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/conversion-value-mapperWrote 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/conversion-value-mapper)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/conversion-value-mapper"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/conversion-value-mapper/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/conversion-value-mapper"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/conversion-value-mapper.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 67 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00187 | $0.02851 |
| Opus 5 | $0.00093 | $0.01425 |
| Sonnet 5 | $0.00037 | $0.00570 |
| Haiku 4.5 | $0.00019 | $0.00285 |
Grade A, and why
conversion-value-mapper 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 9d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Conversion Value Mapper
Defines and QAs the conversion VALUE model behind value-based paid bidding — per-conversion values, margin/net-value adjustment, static-vs-dynamic value rules, proxy values for non-revenue actions, and a value-vs-count sanity check — delivered as a value-model spec plus a pre-launch value QA sheet. Scope line: this skill BUILDS and QAs the values the platform bids toward so tROAS/max-conversion-value chases profit, not raw order count; it does NOT verify that the event fires or that UTMs are clean — conversion-signal-qa owns the plumbing — and it does NOT score the ROAS R1/R2 vetoes — ad-account-auditor judges those. It is a Return-dimension prerequisite, not the verdict. It is also not the standing cross-platform de-dup / incrementality reconciliation — that is attribution-reconciler; here you only define the value the platform receives, not resolve which platform gets credit for it.
Quick Start
Set up my conversion values so tROAS bids to profit, not revenue. Bid goal: tROAS. Here is my GA4 purchase-value export and my margin / COGS by product-category export: [paste/path].
Build value rules for my non-revenue conversions — assign a proxy value to lead, phone-call, and newsletter-signup so max-conversion-value has something to bid toward.
My tROAS optimizes to revenue but our margins vary 20-70% by SKU — map net margin onto the conversion value and QA it before I relaunch. [GA4 + COGS export attached]
Skill Contract
Expected output: a conversion value-model spec (per-conversion value + net-value/margin adjustment + rule logic), a static-vs-dynamic value-rule decision, proxy values for non-revenue actions with a stated derivation, a value-vs-count reconciliation (does the value the platform receives track the profit the business books?), and the standard handoff summary.
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.
- 9d ago Changed 3fd0cae87c30
- 12d ago First seen · 83 lines · 187 tokens per session scan A e20840c2ff53
conversion-value-mapper is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,760 stars, last pushed yesterday), licensed Apache-2.0. It adds 187 tokens to every session and 2,851 once invoked, about $0.0009 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.
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.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.