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AAJ Marketing Skills
39 marketing skills for AI agents. 24 run real engines.
Ask an AI agent about your unit economics, and you get a definition. These compute the numbers. 24 of the 39 ship with runnable engines — unit economics, pipeline forecasts, churn and NRR, budget allocation, AI-citation baselines. Your numbers in, a benchmarked verdict out, arithmetic shown. The other 15 are structured methods: positioning, messaging, content briefs, win/loss, campaign sequencing — written for an agent to execute, not for a human to skim.
I build marketing systems for Seed-to-Series B companies. Earlier this year I kept hitting the same wall: I'd ask an AI agent a unit economics question and get a beautiful paragraph explaining what LTV: CAC means, instead of the number for my actual business.
So I built the engines. 39 skills covering positioning, pipeline, retention, paid media and SEO/GEO — and 24 of them ship with runnable Node code. You give them your numbers, they compute a verdict, and they show the arithmetic.
LTV $12,480 · LTV:CAC 2.6:1 · CAC payback 15.4 months ▼ Below the 3:1 floor — acquisition is inefficient; lift LTV or cut CAC before scaling. ▼ Payback exceeds the ~12-month guideline — cash is tied up longer; watch burn.
Not a definition. A number, a benchmark, and what to do next. Same inputs, same answer, every time — which is something a prompt can't promise you.
A few of them are deliberately opinionated. The citation tracker won't report movement that sits inside run-to-run variance, so it will sometimes tell you nothing happened where a dashboard would draw you a trend line. The campaign orchestrator refuses to write ads until it knows what's broken. That's the point of them.
I've run these against my own work and a set of other companies, and I'd like to see them meet numbers I haven't seen. Run one on yours and tell me what it says — especially if you disagree with a benchmark or think a verdict is wrong. That's the feedback that makes them better, and I'll act on it.
I'm here all day.
About AAJ Marketing Skills on Product Hunt
“39 marketing skills for AI agents. 24 run real engines.”
AAJ Marketing Skills was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #98 on the daily leaderboard. Ask an AI agent about your unit economics, and you get a definition. These compute the numbers. 24 of the 39 ship with runnable engines — unit economics, pipeline forecasts, churn and NRR, budget allocation, AI-citation baselines. Your numbers in, a benchmarked verdict out, arithmetic shown. The other 15 are structured methods: positioning, messaging, content briefs, win/loss, campaign sequencing — written for an agent to execute, not for a human to skim.
On the analytics side, AAJ Marketing Skills competes within Open Source, Marketing, Growth Hacking and GitHub — topics that collectively have 729.4k followers on Product Hunt. The dashboard above tracks how AAJ Marketing Skills performed against the three products that launched closest to it on the same day.
Who hunted AAJ Marketing Skills?
AAJ Marketing Skills was hunted by Saroj Jha. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of AAJ Marketing Skills including community comment highlights and product details, visit the product overview.
Saroj Jha
Hi Product Hunt — Saroj here.
I build marketing systems for Seed-to-Series B companies. Earlier this year I kept hitting
the same wall: I'd ask an AI agent a unit economics question and get a beautiful paragraph
explaining what LTV: CAC means, instead of the number for my actual business.
So I built the engines. 39 skills covering positioning, pipeline, retention, paid media
and SEO/GEO — and 24 of them ship with runnable Node code. You give them your numbers,
they compute a verdict, and they show the arithmetic.
Here's one, verbatim. ARPA $400, 78% gross margin, 2.5% monthly churn, $4,800 CAC:
LTV $12,480 · LTV:CAC 2.6:1 · CAC payback 15.4 months
▼ Below the 3:1 floor — acquisition is inefficient; lift LTV or cut CAC before scaling.
▼ Payback exceeds the ~12-month guideline — cash is tied up longer; watch burn.
Not a definition. A number, a benchmark, and what to do next. Same inputs, same answer,
every time — which is something a prompt can't promise you.
A few of them are deliberately opinionated. The citation tracker won't report movement
that sits inside run-to-run variance, so it will sometimes tell you nothing happened
where a dashboard would draw you a trend line. The campaign orchestrator refuses to write
ads until it knows what's broken. That's the point of them.
No signup, no telemetry. One command:
npx skills add sarojkjha/aaj-marketing-skills --skill unit-economics --yes
I've run these against my own work and a set of other companies, and I'd like to see them
meet numbers I haven't seen. Run one on yours and tell me what it says — especially if you
disagree with a benchmark or think a verdict is wrong. That's the feedback that makes them
better, and I'll act on it.
I'm here all day.