# Competitor Ad Intelligence > Scrape competitor ads from the Meta Ad Library and Google Ads Transparency Center, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing-page funnels, and produce a strategic teardown — positioning bets, vulnerabilities, creative white-space, and counter-plays. For paid/creative teams sizing up the competitive ad landscape before they launch or refresh. - Skill: `robomotionio/competitor-ad-intelligence` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add robomotionio/competitor-ad-intelligence` - Raw SKILL.md: https://api.skillmd.com/api/skills/robomotionio/competitor-ad-intelligence/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: robomotionio (https://skillmd.com/u/robomotionio) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/robomotionio/competitor-ad-intelligence --- # Competitor Ad Intelligence Composite teardown: **Meta scrape + Google scrape → creative-pattern analysis → LP/funnel reconstruction → strategic gap/vulnerability analysis → report**. Scripts do the deterministic scraping/parsing; **you (the agent) do all clustering, scoring, funnel inference, and the teardown narrative.** ## When to use - "What ads are my competitors running?" / "Tear down ``'s ad strategy." - "Find new creative angles" / "Reverse-engineer ``'s paid funnel." - "What hooks/formats are dominant in our space?" / "Find weaknesses in their ad strategy." ## Sub-skills it chains - **`google-ad-scraper`** capability (Google Transparency Center) — call its script by path. - Bundled `scrape_meta_ads.mjs` (Meta Ad Library) and `fetch_landing_page.py` (LP extract). ## How to run One-time browser setup: ```bash cd ${SKILL_DIR}/scripts && npm install && npx playwright install chromium ``` ### 1 — Scrape ads per competitor (channels: meta / google / both) ```bash # Meta Ad Library (this skill's bundled scraper) node ${SKILL_DIR}/scripts/scrape_meta_ads.mjs --query "Notion" --country US --max-ads 60 --output ${WORKSPACE}/meta_notion.json # Google Ads Transparency Center (reuse the google-ad-scraper capability) node ${SKILL_DIR}/../google-ad-scraper/scripts/scrape_google_ads.mjs --domain notion.so --max-ads 50 --country US --output ${WORKSPACE}/google_notion.json ``` Captured per Meta ad: `libraryId, visualType, adText, cta, landingUrl, startDate, platforms, daysRunning`. **`daysRunning` (first-seen → still-running) is the key "what's working" signal — longer runs weight higher in the teardown.** If a library blocks the scraper: **if `APIFY_API_TOKEN` is set → use the Apify ad-library actor (or the Apify fallback in `google-ad-scraper`) for the blocked library; if not → degrade to a `site:facebook.com/ads/library ""` / `site:adstransparency.google.com ""` web search (default; less structured, lower coverage — note it).** That degrade search itself can use the optional SERP upgrade: **if `DATAFORSEO_LOGIN`/`DATAFORSEO_PASSWORD` (or `SERPER_API_KEY`) is set → structured SERP API; else → the agent's keyless web search.** ### 2 — Cluster hooks, tabulate formats & CTAs (you, the agent) Read the scraped JSON. Classify each ad's hook into `Fear / Outcome / Question / Social-proof / Contrarian / Empathy / Product-led`; build hook-distribution, format-distribution, and CTA-taxonomy tables per competitor. Surface the longest-running ads. ### 3 — Fetch each unique landing page ```bash # Collect unique landingUrl values into urls.txt, then: python3 ${SKILL_DIR}/scripts/fetch_landing_page.py --urls ${WORKSPACE}/urls.txt --output ${WORKSPACE}/lps.json ``` Returns hero/subhead/primary-CTA/proof/form-field-count/page stats per LP. For JS-rendered or gated LPs, escalate that URL to a Playwright fetch (reuse `scrape_meta_ads.mjs`'s browser pattern) or capture a screenshot so visual continuity reflects what a clicker sees. ### 4 — Cluster into campaigns, infer funnels & budget, write the teardown (you) Cluster ads into inferred campaigns by **LP destination + theme**. Per campaign infer intent, persona, positioning bet, hook, conversion path, longevity, and A/B signals; infer platform budget allocation from ad volume × platform. Then do the strategic pass: creative white-space, overcrowded angles, format gaps, and vulnerabilities. In **deep** mode, add historical positioning change by fetching `web.archive.org` snapshots of competitor LPs (`fetch_landing_page.py --url "https://web.archive.org/web//"`) and propose counter-plays with headline/body/LP strategy. ### 5 — Render Write `competitor-ad-intel-.md` to `${WORKSPACE}` and attach it to the Agent Teams channel. Optionally persist scraped ads (dedup by `libraryId`/`creativeId` + LP) so monitoring across runs shows true new vs. retired campaigns. ## Outputs `competitor-ad-intel-.md` — coverage summary, Meta + Google ad analysis (hook distribution, longest-running ads, CTA taxonomy, format distribution), per-campaign funnel map, budget-allocation inference, creative gap analysis, vulnerability report, and (deep mode) counter-plays. ## Credentials / env - **Required:** none for the scripts — both ad libraries are public; Playwright + a proxy carry the scrape. Hook clustering, funnel inference, and the teardown are the agent's reasoning (no LLM key needed in scripts). - **Optional (each with a keyless default fallback):** - `APIFY_API_TOKEN` — if set → Apify ad-library actor when a library page is too anti-bot; else → the keyless Playwright scrapers, degrading to `site:` web search (default). - `SUPABASE_URL`/`SUPABASE_KEY` — if set → persist ad history for cross-run new-vs-retired monitoring; else → single-run workspace JSON (default). - `DATAFORSEO_LOGIN`/`DATAFORSEO_PASSWORD` (or `SERPER_API_KEY`) — if set → structured SERP API for the `site:` degrade/archive searches; else → the agent's keyless web search (default). - `HTTPS_PROXY` — Robomotion Proxy for the Playwright scrapers. ## Notes & edge cases - Meta Ad Library and Google Transparency Center are heavy JS SPAs with active anti- scraping — randomize timing, rotate the proxy, set the library `country` to your target market. If blocked, degrade to `site:` snippets and note lower coverage. - Apify Meta-Ad-Library actors are unreliable under Meta's countermeasures — last resort. - Longevity is the load-bearing signal; capture `daysRunning` per ad. - Dedup ads by library/creative id + LP across runs so monitoring is accurate.