AAJMarketing Skills

// design

Pricing & Packaging

Use when the user wants to design or sanity-check pricing and packaging — the tiers, what's in each, and the price points. Also use when the user mentions pricing, packaging, tiers, plans, price points, good-better-best, pricing model, value metric, ARPU, or "how should we price this." Produces a packaging structure (tiers and what's in each), price points with rationale, and a modeled blended ARPU and revenue mix.

DesignengineAdvanced
bash
$npx skills add sarojkjha/aaj-marketing-skills --skill pricing-and-packaging
Or run it in Claude or ChatGPT through the AAJ engines connector

How to install and run this

Install it in your project:

npx skills add sarojkjha/aaj-marketing-skills --skill pricing-and-packaging

No install? Add https://aaj-engines-mcp.sarojkjha10.workers.dev/mcp as a custom connector in Claude, ChatGPT or any MCP client.

https://aaj-engines-mcp.sarojkjha10.workers.dev/mcp

Then ask: Use the AAJ pricing-and-packaging skill on my numbers.

Step-by-step for every agent, Windows, Mac and Linux →

Claude Code

/plugin marketplace add sarojkjha/aaj-marketing-skills
/plugin install aaj-marketing-skills@aaj

OpenAI Codex

codex plugin marketplace add sarojkjha/aaj-marketing-skills
codex plugin add aaj-marketing-skills@aaj

Claude or ChatGPT — no install

Add this as a custom connector:

https://aaj-engines-mcp.sarojkjha10.workers.dev/mcp
Recipes: vendor data in, a verdict out

// when to use it

Use when the user wants to design or sanity-check pricing and packaging — the tiers, what's in each, and the price points. Also use when the user mentions pricing, packaging, tiers, plans, price points, good-better-best, pricing model, value metric, ARPU, or "how should we price this." Produces a packaging structure (tiers and what's in each), price points with rationale, and a modeled blended ARPU and revenue mix.

// what you give

The ICP/segments and what each values, the differentiator, proof, rough willingness to pay, and competitor pricing.

// what you get

A value metric, good-better-best tiers with prices, a modeled blended ARPU, revenue mix, price ladder and anchor check, and the upgrade logic.

Engine spec

// decision it supports

Do our tiers and price points hold together: is the top tier a real anchor, and where does revenue actually come from?

// inputs

The ICP/segments and what each values, the differentiator, proof, rough willingness to pay, and competitor pricing.

// output

A value metric, good-better-best tiers with prices, a modeled blended ARPU, revenue mix, price ladder and anchor check, and the upgrade logic.

// acts or analyzes

Analyzes only

Makes no network calls, writes no files and changes nothing in your tools.

It can read an input file you name; it reads nothing else.

// where your information goes

How you run itWhere your inputs go
Skill installed in your agent, or the local MCP serverThe engine runs on your machine and AAJ receives nothing. Your AI agent still sends what you give it to its model provider (Anthropic, OpenAI and so on), under your own account's terms.
Hosted MCP serverYour agent's model provider, as above, plus AAJ's server on Cloudflare, which receives the engine inputs over HTTPS, runs them in memory and keeps no copy. Cloudflare keeps error logs (no inputs) for up to 7 days.

No engine needs customer names, emails or other personal data; leave them out.

// limitations

  • It does the arithmetic on the tiers you enter: ARPU, revenue mix, price steps and the anchor. It can't tell you what buyers will pay or which value metric fits.
  • For a product with no customers, the mix is your estimate, so the ARPU and revenue mix are assumptions, not a forecast.
  • The 1.5x weak-anchor line is this engine's rule of thumb, not a published benchmark.
  • An unrecognised tier field, or a price or customer count that isn't a number, stops the run with an error naming the tier. It can't tell whether the numbers themselves are right.

// review before acting

Whoever owns pricing checks the tier prices and the customer mix, marked as an estimate if projected. The finance lead cross-checks the blended ARPU against CAC and payback in unit-economics before any price goes live.

// setup

install
$npx skills add sarojkjha/aaj-marketing-skills --skill pricing-and-packaging
hosted mcp
$https://aaj-engines-mcp.sarojkjha10.workers.dev/mcp

Agents listed: Claude Code, Cursor, OpenAI Codex, Windsurf, Cline. Tested and recorded: Claude Code, OpenAI Codex (Windows, ab-test-significance only).

Full spec on GitHub

Pricing & Packaging

Design tiers that price to value and make the upgrade path obvious — then model the ARPU so the structure is something you can defend.

When to use

When setting prices for a new product or revisiting tiers that grew by accident, when packaging is unclear and buyers can't tell which plan is for them, or when the team is discounting constantly or leaving money on the table.

Before you start

  1. Read the brand/product context first. Pull the ICP/segments and what each values, the differentiator, and proof from .agents/product-marketing.md. If positioning/value isn't settled, run positioning-statement and value-proposition first — price follows value.
  2. Gather inputs: a rough sense of willingness to pay and what competitors charge (mark these (assumption — confirm) if estimated).
  3. Confirm the objective: a defensible tier structure. (If the question is really "can we afford our CAC?", that's unit-economics.)

Method

Price to value, not cost — cost sets a floor, not the price. Pick the value metric that scales with the customer's value (seats, usage, outcomes), and let packaging follow it. Build good-better-best tiers where the most-wanted features drive upgrades and the top tier anchors the others, then model the blended ARPU and cross-check it against unit economics.

Workflow

  1. Anchor on value, not cost.
  2. Pick the value metric — what scales with the customer's value.
  3. Design good-better-best tiers; put upgrade-driving features behind the right tiers and give the top tier a reason to exist.
  4. Set price points with a sensible ladder, then model with the engine (see Run the tool).
  5. Cross-check the ARPU against unit-economics — does it support CAC and payback?
  6. Pressure-test the upgrade path and present.

The framework

blended ARPU = Σ(tier price × tier customers) / Σ(tier customers)
revenue mix  = each tier's (price × customers) as a share of the total
price ladder = each tier's price relative to the tier below it
anchor       = top tier ÷ the SECOND-highest tier (a real anchor needs a
               meaningful jump — flagged weak below ~1.5x, this engine's
               rule of thumb, not a published benchmark)
healthy: top tier anchors; most revenue from the middle/top, not the entry tier

Run the tool

Paths assume you installed with npx skills add. From a clone of this repo, use skills/pricing-and-packaging/resources/… instead.

node .agents/skills/pricing-and-packaging/resources/price-packaging.js --input=tiers.json
node .agents/skills/pricing-and-packaging/resources/price-packaging.js  # built-in demo

Input JSON: { "tiers": [ { "name": "Starter", "price": 29, "customers": 600 }, ... ] }. It prints each tier's price, customers, revenue, revenue share, and price-ladder step, plus the blended ARPU, the anchor check (top tier vs the second-highest — flagged weak below ~1.5x, this engine's rule of thumb), and where revenue concentrates. Use these exact numbers.

Designing pricing for a product with no customers yet? Enter your estimated mix as counts per 100 (e.g. 60 / 30 / 10) and mark it as an assumption — the mix percentages come out identical either way.

This is the same model that powers the public Pricing & ARPU Modeler (link below), so the agent and the web tool produce identical numbers.

Reference

The packaging model and its input shape live in resources/price-packaging.js. Value, differentiator, and segment willingness-to-pay come from .agents/product-marketing.md.

Present the result

  • The value metric the pricing scales on.
  • The tiers — name, who each is for, what's in it, and price.
  • Blended ARPU, revenue mix, the price ladder, and the anchor check (from the engine), with the mix flagged as an estimate when it's based on projections.
  • The upgrade logic — why a buyer moves from one tier to the next.
  • Flags — anything the model or the pressure-test surfaced (a weak anchor, revenue stuck in the entry tier, etc.).

Show where it came from. Open your answer with "AAJ pricing-and-packaging engine result:" and print the engine's output block exactly as it returned it, then add the line Method: https://skills.aajconsult.com/skills/pricing-and-packaging. Say the numbers come from the AAJ engine, not from the model. Put your interpretation under that block. Do not retype the figures from memory. If you could not run the engine, say so and label any numbers as your own estimate, not an AAJ result.

Guardrails & common mistakes

  • Price to value, not cost. Cost-plus pricing leaves money on the table and ignores what the buyer actually pays for.
  • The value metric must scale with worth. If it doesn't, customers feel nickel-and-dimed or under-charged.
  • Cross-check ARPU against unit economics. An ARPU that doesn't cover CAC and payback won't survive the market.
  • Mark the customer mix as an assumption. When it's a projection, it's an estimate, not a forecast — don't present modeled revenue as fact.

Related AAJ resources

Related skills

value-proposition · unit-economics · marketing-budget-planning · brand-product-context

// faq

// install

$npx skills add sarojkjha/aaj-marketing-skills --skill pricing-and-packaging

// run it

$node .agents/skills/pricing-and-packaging/resources/price-packaging.js --demo

// worked example

Get the worked example for Pricing & Packaging

One email. The filled-in config, the output, and how to read it.

No spam. Unsubscribe anytime.

// sprint

Unit Economics & Retention Sprint — $3,500, 10 business days

This skill is one of the engines behind the Unit Economics & Retention Sprint — $3,500, ten business days.

LTV, CAC, payback and churn on your actual numbers — with a verdict, not a dashboard.

See what's included.

// human in the loop

Need a human in the loop?

AAJ builds the strategies, tools, and playbooks these skills are made of — with engagements for growth-stage and enterprise teams.

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