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.
npx skills add manu14357/zskills --skill competitive-ads-extractorgit clone --depth 1 https://github.com/manu14357/zskillsWrote 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/skills/manu14357/zskills/competitive-ads-extractor)<a href="https://agentmods.dev/skills/manu14357/zskills/competitive-ads-extractor"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/competitive-ads-extractor/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/skills/manu14357/zskills/competitive-ads-extractor"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/competitive-ads-extractor.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 | $0.00039 | $0.02495 |
| Opus 5 | $0.00019 | $0.01247 |
| Sonnet 5 | $0.00008 | $0.00499 |
| Haiku 4.5 | $0.00004 | $0.00249 |
Grade A, and why
competitive-ads-extractor 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 8d 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 — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Ads Extractor
Use This Skill When
- Researching competitor ad campaigns and messaging
- Finding inspiration for your own ad creative
- Understanding market positioning and pain points
- Analyzing successful ad patterns and copy approaches
- Discovering new use cases or customer problems
- Planning campaigns with proven messaging concepts
- Benchmarking ad performance across competitors
- Understanding audience targeting strategies
When NOT to Use
- Copying competitor ads (research, don't replicate)
- When competitors have private or non-library ads only
- For platforms without public ad libraries
- When you need to analyze offline/traditional media
- For real-time ad performance data (not available in libraries)
Context: Competitive Intelligence
Undeveloped: Manual ad browsing, no systematic analysis, missed patterns.
Target: Extracted ads with analyzed messaging, identified pain points, recognized successful patterns.
Optimized: Comprehensive competitive map, sentiment analysis, trend forecasting, prediction of what works next.
Core Principle
Winning ads solve real problems and use proven messaging patterns. Extract competitor ads systematically to identify which pain points resonate and which creative approaches drive action.
Instructions
Step 1: Identify Competitors and Platforms
Define Competitive Set:
| Competitor | Primary Platform | Ad Frequency | Target Audience |
|---|---|---|---|
| Company A | 15+ ads | Startups | |
| Company B | 8+ ads | Enterprise | |
| Company C | Facebook + LinkedIn | 20+ ads | Mid-market |
Platform Access:
- Facebook Ad Library: facebook.com/ads/library (public, most comprehensive)
- LinkedIn Ads: linkedin.com/ads/library (limited, enterprise focus)
- Google Ads Transparency: ads.google.com/transparency (political/election ads)
- Platform-specific: TikTok, Instagram, YouTube also have ad libraries
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.
- 8d ago First seen · 338 lines · 39 tokens per session scan A 0607440d5ac7
competitive-ads-extractor is a skill published in the GitHub repository manu14357/zskills (16 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 2,495 once invoked, about $0.0002 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 2026-09-03.
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