The Meta Ad Library is public. Every ad your competitor is running right now is visible. The problem is nobody has time to check it every day. This system does.
Your competitor launched a new ad last Tuesday that is crushing it. You will not find out for 6 weeks when your own campaign performance drops and you start investigating. By then they have already captured the audience and the CPM has spiked. Competitive intelligence in paid media is a speed game, and you are playing it manually.
system cost
manual cost replaced
cost reduction
The stack
This pipeline scrapes the Meta Ad Library for every competitor you specify, identifies which ads have been running the longest (the signal for profitability), extracts the hooks, CTAs, and visual patterns, and delivers a daily briefing to your Slack with specific recommendations for your own campaigns.
Runs scheduled actors against the Meta Ad Library API to pull every active ad for your list of competitor pages. Extracts creative URLs, copy text, and launch dates.
Reads every scraped ad, identifies the hook pattern, the emotional angle, and the CTA structure, then compares it against your current campaigns to find gaps.
Compiles the analysis into a daily Slack report with actionable recommendations and optionally feeds winning patterns directly into your ad generation engine.
Where the daily briefing lands: hooks, CTAs, and visual patterns your team can act on the moment it arrives.
What it replaces
2 line items, starting with the competitive intel analyst, priced against the tools that now do the work. The last bar is the whole system at $80/mo.
Competitive Intel Analyst, now Apify + Claude
Creative Strategist, now Pattern Analysis Agent
The whole system
Monthly cost of each role the system replaces, against the system itself.
Why it holds
Everyone can buy Apify. What separates the setups that last from the ones that collapse is one idea.
The longest running ad in a competitor account is almost always their most profitable. Meta charges more for underperforming ads, so anything running 30+ days has positive unit economics. That is the ad you should be studying and adapting, not their newest experiment.
What is inside
This is not theory. 3 pieces, ready to run.
In this playbook
2 of 3How it's built
The file tree, so you know exactly what you would be standing up.
- scraping/
- meta_library_actor.jscompetitor_list.jsonad_schema.ts
- analysis/
- hook_extractor.jspattern_matcher.tsslack_reporter.js
One rule to leave with, the one that stops the competitive intel analyst from creeping back into the budget.
Your competitor already tested the hook for you. Stop guessing and start studying.
The numbers above trace back to the Meta Ad Library Analysis Methods, not projections.
You can wire Apify and the rest of this stack by hand from the playbook above. Or you skip the assembly, because standing up systems like this is exactly what Ultron does.
is what this system replaces every month. Ultron runs it for $80/mo.
No card required. Set it up in about ten minutes.