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/product-feed-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/product-feed-optimizer)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/product-feed-optimizer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/product-feed-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/product-feed-optimizer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/product-feed-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00144 | $0.02903 |
| Opus 5 | $0.00072 | $0.01452 |
| Sonnet 5 | $0.00029 | $0.00581 |
| Haiku 4.5 | $0.00014 | $0.00290 |
Grade A, and why
product-feed-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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Feed Optimizer
Audits and rewrites the Shopping / Performance Max product feed — title and description patterns, required and recommended attributes, GTIN/availability/price hygiene, disapproval triage, and feed-driven asset-group / listing-group structure. This is the research-phase skill that hardens the product data behind the ROAS O (Offer) dimension; it does not write text-ad copy (that is ad-creative-builder) and does not score the account or compute the RQS (that is ad-account-auditor).
Quick Start
Audit my Shopping feed export for disapprovals and missing attributes: [path]. Goal is DR.
Rewrite these product titles to a front-loaded pattern and fill the missing GTIN/brand/condition attributes. [feed CSV]
Triage my Merchant Center disapprovals and group the approved products into PMax listing groups. [diagnostics export + feed]
Skill Contract
Expected output: a feed remediation package — (1) a disapproval / diagnostics triage table (item → cause → fix), (2) rewritten titles + descriptions to a front-loaded attribute pattern, (3) an attribute-completeness map (required + recommended, per item, with the missing fields named), (4) identifier/availability/price hygiene fixes (GTIN, availability, price vs landing page), and (5) a feed-driven asset-group / listing-group structure — with notes that inform the ROAS O (Offer) dimension, plus the standard handoff summary.
- Reads: the user's own product-feed export (TSV/CSV/XML — title, description, GTIN/MPN/brand,
google_product_category,product_type,condition,availability,price,link,image_link), Merchant Center / catalog diagnostics or a disapproval list, the destination landing pages for price/availability truth, the campaign goal (DR or prospecting), and target platforms; approved claim wording and live-offer terms frommemory/claims/claims-ledger.mdandmemory/claims/offers.md— the offer-claims-registry ledger — when present. - Writes: a user-facing feed remediation package and reusable summary to
memory/ad/product-feed-optimizer/. - Promotes: the disapproval causes, the title/attribute pattern chosen, the identifier/price-hygiene rules, and any unresolved disapproval or unsubstantiated-claim risk to
memory/hot-cache.mdandmemory/open-loops.md; propose durable feed conventions (title template, category mapping) as pending-decision items. - Done when: every disapproved item has a named cause and a proposed fix; each rewritten title front-loads the highest-intent attributes within the platform's character limit; required attributes are present or flagged per item;
price/availabilityin the feed match the landing page (or the mismatch is flagged); no title or description carries an unsubstantiated claim or a likely policy violation; and the listing-group / asset-group structure maps to real feed segments. - Primary next skill: ad-account-auditor — scores the feed against ROAS, including O1 (claim integrity) and O2 (platform-policy) veto checks.
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 8b86431228f5
- 12d ago First seen · 90 lines · 144 tokens per session scan A 2a9e20da0f05
product-feed-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 144 tokens to every session and 2,903 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.