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 matteotitta/genesys-skills --skill linkedin-ad-teardowngit clone --depth 1 https://github.com/matteotitta/genesys-skillsWrote 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/matteotitta/genesys-skills/linkedin-ad-teardown)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/linkedin-ad-teardown"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-ad-teardown/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/matteotitta/genesys-skills/linkedin-ad-teardown"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/linkedin-ad-teardown.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 | $0.00188 | $0.01499 |
| Opus 5 | $0.00094 | $0.00749 |
| Sonnet 5 | $0.00038 | $0.00300 |
| Haiku 4.5 | $0.00019 | $0.00150 |
Grade A, and why
linkedin-ad-teardown 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Ad Teardown — competitor paid-creative strategy read
LinkedIn's Ad Library is public (it exists for ad transparency). This skill reads a competitor's ads from it and turns them into a strategy read: what themes and offers they're pushing, in what formats, how often, who they're targeting (from the EU "who's shown this" disclosure), and — the part that matters most — where the gaps are that the client can move into.
Adapted from github.com/stan-default/liam's liam-competitors / inspect_competitor_ads (MIT), accessed 2026-07-14, via /steal — see .claude/discovery/0726-liam-steal-analysis.md. Concept port; the sourcing runs on our scraper stack, not Liam's Playwright engine.
Doctrine inherited
quantitative-evidence-floors.md— no strategy read below the floor. State how many ads the read rests on.crawl-cost-discipline.md— free discovery first (the public Ad Library URLs), metered extraction only on the ads you keep.apify-credits.md— if an Apify actor is used for bulk, gate it and estimate the cost first.storage-policy.md+pii-redaction.md— the Ad Library is public transparency data (no member PII); route the teardown to{client}/competitors/, don't over-collect.
Sourcing — public, gated, cheapest-first
The library lives at https://www.linkedin.com/ad-library/ — searchable by advertiser, no login for the basic view. Source in this order:
- Browser MCP (free). Navigate the public Ad Library, search the advertiser, read the results. The cheapest path — try it first.
- Firecrawl scrape (metered). When the page is too JS-heavy to read cleanly via the browser.
- Apify LinkedIn Ad-Library actor (metered, gated). Only for bulk across many competitors, and only after the
apify-credits.mdestimate + go-ahead.
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 · 111 lines · 188 tokens per session scan A 1533b562c110
linkedin-ad-teardown is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 188 tokens to every session and 1,499 once invoked, about $0.0009 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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