform. Trigger with "apify CI", "apify GitHub Actions", "apify automated deploy", "CI apify", "apify pipeline", "auto deploy actor". allowed-tools: Write, Bash(gh:), Bash(npm:) version: 1.5.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:

• saas
• scraping
• automation
• apify compatibility: Designed for Claude Code

---

Apify CI Integration

Overview

Automate Apify Actor builds, tests, and deployments using GitHub Actions — test-on-PR, deploy-on-merge, live-API integration testing, and Docker build verification. Three workflow files do the work (test, deploy, verify-build); full copy-paste-ready definitions live in references/workflows.md.

Prerequisites

• GitHub repository with Actions enabled
• Apify API token stored as a GitHub secret
• Actor code in the repository

Authentication

CI authenticates to Apify with a personal API token, never a hard-coded credential:

• Store the token as a GitHub Actions secret (gh secret set APIFY_TOKEN), and expose it to a job only via env: APIFY_TOKEN: ${{ secrets.APIFY_TOKEN }}.
• The Apify CLI reads it as apify login --token $APIFY_TOKEN; the REST API and apify-client read it from the APIFY_TOKEN environment variable.
• Use separate APIFY_TOKEN_TEST / APIFY_TOKEN_PROD secrets so integration runs never touch production data. Get tokens from Apify Console → Settings → Integrations.

Instructions

Step 1: Configure GitHub Secrets

# Store Apify token for CI
gh secret set APIFY_TOKEN --body "apify_api_YOUR_CI_TOKEN"

# Optional: separate tokens for test vs production
gh secret set APIFY_TOKEN_TEST --body "apify_api_test_token"
gh secret set APIFY_TOKEN_PROD --body "apify_api_prod_token"

Step 2: Create the test workflow

Add .github/workflows/apify-test.yml with two jobs — unit-tests on every PR and integration-tests gated to push (merge) events. The integration job proves connectivity before running tests:

      - name: Verify Apify connection
        run: |
          curl -sf -H "Authorization: Bearer $APIFY_TOKEN" \
            https://api.apify.com/v2/users/me | jq '.data.username'

Full two-job workflow: references/workflows.md (Test Workflow).

Step 3: Create the deploy workflow

Add .github/workflows/apify-deploy.yml — triggered on merges that touch src/**, package.json, or .actor/** (plus workflow_dispatch). It builds, tests, installs the Apify CLI, apify pushes, then runs a minimal smoke call to confirm the new build actually starts. Full definition: references/workflows.md (Deploy Workflow).

Step 4: Write integration tests

Gate live-API tests on the token so they skip cleanly when it is absent:

const SKIP_INTEGRATION = !process.env.APIFY_TOKEN;

describe.skipIf(SKIP_INTEGRATION)('Apify Integration', () => {
  it('should authenticate successfully', async () => {
    const user = await client.user().get();
    expect(user.username).toBeTruthy();
  });
});

The complete suite (auth, live Actor run, create/delete a named dataset with cleanup) is in references/integration-tests.md.

Step 5: Verify the Actor build

Add a verify-build.yml that docker builds the Actor image and boots it once to confirm the entry point loads. Optionally add branch-protection rules that require the CI contexts before merge. Both blocks: references/workflows.md (Actor Build Verification).

Output

Applying this skill produces committed CI/CD infrastructure in the repository:

• .github/workflows/apify-test.yml — unit tests on every PR, integration tests on merge to main.
• .github/workflows/apify-deploy.yml — apify push + post-deploy smoke test on merge, plus manual workflow_dispatch.
• .github/workflows/verify-build.yml — Docker build + entry-point check on PRs.
• tests/integration/apify.test.ts — token-gated live-API integration suite.
• Configured GitHub secrets (APIFY_TOKEN, optional _TEST / _PROD variants) and, optionally, branch-protection requiring the CI checks to pass.

Once merged, every PR runs unit tests, every merge deploys and smoke-tests the Actor, and a failed deploy surfaces a ::error:: annotation in the run log.

Error Handling

Issue	Cause	Solution
APIFY_TOKEN not set	Secret not configured	gh secret set APIFY_TOKEN
Integration test timeout	Slow Actor run	Increase timeout, use smaller input
Docker build fails in CI	Local-only deps	Commit package-lock.json
apify push fails	Not logged in	Add apify login --token step
Flaky integration tests	External service issues	Add retries, use test.retry(2)

Examples

Minimal test-on-PR gate — the smallest useful setup is Step 1 (store the secret) plus the unit-tests job from the test workflow. Every PR then runs npm ci && npm run build && npm test before it can merge.

Full deploy pipeline — add the deploy workflow so a merge to main that touches src/** builds, tests, runs apify push, then smoke-tests the new build:

      - name: Push Actor to Apify
        run: apify push

      - name: Verify deployment
        run: |
          ACTOR_ID=$(jq -r '.name' .actor/actor.json)
          apify actors call $ACTOR_ID \
            --input='{"startUrls":[{"url":"https://example.com"}],"maxItems":1}' \
            --timeout=120

apify-client app (not Actor dev) — for an app that calls Actors rather than publishing one, mock apify-client in unit tests and gate a real-token integration job to main. Full workflow: references/workflows.md (CI Configuration for apify-client Apps).

See references/workflows.md for every full workflow and references/integration-tests.md for the complete integration suite.

Resources

• GitHub Actions Documentation
• Apify CLI Reference
• Actor Deployment

Next Steps

For runtime deployment patterns beyond CI wiring — release channels, versioned Actor builds, and rollback — see the apify-deploy-integration skill in this pack.