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
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.
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-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/commands/aaron-he-zhu/aaron-marketing-skills/seo-geo)<a href="https://agentmods.dev/commands/aaron-he-zhu/aaron-marketing-skills/seo-geo"><img src="https://agentmods.dev/badge/commands/aaron-he-zhu/aaron-marketing-skills/seo-geo/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/commands/aaron-he-zhu/aaron-marketing-skills/seo-geo"><img src="https://agentmods.dev/badge/commands/aaron-he-zhu/aaron-marketing-skills/seo-geo.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 | $0.00058 | $0.01875 |
| Opus 5 | $0.00029 | $0.00937 |
| Sonnet 5 | $0.00012 | $0.00375 |
| Haiku 4.5 | $0.00006 | $0.00187 |
Grade A, and why
seo-geo 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 13d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO/GEO Command
Run the SEO/GEO lifecycle along the SITE loop (Survey → Implement → Tune → Evaluate) — the single SEO/GEO entrypoint (peer of /aaron-marketing:influencer and /aaron-marketing:ad). Skills score on CORE-EEAT (content) and CITE (domain authority).
Route
Infer the SITE-loop phase from the goal (or honor --phase) and route to the matching skill:
- Survey — keyword-research, serp-analysis, content-gap-analysis, competitor-analysis, offsite-signal-analyzer (backlinks mode), entity-registry, site-structure-optimizer (linking mode)
- Implement — content-writer (new/refresh modes), geo-content-optimizer, serp-markup-builder (meta/schema modes), site-structure-optimizer, content-quality-auditor (pre-publish gate); by page intent: page-play-builder (
--type programmaticbulk/template ·parasitethird-party-platform ·comparison"X vs Y"/alternatives ·locallocal/GMB) - Tune — on-page-seo-checker, content-quality-auditor, technical-seo-checker, site-structure-optimizer (with --full or --tech), geo-content-optimizer
- Evaluate — domain-authority-auditor (CITE citation-trust gate), rank-tracker, performance-monitor (report/alert modes), offsite-signal-analyzer (ai-referrals mode), memory-management, entity-registry
Rules
Phase selection: honor --phase when given. Without it, infer from the goal (a topic/market → survey; a brief/draft/series → implement; a URL/domain to evaluate → tune; authority/rankings/alerts/reports/memory → evaluate); if ambiguous, ask one concise blocking question instead of guessing.
--phase survey
- Discover search demand, SERP intent, topic clusters, and content opportunities; keep AI-answer-inclusion diagnosis in
--phase tune --visibility. - With
--competitors, compare across rankings, content coverage, backlinks, authority, and AI citation visibility; return a battlecard, gaps, priority opportunities, and evidence mode. - For the
competitor_gapscenario (--competitors), explicitly run bothdata_insufficientandgeo_visibility_claim; require competitors, market, and domain as blocking inputs. AI citation visibility is not an optional metric that may be estimated: without observed query, market/locale, engine, date, and citation evidence, keep the AI gap Unknown/NEEDS_INPUTand do not claim it. - With
--map(or a known opportunity set), turn findings into a content architecture, topic/entity map, and internal-link plan: clusters, pillar/supporting pages, orphan risks, anchor guidance, and next briefs. - Keep evidence mode visible (tool vs. estimate); hand off to
--phase implementfor production.
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.
- 13d ago First seen · 61 lines · 58 tokens per session scan A 398868656ab1
seo-geo is a command published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,771 stars, last pushed today), licensed Apache-2.0. It adds 58 tokens to every session and 1,875 once invoked, about $0.0003 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 commands, from other repositories
geo:loop
Run one bounded loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, close the run.
geo
Full GEO optimization pipeline - analyze, rank, rewrite, and generate schema for any URL or content.
geo:optimize
Optimize a local content file for GEO without full audit.
geo:audit
Analyze content for GEO optimization opportunities without making changes.
geo:batch
Process multiple content files in a folder.
geo:compete
Analyze competitive landscape for a query or topic.