Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/thatrebeccarae/claude-marketingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/thatrebeccarae/claude-marketing/competitor-ads-analyst)<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/competitor-ads-analyst"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/competitor-ads-analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/thatrebeccarae/claude-marketing/competitor-ads-analyst"><img src="https://agentmods.dev/badge/rules/thatrebeccarae/claude-marketing/competitor-ads-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.01947 |
| Opus 5 | $0.00026 | $0.00974 |
| Sonnet 5 | $0.00011 | $0.00389 |
| Haiku 4.5 | $0.00005 | $0.00195 |
Grade A, and why
competitor-ads-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 248 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Ads Analyst
Analyze competitor advertising from public ad libraries to understand what messaging, creative formats, and positioning strategies are working.
Core Capabilities
- Extract ads from public ad libraries (Facebook, Google, TikTok, LinkedIn)
- Categorize ad creative by format, message type, and funnel stage
- Identify pain points competitors highlight (with frequency scoring)
- Map competitor positioning and find white space
- Extract headline and copy formulas
- Track creative format trends (UGC, before/after, testimonial, etc.)
- Generate competitive intelligence reports and swipe files
Ad Library Access
| Platform | URL | Access |
|---|---|---|
| Meta (Facebook/Instagram) | facebook.com/ads/library | Free, public |
| Google Ads | adstransparency.google.com | Free, public |
| TikTok | library.tiktok.com | Free, public |
| linkedin.com/ad-library | Requires account |
Workflow
1. Define Scope
- Competitors: 3-5 companies to analyze
- Timeframe: Last 30, 60, or 90 days
- Platforms: Which ad libraries to search
- Focus: Messaging, creative format, audience targeting, or all
2. Extract Ads
For each competitor, collect:
- Ad copy (headline, primary text, CTA)
- Creative format (image, video, carousel, UGC)
- Landing page URL
- Active date range (if visible)
- Platform and placement
3. Categorize
Classify each ad by:
Message Type:
- Pain point (problem-aware)
- Solution (product-aware)
- Social proof (testimonial, case study, press)
- Offer (discount, free trial, demo)
- Educational (how-to, tip, guide)
- Brand (awareness, positioning)
Funnel Stage:
- Top (awareness, problem education)
- Middle (consideration, comparison)
- Bottom (conversion, offer, urgency)
Creative Format:
- Static image
- Video (short <15s, medium 15-60s, long >60s)
- Carousel
- UGC-style
- Before/after
- Screenshot/product demo
- Testimonial quote card
- Data/stat visualization
4. Analyze Patterns
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 248 lines · 53 tokens per session scan A df4c9287481e
competitor-ads-analyst is a cursor rule published in the GitHub repository thatrebeccarae/claude-marketing (133 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 1,947 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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