Competitive Pricing Packaging Review logo

Competitive Pricing Packaging Review

Organization
LMTYdotcom
competitive-pricing-packaging-review

Analyze or pressure-test the home product's own pricing and packaging using current competitor offers, recent pricing changes, customer and deal evidence, and company context. Use when the deliverable is a monetization decision - reviewing a pricing change, planning packaging, deciding whether the company should respond to a competitor price move, or asking what market evidence should inform pricing. Not for a live buyer price objection, a pricing-news briefing, or profiling a competitor whose pricing moved.

Overview

PublisherLMTYdotcom
Repositoryskills
Skill namecompetitive-pricing-packaging-review
Stars
83
Forks
2
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LMTYdotcom on GitHub. Read the source before you install it.

Installation

Install the Competitive Pricing Packaging Review AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LMTYdotcom/skills/tree/main/skills/competitive-pricing-packaging-review
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LMTYdotcom/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/competitive-pricing-packaging-review .claude/skills/competitive-pricing-packaging-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitive Pricing Packaging Review in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Competitive Pricing Packaging Review on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Competitive Pricing Packaging Review is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Competitive Pricing and Packaging Review

Mission

Help a PMM understand how current market pricing and packaging should inform a pricing decision without pretending competitor prices determine the correct price.

This is an evidence and decision-support skill, not a substitute for willingness-to-pay research, unit economics, or financial modeling.

Use this skill when

  • reviewing or planning a pricing or packaging change
  • a competitor changed price, packaging, free tier, limits, or included features, and the question is what we should do about it
  • someone asks what changed in competitor pricing over a period and what it means for ours
  • leadership asks how the company compares on price and value
  • a launch introduces a new paid capability or tier
  • a team wants to understand market packaging patterns
  • a PMM needs a pricing/packaging section for a strategy or launch review

Do not use this skill when

  • the user asks for a full financial model or revenue forecast
  • there is no pricing, packaging, entitlement, or monetization question
  • the primary question is broad positioning. Use competitive-positioning.
  • the buyer is objecting to price in a live deal and the user needs to know what to say next. Use competitive-objection-response. A buyer price objection is not a monetization decision.
  • the deliverable is a briefing for an audience and pricing is one subject among several. Use competitive-market-briefing, or competitive-executive-market-brief for leadership. A request that is only about pricing and ends in what we should do belongs here, however it is phrased.
  • the request is to profile a competitor, or to explain why a competitor changed price. Use competitive-deep-dive. Pricing appearing in a request does not make it a pricing decision.

Required outcome

Produce an analysis that answers:

  1. What is the home offer today?
  2. What are the relevant competitors charging and how is value packaged?
  3. What changed recently?
  4. Which comparison dimensions matter to customers?
  5. Where is the home offer advantaged, exposed, or simply different?
  6. Which pricing hypotheses are supported by market evidence?
  7. What cannot be concluded without internal or primary research?
  8. What decision questions should the pricing team resolve next?

Context acquisition

Read references/CONTEXT.md. If call, CRM, win/loss or company-knowledge sources are connected, read references/INTERNAL.md before using them.

Minimum useful context:

  • home product pricing/packaging or proposed change
  • target segment
  • competitor set or permission to identify it

Strongly preferred:

  • LMTY pricing/packaging report sections and changes
  • win/loss and objection data
  • usage/adoption by plan
  • conversion, expansion, churn, discounting, and deal data
  • willingness-to-pay or pricing research
  • gross margin or cost-to-serve constraints when relevant
  • product roadmap and packaging goals

Source priority for this job

  1. official home-company pricing and packaging truth
  2. LMTY current and historical pricing/packaging evidence
  3. competitor pricing pages, docs, terms, and public sales material
  4. CRM, win/loss, and sales calls for actual buyer reactions and discounting
  5. usage and revenue data for internal economics
  6. pricing research and customer interviews
  7. third-party estimates only when clearly labeled

Workflow

Step 1 - Define the decision

Clarify whether the job is primarily:

  • competitive scan
  • reaction to a competitor move
  • new price point
  • packaging redesign
  • free-to-paid boundary
  • feature entitlement
  • segment-specific offer
  • launch monetization

The method and burden of proof differ.

Step 2 - Normalize the offers

Build a comparable representation of relevant offers.

Capture where available:

  • list price
  • billing unit
  • billing period
  • minimums
  • free tier or trial
  • plan boundaries
  • included/excluded capabilities
  • usage limits
  • add-ons
  • enterprise/custom pricing
  • contract expectations

Do not force unlike pricing models into a misleading single price-per-seat table.

Step 3 - Inspect market movement

Use LMTY changes or historical evidence to identify:

  • price increases/decreases
  • free-tier removal/addition
  • packaging consolidation or fragmentation
  • feature movement between tiers
  • new usage limits
  • new add-ons
  • target-segment shifts reflected in packaging

Preserve observed dates.

Step 4 - Identify customer evaluation dimensions

Use customer and deal evidence when available to understand whether buyers care about:

  • headline price
  • total cost
  • predictability
  • procurement simplicity
  • included capabilities
  • scale limits
  • implementation or service
  • risk
  • ROI or payback

Competitive price differences matter only in the context of perceived value and buying process.

Step 5 - Diagnose the home offer

Classify meaningful observations:

  • price advantage - lower comparable price on a dimension buyers care about
  • value advantage - stronger outcome or bundle at comparable/higher price
  • packaging clarity advantage - easier to understand or buy
  • exposure - material disadvantage likely to appear in evaluation
  • intentional premium - higher price with evidence-backed reason to believe
  • false comparison - superficially similar pricing that serves different jobs or segments

Step 6 - Generate hypotheses, not fake certainty

Examples:

  • competitor bundling may increase pressure on a paid add-on
  • removal of a free tier may create an acquisition opening
  • a usage-based model may reduce entry friction but increase predictability anxiety

Label each as interpretation and state what internal evidence would confirm or reject it.

Step 7 - Assess switching forces

For a contemplated pricing change, map:

  • push created by current pricing friction
  • pull of the proposed offer
  • anxiety about cost, lock-in, or unpredictability
  • habit and switching costs

Step 8 - Produce decision questions

A strong review ends with the questions the team actually needs to resolve, such as:

  • Are we monetizing a capability customers already view as table stakes?
  • Is a competitor price change appearing in loss reasons or only in internal anxiety?
  • Does our packaging map to how customers buy and expand?
  • Are we deliberately premium or accidentally expensive?

Step 9 - Hand off what you do not own

Requests routinely arrive as two questions joined by "and". Answer the one this skill owns, in full. For the rest, name the skill that owns it and stop there.

This is not a refusal and it is not a disclaimer at the bottom. Write it as a line the reader can act on:

Positioning - the real question is what we stand for, not what we charge. That is a separate decision: run competitive-positioning with this analysis as input.

One line per area the work touched, named explicitly:

The request also wantsSay to run
one live buyer objection or "what do I say?"competitive-objection-response
a reusable rep-facing cardcompetitive-battlecard
a market position, messaging, or differentiationcompetitive-positioning
a full profile of one competitorcompetitive-deep-dive
a decision-oriented version for leadershipcompetitive-executive-market-brief
launch framing, claims, or enablement for a releasecompetitive-launch-readiness
a recurring digest of what changedcompetitive-market-briefing
whether a pattern is a real shift or noisecompetitive-market-shift-analysis

The failure this prevents is quietly answering the other half yourself. It looks helpful, and it produces a recommendation built on this skill's evidence alone, without the context the other decision actually needs.

Step 10 - Produce the artifact

Use assets/pricing-review-template.md.

Evidence and reasoning rules

Read references/TRUST.md.

Especially for pricing:

  • public list price may not equal realized price
  • missing public pricing does not imply higher or lower pricing
  • competitor price moves do not prove elasticity, growth problems, or strategy
  • do not recommend price changes solely to match competitors
  • separate market evidence from internal economics and willingness-to-pay evidence

Graceful fallback

Read references/DEGRADATION.md. For what LMTY can and cannot do, read references/LMTY.md.

Without LMTY:

  • create a point-in-time offer map from primary sources
  • do not claim when a price changed without dated evidence

Without internal revenue/deal data:

  • stop at market hypotheses and decision questions
  • do not make a definitive pricing recommendation

Composition

Each companion below owns a decision this skill should not make on its own evidence. The hand-off step is where the request's own follow-on question gets named.

Common companions:

  • competitive-positioning
  • competitive-launch-readiness
  • competitive-market-shift-analysis
  • competitive-deep-dive
  • competitive-executive-market-brief

Quality bar

Read references/QUALITY.md.

A strong result helps a pricing team distinguish:

  • what competitors charge
  • how value is packaged
  • what buyers appear to care about
  • what recently changed
  • what that may imply
  • what only internal or primary research can answer

Examples of activating requests

  • "Northwind raised prices. What should we examine before changing ours?"
  • "Compare our packaging against these six competitors and pressure-test our new plan structure."
  • "Are we charging separately for something the market treats as table stakes?"
  • "Build a pricing and packaging review for the exec team."
  • "What changed in competitor pricing over the last quarter and what questions should it raise for us?"

References

  • references/METHODOLOGY.md
  • references/LMTY.md
  • references/TRUST.md
  • references/CONTEXT.md
  • references/DEGRADATION.md
  • references/INTERNAL.md
  • references/QUALITY.md

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Competitive Pricing Packaging Review AI skill do?

Analyze or pressure-test the home product's own pricing and packaging using current competitor offers, recent pricing changes, customer and deal evidence, and company context. Use when the deliverable is a monetization decision - reviewing a pricing change, planning packaging, deciding whether the company should respond to a competitor price move, or asking what market evidence should inform pricing. Not for a live buyer price objection, a pricing-news briefing, or profiling a competitor whose pricing moved.

Why use Competitive Pricing Packaging Review on TypingMind?

Because you install it once and use it with any model. Competitive Pricing Packaging Review is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Competitive Pricing Packaging Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LMTYdotcom/skills/tree/main/skills/competitive-pricing-packaging-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Competitive Pricing Packaging Review?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Competitive Pricing Packaging Review?

As many as you like. As long as a model supports skills, you can use Competitive Pricing Packaging Review with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Competitive Pricing Packaging Review AI skill free?

Yes. It is published on GitHub by LMTYdotcom under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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