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/landing-optimizerWrote 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/landing-optimizer)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/landing-optimizer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/landing-optimizer/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/landing-optimizer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/landing-optimizer.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 | $0.00092 | $0.02893 |
| Opus 5 | $0.00046 | $0.01447 |
| Sonnet 5 | $0.00018 | $0.00579 |
| Haiku 4.5 | $0.00009 | $0.00289 |
Grade A, and why
landing-optimizer 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Optimizer
This skill helps you create and optimize landing pages specifically for influencer marketing traffic. When users click from an influencer's post, the landing experience should feel connected and optimized for conversion.
Cross-discipline (paid ads): this is also the paid-ads post-click skill — the page half of the ROAS Offer message-match (it pairs with ad-creative-builder, which owns the ad half). The same diagnose-and-fix flow applies to paid landing pages; save paid runs under
memory/ad/landing-optimizer/. On paid runs, message-match the page against the offer-claims-registry ledger when present: offer terms, promo codes, and dates againstmemory/claims/offers.md, and claim wording against the approved variants inmemory/claims/claims-ledger.md.
Quick Start
Shortest invocation:
Optimize our landing page for traffic from [influencer campaign]
Common scenario — diagnose and fix a low-converting creator page:
Our influencer landing page has [X%] conversion rate. How can we improve it?
Skill Contract
- Reads: a transient landing-page locator plus opaque
page_ref/snapshot ref and current state, conversion rate and goal, traffic source, stable opaquecreator_ref, platforms/content type, and any proposed creator display name, message, quote, asset, embed, or screenshot. Every creator reuse also reads the exact frozenapproved_asset_refplus creator-content-auditorapproval_ref, and a rights record that isactive, dated/evidenced, unexpired, and explicitly scoped to channel, territory, format, duration, and paid-vs-organic use. Inputs come from the user when no tool is connected. - Writes: return the optimization plan inline by default; save it to
memory/influencer/landing-optimizer/YYYY-MM-DD-<topic>.md(or the declared paid path) only with exact WARM-save authorization. Saved artifacts and handoffs keepcreator_ref,page_ref,snapshot_ref, frozen asset/approval refs, and opaque rights/evidence refs only—never a raw creator handle/name, profile/content/page URL, email, provider ID, or embedded creator media. - Promotes: only with separate exact authorization, promote durable facts — active campaign ref, opaque page ref, baseline conversion rate, promo code ref, primary
creator_ref— tomemory/hot-cache.md. - Done when:
- Message-match score and named fixes are produced for the page.
- A prioritized conversion plan (CTA, promo-code experience, friction, mobile) exists with evidence-labeled impact or
Unknown/NEEDS_INPUT. - An A/B test roadmap with at least one hypothesis and success metric is written.
- Every proposed creator name/quote/asset/embed/screenshot reuse has the exact frozen auditor approval and an active dated scoped-rights row covering the whole implementation/test duration; blocked reuse remains
NEEDS_INPUTand is neither copied nor tested.
- Primary next skill: performance-analyzer — measure whether the optimizations moved conversion.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · +3 lines c3a01696d794
- 13d ago First seen · 112 lines · 92 tokens per session scan A d599c7bc4d5a
landing-optimizer 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 92 tokens to every session and 2,893 once invoked, about $0.0005 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.