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/campaign-architectWrote 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/campaign-architect)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/campaign-architect"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/campaign-architect/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/campaign-architect"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/campaign-architect.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 | $0.00141 | $0.02126 |
| Opus 5 | $0.00071 | $0.01063 |
| Sonnet 5 | $0.00028 | $0.00425 |
| Haiku 4.5 | $0.00014 | $0.00213 |
Grade A, and why
campaign-architect 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Campaign Architect
Plans the structure of a paid-ads account — campaign types, ad-group/asset-group layout, targeting, match types, and negative/exclusion hygiene — and scores the ROAS A (Audience) dimension plus structure. It designs the paid account skeleton (distinct from organic site architecture) and hands the finished structure to the auditor that scores the full account; it does not compute the final RQS itself.
Quick Start
Plan the paid account structure for [goal] on [platforms]. Here is my exported campaign + search-terms report: [paste/path].
Should this be Search, PMax, or broad match? Lay out ad groups and the negative-keyword list for [themes].
Audit paid↔organic cannibalization: here is my GA4 traffic-acquisition export and my campaign export.
Skill Contract
Expected output: a paid account structure (campaign-type choice, ad-group/asset-group map, targeting + match-type plan, negative/exclusion lists), a paid↔organic cannibalization read, a ROAS A dimension score with structure notes, and the standard handoff summary.
- Reads: account/campaign goal, exported campaign + search-terms report, audience/placement reports, GA4 traffic-acquisition export (own data); the budget split from budget-optimizer when present.
- Writes: a user-facing structure plan and reusable summary to
memory/ad/campaign-architect/. - Promotes: chosen campaign type, structure decisions, A-dimension score, cannibalization findings, and missing exports to
memory/hot-cache.mdandmemory/open-loops.md; propose durable structure choices as pending-decision items. - Done when: campaign type is justified against the goal; every ad group / asset group has a single intent theme; match types and a negative/exclusion list are specified; the paid↔organic overlap is reported or its qualified item is Unknown; and the typed ROAS A score is emitted only at complete applicable coverage, otherwise the run is
NEEDS_INPUT/UNDECIDED/NOT_SCOREDwith no score. - Primary next skill: ad-account-auditor to score the full RQS and enforce the veto items.
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 · +1 lines 00701af99252
- 13d ago First seen · 85 lines · 141 tokens per session scan A 384133124aa3
campaign-architect is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed today), licensed Apache-2.0. It adds 141 tokens to every session and 2,126 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.
content-scoring
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.