Agent skill · software engineering · jeremylongshore
apify-prod-checklist
Production readiness checklist for Apify Actor deployments. Use when deploying an Actor to production, preparing for launch, or validating Actor configuration, scheduling, monitoring, and rollback before going live. Trigger with "apify production", "deploy actor to prod", "apify go-live", "apify launch checklist", "actor production ready".
Why this skill is useful
Adds a comprehensive production readiness checklist with specific commands and configurations for deploying Apify Actors, which the AI wouldn't reliably generate on its own.
What it needs
Requires apify installed locally. Requires apify account access. About 4k tokens when loaded. Last updated 2026-08-07. 2,604 stars on the source repository.
What this skill does
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 [ ] INPUTSCHEMA.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, INPUTSCHEMA.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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills jeremylongshore/apify-prod-checklist