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Price sensitivity analysis

Skill intelligems-io/intelligems-mcp-skills/skills/price-sensitivity-analysis

Ready-to-install Agent Skills for analyzing Intelligems experiments, segments, pricing, and reporting through MCP and API.

Install
npx -y skills add intelligems-io/intelligems-mcp-skills --skill price-sensitivity-analysis

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Use when a user asks what Intelligems test data says about price tolerance, discount response, AOV tradeoffs, or segment-specific price behavior.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

1.7 KB, 306 tokens by cl100k_base, as published. Nobody here has run it

Price Sensitivity Analysis

Use this skill to interpret price, discount, shipping, and offer tests.

Steps

  1. Resolve organization, period, and product or offer scope.
  2. Search relevant experiences by name, category, or explicit IDs.
  3. Analyze overview metrics first: conversion rate, RPV, GPV, AOV, net revenue, confidence.
  4. Segment the results by device, visitor type, country, traffic source, and landing page when relevant.
  5. Compare results across tests to identify repeatable price tolerance patterns.

Questions To Answer

  • Did price or discount changes increase profit, not just conversion?
  • Which segments are most price-sensitive?
  • Did higher AOV offset lower conversion, or did lower price dilute value?
  • Are discounts training behavior or unlocking incremental demand?
  • What guardrails should future price tests use?

Output

Return:

  1. Price sensitivity summary.
  2. Evidence table by test and segment.
  3. Profit and risk interpretation.
  4. Recommendations for pricing, discounting, and follow-up tests.
  5. Caveats around sample size, seasonality, and missing margin data.

Do not recommend a permanent pricing change without profit evidence and business-context caveats.

Data Safety

Use only the user's authenticated MCP or API context. Do not save raw responses, exports, graph URLs, order data, customer data, or private store identifiers to the repository. Summarize findings and include experience IDs only when needed for the user's workflow.

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