aaron-marketing-skills is a collection of 120 AI-agent skills covering marketing work such as brand narrative, search optimization, social media, email, advertising, influencer campaigns, and launches. Marketers and agent users can install it as a plugin, use its portable skills, or run its described bot team. The catalogue entries are components of this marketing workflow.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skillsnpx agentmods add skills/aaron-he-zhu/aaron-marketing-skills/audience-segment-builderWrote 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/aaron-he-zhu/aaron-marketing-skills/audience-segment-builder)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/audience-segment-builder"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/audience-segment-builder/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/aaron-he-zhu/aaron-marketing-skills/audience-segment-builder"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/audience-segment-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00150 | $0.02063 |
| Opus 5 | $0.00075 | $0.01032 |
| Sonnet 5 | $0.00030 | $0.00413 |
| Haiku 4.5 | $0.00015 | $0.00206 |
Grade A, and why
audience-segment-builder 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 10d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience Segment Builder
Turns the user's own customer/CRM/GA4 export into seed audiences, value-based lookalike SEED lists, exclusion/suppression segments, and a cross-platform funnel-stage targeting map. It defines who the audiences are and how they are seeded and suppressed — campaign-architect then consumes these segments into account structure and match types; this skill does not build campaigns, and it is distinct from organic keyword-research, which reads SERP intent rather than paid segments.
Quick Start
Build audience segments from my customer export: [path]. Goal is DR. Platforms: Google + Meta.
Make a value-based lookalike SEED list from my top customers and the exclusion list for people who already bought. [customer CSV]
Map my GA4 audiences to funnel stages so I can reuse the same targeting across Google and Meta. [GA4 audience/demographics export]
Skill Contract
Expected output: a set of named audiences in four buckets — (1) seed audiences grouped by trait/behavior, (2) value-based lookalike SEED lists (the high-value seed rows themselves, not a platform key), (3) exclusion/suppression segments (existing customers, recent purchasers, bad-fit), and (4) a funnel-stage targeting map reusable across platforms — with notes that inform the ROAS A (Audience) dimension, plus the standard handoff summary.
- Reads: the user's own customer/CRM CSV (traits, value/LTV, last-purchase date, fit signals) and GA4 audience/demographics export; the ROAS profile (
direct-response|prospecting|incremental-profit); target platforms. - Writes: a user-facing segment plan and reusable summary to
memory/ad/audience-segment-builder/. - Promotes: the seed/lookalike-seed/exclusion bucket names, the funnel-stage map, the suppression rules, and any missing export to
memory/hot-cache.mdandmemory/open-loops.md; propose durable segment definitions as pending-decision items. - Done when: each audience is named and grounded in an exported column; value-based seeds are ranked by the user's own value field; exclusion segments cover existing customers and recent purchasers (window stated); the funnel-stage map is platform-neutral; and the ROAS A relevance of each bucket is noted (or flagged NEEDS_INPUT).
- Primary next skill: campaign-architect to consume these segments into account structure and match types.
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.
- 10d ago Changed 2aa454645d50
- 13d ago First seen · 81 lines · 150 tokens per session scan A 919ae856497c
audience-segment-builder is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed yesterday), licensed Apache-2.0. It adds 150 tokens to every session and 2,063 once invoked, about $0.0007 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.
Other skills, from other repositories
geo-visibility-check
One-shot GEO audit: does your brand appear in Claude, ChatGPT, and Gemini answers for the buyer questions that matter? Runs a prompt panel through the engines with citation tracing and reports per-prompt verdicts, who wins instead, and which sources the answers come from.
geo-optimizer-skill
Run geo audit first. It scores the site 0–100 across 8 categories and generates a prioritized action list.
geo-loop
Run one bounded eGEOagents loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, append one Timeline entry and one LOG line. Use for loop mode, /geo:loop, scheduled GEO runs, or continuous monitoring.
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Score content against the 10 GEO criteria with evidence and prioritized fixes. Use when users ask to score, rate, evaluate, or estimate ranking strength.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
schema-generator
Generate JSON-LD schema markup for pages and content types with an implementation checklist. Use when users ask for schema, structured data, rich snippets, or markup.