apify-ads-intelligence

Who is advertising what, and where, taken live from the public ad archives that Meta, Google, TikTok and LinkedIn keep — plus a best-effort look at promoted posts on X.

WHEN TO USE

SCOPE

USE FOR

Pull the ads a competitor is running on Facebook, Instagram, Google, TikTok or LinkedIn.
Search ads by keyword or industry to see which advertisers are bidding on it.
Rank the longest-running or highest-engagement ads in a niche to find proven creative angles.
Trace an ad's landing pages back to the funnel and domain behind a campaign.
Compare a brand's ad spend and messaging across all five platforms at once.

NOT FOR

Skip this for a one-off Meta-ads check inside a broader ecommerce question — use apify-ecommerce instead.

This is a saved copy of the skill's SKILL.md instructions. It may differ from the upstream file or the version you install.

In this document (7)

Ads Intelligence Cluster

Answer natural language questions about ads, ad libraries, and competitor advertising activity by routing to the right Apify Actor and delivering a synthesized answer.

CLI rules: Always pass --user-agent apify-awesome-skills/apify-ads-intelligence, --json (or the relevant --format flag on datasets get-items), and 2>/dev/null. The --user-agent flag is critical for telemetry — never omit it.

Note on platform coverage

Meta, Google, TikTok, LinkedIn: real public ad libraries with rich data (creatives, targeting, dates, reach where disclosed).
X (Twitter): no public ad library exists. Coverage is a best-effort workaround that scrapes a brand's tweets and flags items with non-empty card field or source containing "Ads" as likely promoted. Always include the caveat in synthesis output.

Note on overlap with `apify-ecommerce`

That skill has an ads-intelligence intent that routes to apify/facebook-ads-scraper for shallow Meta-ad lookups. This skill is the deep dive across all five platforms. If you only need Meta ads as a side detail of an ecommerce question, stay in apify-ecommerce. If ads are the main task, use this skill.

Prerequisites

(No need to check it upfront)

Apify CLI v1.5.0+ (npm install -g apify-cli)
jq (recommended for response parsing and filtering; brew install jq on macOS, apt install jq on Linux)
Authentication via one of:
- apify login (OAuth, opens browser)
- APIFY_TOKEN env variable (e.g. export APIFY_TOKEN=... or .env file)
- Token from Apify Console → Settings → Integrations

Verify auth: apify info --user-agent apify-awesome-skills/apify-ads-intelligence — should show username and userId.

Workflow

Copy this checklist and track progress:

Task Progress:
- [ ] Step 1: Detect intent and select Actor(s)
- [ ] Step 2: Fetch Actor schema
- [ ] Step 3: Ask user preferences (output format, result count, country)
- [ ] Step 4: Run the Actor (or Actors in parallel for cross-platform-audit) and fetch results
- [ ] Step 5: Synthesize a direct answer (not a data dump)

Step 1: Detect Intent and Select Actor

Classify the user's message into an intent, then pick the right Actor.

Intent signals:

Signals in user message	Intent
"what ads is X running", "competitor [brand] ads", "[brand] FB/Google/TikTok/LinkedIn/X/Twitter ads", "show ads from [page]", "promoted tweets from [brand]"	`competitor-ads`
"ads about [topic]", "find [keyword] ads", "ads for [vertical]", "fitness/fintech/saas ads"	`keyword-ads`
"trending ads", "winning ads", "top ads", "best performing", "long-running ads", "creative inspiration"	`top-creatives`
"where do these ads go", "landing pages from ads", "click destinations", "ad funnels"	`landing-page-audit`
"compare X's ads across platforms", "all ads from [brand]", "cross-platform ad audit"	`cross-platform-audit`

If multiple intents detected, ask: "Do you want [intent A] or [intent B]?"

Actor routing — always try Primary first, switch to Fallback only if it fails or returns 0 results:

Intent	Platform	Primary Actor	Fallback Actor
`competitor-ads`	Meta (FB/IG)	`apify/facebook-ads-scraper`	`brilliant_gum/facebook-ads-library-scraper`
`competitor-ads`	Google	`dz_omar/google-ads-scraper`	`solidcode/ads-transparency-scraper`
`competitor-ads`	TikTok	`brilliant_gum/tiktok-ads-library-scraper` (`source: library`)	`silva95gustavo/tiktok-ads-scraper`
`competitor-ads`	LinkedIn	`silva95gustavo/linkedin-ad-library-scraper`	`dz_omar/linkedin-ads-scraper`
`competitor-ads`	X (workaround)	`apidojo/twitter-scraper-lite` (`twitterHandles: [<brand>]`) + heuristic filter	`apidojo/tweet-scraper`
`keyword-ads`	Meta	`brilliant_gum/facebook-ads-library-scraper`	`apify/facebook-ads-scraper`
`keyword-ads`	Google	`apify/google-search-scraper` (`focusOnPaidAds: true`)	—
`keyword-ads`	TikTok	`brilliant_gum/tiktok-ads-library-scraper`	—
`keyword-ads`	LinkedIn	`silva95gustavo/linkedin-ad-library-scraper`	—
`keyword-ads`	X (workaround)	`apidojo/twitter-scraper-lite` (`searchTerms: [<keyword>]`) + heuristic filter	`apidojo/tweet-scraper`
`top-creatives`	Meta	`brilliant_gum/facebook-ads-library-scraper` (rank by `daysRunning`)	—
`top-creatives`	TikTok	`burbn/tiktok-top-ads-spy` (sort by CTR / impressions / likes)	`brilliant_gum/tiktok-ads-library-scraper` (`source: creative_center`)
`top-creatives`	Google	n/a — fall back to `competitor-ads` route, filter to active ads	—
`top-creatives`	LinkedIn	n/a — fall back to `competitor-ads` route, rank by `impressionsPerCountry` reach	—
`top-creatives`	X	n/a in v1 — no reliable promoted-content signal across timelines	—
`landing-page-audit`	Meta	`brilliant_gum/facebook-ads-library-scraper` (`resolveSnapshotUrls: true`)	—
`landing-page-audit`	Google	`apify/google-search-scraper` (`focusOnPaidAds: true`, `directUrl`)	`dz_omar/google-ads-scraper` (`destinationUrl`)
`landing-page-audit`	X	n/a in v1 — heuristics not reliable enough for landing-page extraction	—
`cross-platform-audit`	All five	Run Meta + Google + TikTok + LinkedIn primaries in parallel; X workaround runs separately with caveat. Merge by advertiser.	—

X (Twitter) heuristic filter — after scraping, flag a tweet as likely promoted if any of the following hold:

card field is non-empty (website cards / CTAs are commonly attached to promoted tweets)
source field contains "Ads" (e.g. "Twitter Ads")

Surface results with the explicit caveat: "X has no public ad library; results below are tweets from the brand's own timeline that match promoted-content heuristics. They will miss promoted-only ads that appear in other users' feeds."

Step 2: Fetch Actor Schema

Fetch the Actor summary, input schema, and README:

# Summary (title, description, pricing, stats)
apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-ads-intelligence --json 2>/dev/null

# Input schema (required and optional parameters; schema lives in
# .taggedBuilds.latest.build.inputSchema as an escaped JSON string)
apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-ads-intelligence --input --json 2>/dev/null

# README (capabilities, examples, gotchas)
apify actors info "ACTOR_ID" --user-agent apify-awesome-skills/apify-ads-intelligence --readme 2>/dev/null

Replace ACTOR_ID with the selected Actor (e.g., apify/facebook-ads-scraper).

Step 3: Ask User Preferences

Before running, ask:

Output format:
- Quick answer (default) — synthesized answer in chat, no file saved
- CSV — full export saved to disk
- JSON — full export saved to disk
Result count — defaults by intent:

Intent Default count
competitor-ads 30
keyword-ads 30
top-creatives 20
landing-page-audit 50
cross-platform-audit 15 per platform
Country — default US. For TikTok library specifically, default DE (EU-only) and warn the user; for global TikTok use source: creative_center. X routes are global by handle/keyword, no country parameter.

Intent	Default count
`competitor-ads`	30
`keyword-ads`	30
`top-creatives`	20
`landing-page-audit`	50
`cross-platform-audit`	15 per platform

Cost safety: Always set a sensible result limit in the Actor input (e.g., maxResults, resultsLimit, or the equivalent field per Actor schema). Warn the user before runs of 500+ ads — apify/facebook-ads-scraper charges per ad and X primaries charge per tweet.

Step 4: Run the Actor and Fetch Results

Two steps: run the Actor (blocks until done), then fetch dataset items in the requested format.

Run the Actor — returns run metadata as JSON; extract defaultDatasetId for the next step:

apify actors call "ACTOR_ID" -i 'JSON_INPUT' \
  --user-agent apify-awesome-skills/apify-ads-intelligence --json 2>/dev/null

From the output use .id (run ID), .status (should be SUCCEEDED), and .defaultDatasetId.

Fetch results — pick the variant based on the user's preference:

# Quick answer: total count + fields + top 5 in chat (no file)
apify datasets info DATASET_ID --json \
  --user-agent apify-awesome-skills/apify-ads-intelligence 2>/dev/null \
  | jq '{itemCount, fields, consoleUrl}'
apify datasets get-items DATASET_ID --limit 5 \
  --user-agent apify-awesome-skills/apify-ads-intelligence --format json 2>/dev/null

# CSV file
apify datasets get-items DATASET_ID \
  --user-agent apify-awesome-skills/apify-ads-intelligence --format csv 2>/dev/null > YYYY-MM-DD_filename.csv

# JSON file
apify datasets get-items DATASET_ID \
  --user-agent apify-awesome-skills/apify-ads-intelligence --format json 2>/dev/null > YYYY-MM-DD_filename.json

Other --format options: jsonl, xlsx, xml, rss, html. Use --offset N to paginate large datasets.

Tip: for anything more than a quick peek, save the dataset to a local file first (with > file.json / > file.csv) and run further analysis from disk. apify datasets get-items always streams over the network, so piping it straight into jq re-downloads the whole thing every iteration.

Cross-platform audit (parallel runs): For cross-platform-audit, kick off Meta + Google + TikTok + LinkedIn primaries in parallel by backgrounding each apify actors call ... invocation with & and calling wait before fetching results. Example:

apify actors call "apify/facebook-ads-scraper" -i '<META_INPUT>' \
  --user-agent apify-awesome-skills/apify-ads-intelligence --json 2>/dev/null > meta_run.json &
apify actors call "dz_omar/google-ads-scraper" -i '<GOOGLE_INPUT>' \
  --user-agent apify-awesome-skills/apify-ads-intelligence --json 2>/dev/null > google_run.json &
apify actors call "brilliant_gum/tiktok-ads-library-scraper" -i '<TIKTOK_INPUT>' \
  --user-agent apify-awesome-skills/apify-ads-intelligence --json 2>/dev/null > tiktok_run.json &
apify actors call "silva95gustavo/linkedin-ad-library-scraper" -i '<LINKEDIN_INPUT>' \
  --user-agent apify-awesome-skills/apify-ads-intelligence --json 2>/dev/null > linkedin_run.json &
wait
# Then extract each .defaultDatasetId and fetch items per platform; X workaround runs separately with caveat.

Combining with jq for quick extraction:

Treat jq as a complement to apify datasets get-items, not a replacement: server-side --limit / --offset / --format keeps cost and bandwidth down. Use jq on a sample item or on a file you already saved.

# Discover real field names from one sample item (Actor outputs vary —
# use this before composing further jq queries)
apify datasets get-items DATASET_ID --limit 1 --format json \
  --user-agent apify-awesome-skills/apify-ads-intelligence 2>/dev/null \
  | jq '.[0]'

# X heuristic filter on a saved tweets file: keep items with non-empty card
# or source containing "Ads"
jq '[.[] | select((.card != null and .card != "") or (.source != null and (.source | contains("Ads"))))]' \
  YYYY-MM-DD_x_tweets.json

Step 5: Analyze Results and Deliver Answer

Synthesize, don't dump. Patterns by intent:

Intent	What the synthesis surfaces
`competitor-ads`	Total ads found, active vs inactive split, top creative formats, top 5 ad copy snippets, list of unique landing-page domains. For X specifically: total tweets scraped, count flagged as likely-promoted, top 5 flagged tweets with the heuristic-detection caveat.
`keyword-ads`	Top 5 advertisers running ads on this keyword, total ads, country split
`top-creatives`	Top 5 by `daysRunning` (Meta) or CTR (TikTok), with creative summary, link to Ad Library entry
`landing-page-audit`	List of unique landing URLs, grouped by domain, with ad counts pointing at each
`cross-platform-audit`	Per-platform ad count and tone summary, then a "where they're spending most" inference

Suggested follow-ups — keyed off the intent that just ran:

If user just ran…	Suggest next
`competitor-ads` (Meta)	Stack with `apify-competitor-intelligence` to add their FB Page posts, IG profile, and Google Maps reviews
`landing-page-audit` (any)	Stack with `apify-ecommerce` (`tech-stack` intent) to detect the platform behind the landing pages, or with `apify-lead-generation` to enrich destination domains with contact info
`top-creatives` (TikTok / Meta)	Stack with `apify-influencer-discovery` if any creatives are influencer collabs
`keyword-ads` (Google / Meta)	Stack with `apify-trend-analysis` to see whether the keyword is rising or falling on Google Trends / Instagram / TikTok
`cross-platform-audit`	Stack with `apify-content-analytics` for the brand's organic content side; combined paid + organic picture

Quirks

TikTok keyword search is loose. Searching "Nike" can return ads from unrelated advertisers (Interactive Brokers, Shopify in our test). Always post-filter by advertiserName matching the user's intended brand; warn the user if zero matches after filter.
TikTok Ads Library is EU/EEA/UK only. The library source needs an EU country code (DE / FR / IT / ES / NL / PL / SE etc.). For US/global coverage, switch to creative_center source — different fields (CTR, impression ranges, no targeting data).
dz_omar/google-ads-scraper requires resultsPerQuery >= 10. Smaller values fail validation. Always set 10+ even for small intents.
apify/facebook-ads-scraper takes URLs, not keywords. For competitor-ads: build https://www.facebook.com/<PageName> from the brand name. For keyword-ads: build a Meta Ad Library URL with q=<keyword>&country=<XX>.
apify/google-search-scraper paid-ads mode has a built-in retry (up to 3) when no paid results are found — sometimes a query genuinely has no paid results. Treat empty paidResults as a valid answer, not an error.
LinkedIn Ad Library URL construction: company URL https://www.linkedin.com/company/<slug>/ is allowed but slow and ignores filters. For competitor-ads use https://www.linkedin.com/ad-library/search?accountOwner=<slug>&countries=<XX>. For keyword-ads use ?keyword=<term>&countries=<XX>.
X has no public ad library. Coverage is heuristic only. The route uses apidojo/twitter-scraper-lite to scrape a brand's own tweets (or keyword search results), then flags items with non-empty card field or source containing "Ads" as likely promoted. This will miss promoted-only tweets that never appear in the brand's own timeline.
X session sensitivity. If the primary X Actor returns only noResults sentinels, switch to the fallback before declaring zero results.
Pricing. Most primaries are FREE in our pricing tier; apify/facebook-ads-scraper charges per ad ($0.001 - $0.0058); X primaries charge per tweet (~$0.0004 / 1k). Default counts (30 / 20 / 50) keep cost negligible. Warn before runs of 500+ ads.

Error Handling

Auth error → run apify login, or set APIFY_TOKEN env var
Actor not found → check Actor ID against the routing table
Run status FAILED → open the console URL (.consoleUrl from run metadata) for logs
Timeout / very long run → pass --timeout <seconds> to apify actors call, or reduce result count
0 results → switch to the Fallback Actor; if still 0, try a different country code
TikTok library: no EU country supplied → default to DE and warn the user
dz_omar/google-ads-scraper: validation error on resultsPerQuery → bump to 10+
X scraper: only noResults sentinels → switch to the fallback X Actor
proxy is required error → add "proxy": {"useApifyProxy": true} to the input

Account or credentials required

1 FOR YOU

01
Create an Apify account and get an API token
Sign up at apify.com and authenticate via apify login or by setting APIFY_TOKEN from Apify Console → Settings → Integrations.
```
apify login
```

This skill ships inside the Apify Community Skills plugin.

Run this outside your AI tool, using Node.js 22.20.0 or newer. Skills CLI 1.7.2 skips skill selection; prompts cancel installation without an interactive terminal. If choosing Project when asked about installation scope, open your project's folder before running this.

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npx skills add apify/awesome-skills --skill "apify-ads-intelligence" --full-depth

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