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 itallstartedwithaidea/agent-skills --skill audience-targetinggit clone --depth 1 https://github.com/itallstartedwithaidea/agent-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/itallstartedwithaidea/agent-skills/audience-targeting)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/audience-targeting"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/audience-targeting/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/itallstartedwithaidea/agent-skills/audience-targeting"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/audience-targeting.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 | $0.00031 | $0.02576 |
| Opus 5.5 | $0.00012 | $0.01030 |
| Sonnet 5.5 | $0.00006 | $0.00515 |
| Haiku 4.5 | $0.00003 | $0.00258 |
Grade A, and why
audience-targeting 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 1mo 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience Targeting
Part of Agent Skills™ by googleadsagent.ai™
Description
The Audience Targeting skill orchestrates Google Ads' full audience ecosystem to reach the right users at every stage of the conversion funnel. From broad prospecting with in-market and custom intent audiences to surgical remarketing with customer match and dynamic lists, this skill builds layered audience strategies that maximize reach efficiency while maintaining conversion quality.
Google Ads offers an increasingly complex audience taxonomy: in-market audiences (users actively researching products), custom intent audiences (defined by keywords and URLs), custom affinity audiences (defined by interests and behaviors), similar/lookalike audiences, customer match (first-party data uploads), remarketing lists (website visitors, app users, YouTube viewers), and combined audiences (boolean logic across segments). The skill navigates this complexity by mapping each audience type to its optimal funnel position and campaign objective.
For Performance Max campaigns, audience signals are particularly critical. While PMax uses automated targeting, the quality of audience signals dramatically influences where Google's algorithms focus initial exploration. This skill builds optimized audience signal packages combining first-party data, custom segments, and Google audiences to accelerate PMax learning phases and improve signal quality throughout the campaign lifecycle.
Use When
- User asks about "audience targeting" or "audience strategy"
- User mentions "remarketing lists" or "retargeting setup"
- User wants to create "custom audiences" or "custom intent audiences"
- User asks about "in-market audiences" or "affinity audiences"
- User mentions "customer match" or "first-party data upload"
- User asks about "audience signals" for Performance Max
- User wants to "build lookalike audiences" or "similar audiences"
- User mentions "audience segmentation" or "audience layering"
- User asks to "expand reach" while maintaining conversion quality
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.
- 1mo ago First seen · 294 lines · 31 tokens per session scan A f6e95673466d
audience-targeting is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 31 tokens to every session and 2,576 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
tracking-health
Preventive audit of conversion tracking across all configured ad platforms — Meta pixels + CAPI, Google Ads conversion actions, and final-URL tracking-parameter consistency on every platform — with a GA4 cross-check. Use when the user asks to check tracking, audit conversion measurement, verify pixels / tags, check…
learn
Save a marketing diagnosis insight to the pro-diagnosis knowledge base so it is applied in future operations across all platforms. Use when the user runs /learn, explicitly teaches the agent a marketing insight, corrects the agent's analysis, or asks to remember/record an operational learning for next time. Also use…
adspirer-amazon-ads
Create and manage Amazon Ads through Adspirer — Sponsored Products, Sponsored Brands, and Sponsored Display, with ASIN targeting, search-term reports, ACoS, and bid optimization. Use for anything on Amazon Advertising or Seller/Vendor Central ads.
weekly-report
A skill that creates a weekly marketing-operations summary across connected platforms. It reviews logged actions and strategy information to produce a recap.
review-stack
Stack review of one or more third-party tools (OXC, TanStack, Vite, cf, Sentry, pnpm, Claude Code...) against current official best practices, or of what a dependency bump brings (bumped): one parallel subagent per tool, verified findings, one consolidated plan in chat. USE WHEN: user runs /review-stack or explicitly…
sealeap-amazon-product-targeting
An Amazon Ads planning skill for targeting product pages and categories, including competitor, complementary, upsell, and own-product traffic.