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Researches and analyzes ads across Meta, Google, TikTok, LinkedIn, and X using Apify Actors for competitor ad intelligence and creative analysis.
How this skill is triggered — by the user, by Claude, or both
Slash command
/awesome-skills:apify-ads-intelligenceThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Answer natural language questions about ads, ad libraries, and competitor advertising activity by routing to the right Apify Actor and delivering a synthesized answer.
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.
card field or source containing "Ads" as likely promoted. Always include the caveat in synthesis output.apify-ecommerceThat 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.
(No need to check it upfront)
npm install -g apify-cli)jq (recommended for response parsing and filtering; brew install jq on macOS, apt install jq on Linux)apify login (OAuth, opens browser)APIFY_TOKEN env variable (e.g. export APIFY_TOKEN=... or .env file)Verify auth: apify info --user-agent apify-awesome-skills/apify-ads-intelligence — should show username and userId.
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)
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 | 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 | 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 | apify/google-search-scraper (focusOnPaidAds: true) | — | |
keyword-ads | TikTok | brilliant_gum/tiktok-ads-library-scraper | — |
keyword-ads | 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 | n/a — fall back to competitor-ads route, filter to active ads | — | |
top-creatives | 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 | 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."
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).
Before running, ask:
Output format:
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.
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.
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
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 |
advertiserName matching the user's intended brand; warn the user if zero matches after filter.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.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>.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.noResults sentinels, switch to the fallback before declaring zero results.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.apify login, or set APIFY_TOKEN env varActor not found → check Actor ID against the routing tableFAILED → open the console URL (.consoleUrl from run metadata) for logs--timeout <seconds> to apify actors call, or reduce result countDE and warn the userdz_omar/google-ads-scraper: validation error on resultsPerQuery → bump to 10+noResults sentinels → switch to the fallback X Actorproxy is required error → add "proxy": {"useApifyProxy": true} to the inputnpx claudepluginhub apify/awesome-skills --plugin apify-influencer-brand-collabsAnalyzes competitor ad copy, creative strategy, keyword targeting, and estimated spend across Google, Meta, LinkedIn, TikTok, Microsoft, and Apple Ads. Identifies competitive gaps and opportunities.
Extracts and analyzes competitors' ads from Facebook and LinkedIn ad libraries to identify successful messaging, problems, and creative patterns. Helps inspire ad campaigns.
Analyzes competitor ads across Google, Meta, LinkedIn, TikTok, and Microsoft using ad libraries and auction insights. Evaluates copy, creatives, keywords, spend, and identifies gaps/opportunities.