Automation: guides & tutorials

Most scraping guides stop at the one-off run. The real leverage is recurring data: the prices, reviews, and listings that change while you are not watching. Cowork's scheduled tasks turn a single scrape into a standing job.

Agent Skills are modular capabilities that extend Claude: each Skill packages instructions, metadata, and optional resources that Claude uses automatically when they are relevant to your request. Instead of re-explaining how you like to scrape every time, you encode it once, and Claude reaches for it on its own.

This guide builds a scraping skill that calls Apify, and points you at the official plugin if you would rather not start from scratch.

Anthropic ships both Claude Code and Claude Cowork on the same agentic architecture, which confuses a lot of people into thinking they overlap. They do not. The difference is who each is for and where it runs.

Claude Cowork is Anthropic's "hand Claude real work" surface: it runs on the same agentic engine as Claude Code but with no terminal, so it can take on multi-step tasks and finish them for you. Cowork can already search the web and, with computer use, drive a browser a page at a time. What it has no answer for is bulk structured extraction: paginating a result grid, holding a schema across thousands of rows, rotating proxies, and surviving anti-bot defences. That is exactly what Apify adds: a store of pre-built scrapers Cowork can call as tools.

This guide connects the two and turns "get me this web data" into a spreadsheet, with no code.

Create a free Apify account (free monthly credits) →

Trustpilot is where customers rate companies at scale, so its reviews are a goldmine for reputation monitoring, competitor benchmarking, and product research. The site's UI paginates and caps what you can see, and it has no export button, which is exactly the gap a scraper fills.

This guide covers no-code Trustpilot extraction on Apify: which Actor to pick, the input fields that matter, what it costs, and how to turn the review text into insight with an LLM.

Start on Apify (free monthly credits) →

Google reviews are one of the richest public signals a business leaves behind: star ratings, written feedback, dates, and the owner's replies. Pulling them at scale lets you monitor reputation, track sentiment over time, and benchmark competitors, none of which the Google Maps UI lets you export.

This guide walks through no-code review extraction with the Apify Google Maps Reviews Scraper (used by more than 48,000 people), the input fields that matter, what it costs, and how to turn the raw text into insight with an LLM.

Start on Apify (free monthly credits) →

Zapier Pro costs $69/month for 2,000 tasks and 20 Zaps. Hit your task cap mid-month and your workflows stop running until the next billing cycle — or you upgrade to the $103/month Team plan. If your automation volume is growing or your workflows touch internal systems and proprietary data, a self-hosted alternative gives you unlimited executions, full control over credentials and API keys, and a fixed monthly VPS cost that doesn't scale with task count.

TL;DR

• One docker-compose.yml: n8n + LiteLLM + Twenty CRM + shared PostgreSQL + Redis + Caddy
• Measured idle RAM: ~1.7 GB; peak: ~2.5 GB (parallel workflow executions with live LiteLLM calls)
• Minimum Liquid Web tier: 8 GB Managed VPS (~$33–$40/mo)
• Zapier Pro + unmanaged OpenAI API + HubSpot Starter: ~$69 + variable + $50/mo vs ~$33/mo self-hosted

Most sales teams that want AI-augmented automation end up in one of two bad places: paying for Zapier Pro, a direct OpenAI API key they can't audit, and HubSpot CRM — three separate subscriptions that don't compose well and bill regardless of usage. Or they write fragile Python scripts that break on every API change and nobody wants to maintain.

This guide deploys the middle path: Twenty CRM for pipeline management, n8n for visual workflow automation, and LiteLLM as an OpenAI-compatible AI proxy — all on a single Liquid Web 8 GB VPS. n8n calls LiteLLM for every AI task (lead enrichment summaries, proposal draft generation, inbound form classification), and LiteLLM provides budget caps, model fallbacks, and a unified request log.

"Agentic AI" is not just for enterprise. In April 2026, small and medium businesses can deploy practical AI agents using no-code tools — agents that qualify leads, answer support questions, schedule content, chase invoices, and monitor inventory.

This playbook covers five agents built with tools you can set up without a developer: Claude, Make.com, n8n, Apify, and Google Sheets. For Model Context Protocol (MCP) setups that connect Claude to your tools (see the "Claude Desktop + MCP" guide in Next steps below), the same agent ideas apply once data and actions are wired in.

TL;DR:

Prerequisites:

• Make.com account (free tier available)
• Claude: Use Claude API for automation (pay-per-token billing via Anthropic Console — free tier includes limited credits). Claude Pro ($20/mo) is a chat subscription for claude.ai; it does not provide API access for Make.com or n8n HTTP calls.
• Google account (for Sheets, Gmail)
• No coding required for agents 1–4

Enterprise competitive intelligence tools — Crayon, Klue, Kompyte, Similarweb — charge $300–$2,000/month (quote-based, plan-dependent) for competitive monitoring dashboards. This same monitoring can be built with Apify for data collection, Claude for analysis, n8n for orchestration, and a free dashboard — for under $50/month (starting cost; scales with competitor count, Actor fees, and proxy usage).

This guide builds it step by step: from identifying what to monitor, to automated daily scrapes, to AI-powered change detection that alerts your team in Slack when competitors make moves that matter.

TL;DR: