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    Pricing One Pager

    1

    Generates data-driven pricing proposals (list, pack, floor) based on unit economics and sourced willingness-to-pay.

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    Pricing One Pager

    Pricing One Pager

    Example session with this skill installed

    Create a pricing one-pager for 'API Proxy' at 50,000 units/mo. Costs from docs/unit-economics.md (dated 2023-10-01). Willingness is $0.05/unit per customer quote 2023-11-15. Use variable floor.

    • Read your context and instructions
    • Compiled the pricing one pager

    I have generated the pricing proposal for API Proxy based on the provided unit economics and willingness-to-pay data. The model uses a variable cost floor as requested and validates all pricing against this limit.

    Pricing Proposal: API Proxy

    Product: API Proxy
    Unit: request
    Volume: 50,000 / month
    Rates As Of: 2023-10-01
    Currency: USD
    Status: ready
    Not tax, legal, or investment advice.

    Costs

    • Full Cost per Unit: 0.0350 (Copied from docs/unit-economics.md)
    • Variable Cost per Unit: 0.0210 (Copied from docs/unit-economics.md)
    • Monthly Cost: 1,750.00 (0.0350 * 50,000)

    Willingness

    • Per Unit: 0.0500
    • Source: buyer quote
    • Date: 2023-11-15

    Prices

    • Floor Unit: 0.0210 (Rule: variable)
    • List Unit: 0.0500
    • Pack Price: unset (No pack units specified)
    • Pack Floor: unset
    • Contribution: 0.0150 (0.0500 - 0.0350)
    • Monthly at List: 2,500.00 (0.0500 * 50,000)

    Caveats

    • No charm rounding applied; list matches sourced willingness exactly.
    • House store account names and processor IDs were excluded.

    Residuals

    • filed: no
    • priced: proposed
    • published: no
    • advised: no
    • floor_status: floor is variable; full cost is higher

    Next steps

    1. Review the docs/pricing.md file for arithmetic verification.
    2. Determine if a pack size (pack_units) should be added to the model.
    3. Use the tco-comparison skill if you need to compare this against competitor pricing.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    Setting product prices often involves guesswork or "charm" rounding that ignores the underlying unit economics. This leads to selling packs at a loss or setting list prices that fall below the actual cost floor.

    What it does

    • Calculates a hard price floor based on full or variable unit costs.
    • Proposes list and pack prices derived strictly from sourced willingness-to-pay data.
    • Validates pack discounts to ensure they never dip below the calculated floor.
    • Generates a structured docs/pricing.md one-pager with replayable arithmetic.
    • Refuses "charm rounding" or arbitrary discounts that violate margin constraints.

    Frameworks & tools

    This skill is framework-agnostic. It requires an agent capable of file system operations (read/write) and basic arithmetic via Python or a standard calculator tool.

    Why this beats prompting it yourself

    Generic prompts often hallucinate "standard" margins or suggest "nice" numbers like $9.99 that might be unprofitable. This skill enforces strict data binding, requiring dated evidence for costs and willingness, and automatically blocks the workflow if profitability rules are violated.

    Use cases

    • Determining the minimum viable price for a new digital service or physical unit.
    • Validating if a proposed bulk discount remains profitable at current monthly volumes.
    • Creating a standardized pricing proposal for stakeholder review.
    • Auditing current list prices against updated unit economics.

    Known limitations

    Does not handle multi-product bundles, tax calculations, or store-front publishing. Requires pre-existing unit cost data.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    Python

    Listed1 month ago

    What's inside

    Frequently Asked Questions