acquire-codebase-knowl…
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like…
Use this skill when the user asks to analyze, tear down, or reverse-engineer a competitor''s paid ads. Trigger for prompts like "what ads is [competitor] running", "tear down their ad strategy", "competitor ad analysis", "find ad angles we haven''t tried", or "reverse-engineer
Claude Code
$ npx -y skills add github/awesome-copilot --skill competitor-ad-intelligence --agent claude-code
Codex
$ npx skills add github/awesome-copilot --skill competitor-ad-intelligence -a codex -y
Cursor
$ npx skills add github/awesome-copilot --skill competitor-ad-intelligence -a cursor -y
Gemini CLI
$ npx skills add github/awesome-copilot --skill competitor-ad-intelligence -a gemini-cli -y
GitHub Copilot
$ npx skills add github/awesome-copilot --skill competitor-ad-intelligence -a github-copilot -y
OpenCode
$ npx skills add github/awesome-copilot --skill competitor-ad-intelligence -a opencode -y
Windsurf
$ npx skills add github/awesome-copilot --skill competitor-ad-intelligence -a windsurf -y
name: competitor-ad-intelligence description: 'Use this skill when the user asks to analyze, tear down, or reverse-engineer a competitor''s paid ads. Trigger for prompts like "what ads is [competitor] running", "tear down their ad strategy", "competitor ad analysis", "find ad angles we haven''t tried", or "reverse-engineer their paid funnel". Do not trigger for organic/SEO competitor research or website positioning analysis.' license: MIT compatibility: 'Cross-platform. Uses web search and public ad libraries (Meta Ad Library, Google Ads Transparency Center) only — no API keys or credentials required.' metadata: version: "1.0" author: GooseWorks source: https://github.com/gooseworks-ai/goose-skills
Scrape competitor ads from Meta and Google, analyze creative patterns, reverse-engineer landing page funnels, and produce a full strategic teardown — hooks, formats, positioning bets, vulnerabilities, and counter-plays.
**Core principle:** A competitor's ad portfolio is a window into their growth strategy. Long-running ads reveal what converts. New ads reveal what they're testing. Landing pages reveal their positioning bets. The best ad creative teams start with evidence from what's already working, then differentiate.
Gather from the user:
1. **Competitor names + domains** (e.g., `apollo.io`, `clay.run`) 2. **Your product/domain** — for comparison framing 3. **Channels:** Meta only, Google only, or both? (default: both) 4. **Depth level:**
5. **Product category** — helps frame analysis 6. **Known competitor landing pages?** — any URLs already spotted in their ads
For each competitor domain, scrape ads from Meta Ad Library.
Use `web_search` to find competitor ads in the Meta Ad Library (publicly accessible, no API key needed):
web_search: site:facebook.com/ads/library "[competitor_name]" web_search: "[competitor_name]" Meta Ad Library active ads web_search: "[competitor_name]" facebook ads examples
You can also visit the Meta Ad Library directly: `https://www.facebook.com/ads/library/?active_status=active&ad_type=all&country=US&q=<competitor_name>`
Use `fetch_webpage` on the Ad Library URL to extract ad details if your agent supports it.
> **Note:** Apify actors for Meta Ad Library scraping exist but are unreliable as of April 2026 due to Meta's anti-scraping measures. Use `web_search` as the primary method.
**Collect per ad:**
For each competitor domain, scrape ads from Google Ads Transparency Center.
Use `web_search` to find competitor ads in Google Ads Transparency Center (publicly accessible):
web_search: site:adstransparency.google.com "[competitor_name]" web_search: "[competitor_name]" Google Ads transparency web_search: "[competitor_name]" google search ads examples
You can also visit directly: `https://adstransparency.google.com/?search_text=<competitor_name>`
Use `fetch_webpage` on the Transparency Center URL to extract ad details if your agent supports it.
**Collect per ad:**
After collecting all ads, perform structured analysis.
Group all ad headlines/openers by hook type:
| Hook Type | Pattern | Example | |-----------|---------|---------| | **Fear/Loss** | Risk of missing out or falling behind | "Your competitors are already using AI SDRs" | | **Outcome** | Direct result promise | "10x your pipeline in 30 days" | | **Question** | Challenges current assumption | "Still doing outbound manually?" | | **Social proof** | Names customers or numbers | "Join 500+ B2B teams using [product]" | | **Contrarian** | Challenges conventional wisdom | "Cold email isn't dead. Your copy is." | | **Empathy** | Validates their pain | "We know SDR ramp time is brutal" | | **Product-led** | Feature as hook | "[Feature] is live — see what's new" |
Count how many ads per competitor use each hook type. This reveals their primary messaging strategy.
| Format | Meta | Google | |--------|------|--------| | Static image | [N] | N/A | | Video | [N] | [N] | | Carousel | [N] | N/A | | Search text | N/A | [N] | | Display banner | N/A | [N] |
List all unique CTAs found. Common patterns:
For each unique landing page URL found in ads, fetch and analyze:
fetch_webpage: [landing_page_url]
Or use `curl` if `fetch_webpage` is unavailable.
**Extract per landing page:**
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