Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/apify-prod-checklist/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill apify-prod-checklist. Copyright stays with the author (MIT).

Apify Production Checklist

Overview

Complete checklist for deploying Actors to the Apify platform and integrating them into production applications. Covers Actor configuration, scheduling, monitoring, alerting, and rollback. Work top to bottom: clear the pre-deployment gates, then run the six deploy steps, then wire the alert conditions.

Prerequisites

• Actor tested locally with apify run
• apify login configured with production token
• Familiarity with apify-core-workflow-a and apify-deploy-integration

Pre-Deployment Checklist

Actor Configuration

• .actor/actor.json has correct name, title, description
• INPUT_SCHEMA.json validates all required inputs
• Dockerfile uses pinned base image version (apify/actor-node:20, not latest)
• package-lock.json committed (deterministic installs)
• Memory set appropriately (start at 1024MB, tune after profiling)
• Timeout set with buffer (2x expected runtime)

Code Quality

• Actor.main() wraps entry point (handles init/exit/errors)
• failedRequestHandler logs failures without crashing Actor
• Input validation at Actor start (if (!input?.startUrls) throw ...)
• No hardcoded URLs, credentials, or magic numbers
• Proxy configured for target sites that block datacenter IPs
• maxRequestsPerCrawl set to prevent runaway costs

Data Output

• Dataset schema documented (consistent field names)
• SUMMARY key-value store record saved with run stats
• Large payloads chunked (9MB dataset push limit)
• PII sanitized before storage

Instructions

Read the Actor's .actor/actor.json, INPUT_SCHEMA.json, and Dockerfile first to confirm the pre-deployment gates above, then run the six deploy steps. Each step's full command and code block lives in references/implementation.md; the skeleton is below.

1. Deploy Actor — apify push, then apify builds ls to confirm the build, then apify actors call with a small production-like input to smoke-test on-platform.
2. Configure Scheduling — create a cron schedule with client.schedules().create({...}) (or Apify Console: Actors > Your Actor > Schedules). Set cronExpression, runInput, and runOptions (memory/timeout).
3. Set Up Webhooks — client.webhooks().create({...}) on ACTOR.RUN.SUCCEEDED/FAILED/TIMED_OUT with a payloadTemplate posting runId, status, and datasetId to your server.
4. Monitor Runs — a checkActorHealth(actorId, lookbackHours) helper lists recent runs and reports success rate, failures, timeouts, and total cost.
5. Implement Rollback — apify builds ls, then repoint the Actor at a prior build via the POST /v2/acts/ACTOR_ID?build=N API, or redeploy from a git tag.
6. Cost Guard — runWithCostGuard(actorId, input, maxCostUsd) polls usageTotalUsd every 30s and aborts the run if it exceeds budget.

The first deploy step in full:

# Build and push to Apify platform
apify push

# Verify the build succeeded
apify builds ls

Output

Working through this skill produces a production-ready Actor with:

• A pushed, verified build (apify builds ls shows a SUCCEEDED build).
• A live cron schedule and a completion webhook firing on success/failure/timeout.
• A repeatable health check printing success rate, failure/timeout counts, and 24h cost.
• A tested rollback path (build repoint or git-tag redeploy).
• A cost guard that aborts runs exceeding budget.

Health-check output looks like:

Actor: username/product-scraper
Last 24h: 3 runs, 66.7% success
Failed: 1, Timed out: 0
Total cost: $0.4213

Production Alert Conditions

Error Handling

Examples

Four worked examples — a first production deploy, health-check output, a cost guard aborting a runaway run, and a build rollback — are in references/examples.md. A first production deploy in brief:

# Smoke-test on-platform with a tiny input before scheduling
apify actors call username/product-scraper \
  --input='{"startUrls":[{"url":"https://target.com"}],"maxItems":10}'

Then create the daily schedule and completion webhook (implementation.md Steps 2–3).

Resources

• Full deploy walkthrough (Steps 1–6)
• Worked examples
• Actor Deployment Guide
• Schedules Documentation
• Webhook Event Types
• Usage & Billing

Next Steps

Once production is stable, plan version upgrades with the apify-upgrade-migration skill: it covers bumping the Actor base image, migrating INPUT_SCHEMA.json fields without breaking existing schedules, and re-running this checklist against the new build before repointing traffic.