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 dalroot/agentic-seo --skill paid-adsgit clone --depth 1 https://github.com/dalroot/agentic-seoWrote 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/dalroot/agentic-seo/paid-ads)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/paid-ads"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/paid-ads/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/dalroot/agentic-seo/paid-ads"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/paid-ads.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 | $0.00173 | $0.03181 |
| Opus 5.5 | $0.00069 | $0.01272 |
| Sonnet 5.5 | $0.00035 | $0.00636 |
| Haiku 4.5 | $0.00017 | $0.00318 |
Grade A, and why
paid-ads-strategy 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 today.
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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategies: Paid Ads
Guides paid ads strategy: when to use paid acquisition, channel selection, budget allocation, ad-to-landing-page alignment, and cross-platform best practices. Paid ads (Google Ads, Meta, LinkedIn, Reddit, TikTok, etc.) deliver immediate reach and targeting; use when PMF is validated and budget allows.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Platform-specific execution: Web: google-ads, meta-ads, linkedin-ads, reddit-ads, tiktok-ads. App: app-ads. TV/Streaming: ctv-ads.
Before Starting
Check for project context first: If .agents/project-context.md or .claude/project-context.md exists, read it before asking questions.
Gather this context (ask if not provided):
| Area | Questions |
|---|---|
| Goals | Primary objective? (Awareness, traffic, leads, sales, app installs) Target CPA/ROAS? Monthly budget? Constraints? |
| Product & offer | What are you promoting? Landing page URL? What makes it compelling? |
| Audience | Ideal customer? Problem you solve? What do they search for or care about? Existing customer data for lookalikes? |
| Current state | Run ads before? What worked/didn't? Pixel/conversion data? Funnel conversion rate? |
Two Modes: PMF Testing vs Conversion-Driven
| Mode | When | Goal | Budget | Metrics |
|---|---|---|---|---|
| PMF testing | Pre-PMF; idea validation | Validate demand, messaging, pricing, audience before building | $47–500; small | CTR, sign-up rate, bounce rate; low CTR/high bounce = messaging issue |
| Conversion-driven | PMF validated | Commercialization; scale; efficient acquisition | Scale; ROAS target | ROAS, CAC, conversion rate |
PMF testing: Use paid ads as a learning tool—simple landing page, "Join Waitlist" or "Get Early Access" CTA, test ad variations (value props, price points, audiences). No full product needed. See google-ads for PMF testing setup.
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.
- today First seen · 212 lines · 173 tokens per session scan A 96b935ec5ac5
paid-ads-strategy is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 173 tokens to every session and 3,181 once invoked, about $0.0007 per session on Opus 5.5. 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 2026-10-26.
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