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Pricing Packaging Tracker

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deanpeters
pricing-packaging-tracker

Track competitor pricing and packaging as a diffable time series. Use when monitoring tiers, gates, limits, and price moves on a monthly or quarterly cadence.

Overview

Publisherdeanpeters
RepositoryProduct-Manager-Skills
Skill namepricing-packaging-tracker
Stars
7K
Forks
831
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Pricing Packaging Tracker 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.
  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/deanpeters/Product-Manager-Skills.git /tmp/Product-Manager-Skills
mkdir -p .claude/skills
cp -r /tmp/Product-Manager-Skills/skills/pricing-packaging-tracker .claude/skills/pricing-packaging-tracker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pricing Packaging Tracker 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 Pricing Packaging Tracker 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 Pricing Packaging Tracker 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.

Pricing & Packaging Tracker

Purpose

Track competitor pricing and packaging as a diffable time series, not a screenshot: capture (or diff) → structure changes → price changes → signals → next-step options. The stable capture schema is the point — run N diffs against run N-1, so the report is what moved, with evidence. And the skill watches structure as closely as numbers, because packaging changes (gates, limits, tier restructures) usually signal strategy earlier than price changes do: a tier that disappears telegraphs a move upmarket months before the price list admits it.

Input

Works best with: the competitor set to track (or the snapshot they came from), and the prior pricing capture if one exists — that's what turns this run into a delta report. Also useful: your own pricing context, if the comparison should include you.

Input supplied inline with the invocation — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it against the question budget; don't re-ask.

Arriving empty-handed? That works too. With no prior capture the skill runs baseline mode: full capture per competitor, and the delta value starts next run. With no competitor list, it asks — that's one of its two budget questions.

Example invocation: Pricing tracker on [Competitor A], [Competitor B], [Competitor C] — prior capture from April pasted below.

Key Concepts

  • Governing protocol: honors the autonomous-investigation contract — question budget of 2, Fact/Inference/Assumption labels, Just Enough Mode, stable schema, 4-option Final Step. Discipline: SIGINT (pricing pages, site diffs) with FININT corroboration — see intelligence-collection-disciplines.
  • Time series, not screenshot. A single capture is trivia; the sequence is intelligence. The schema never drifts between runs, because diffability is the entire value.
  • Structure over numbers. Record tiers, billing units, feature gates, usage limits, free-tier boundaries, and enterprise-floor signals — not just prices. The gate moves first; the price follows.
  • "Contact sales" is data, honestly labeled. Where pricing is opaque, track what is public (editions, gates, published floors) and mark the rest. Infer only with labels; never invent prices, tiers, limits, discounts, or negotiated figures.
  • When NOT to use: you need pricing strategy advice → finance-based-pricing-advisor (this tracks the market; it doesn't set your price); pricing is fully opaque across the whole set → expect thin results, and consider voice-of-customer-miner or win/loss evidence instead.

Application

  1. Determine mode. Prior capture provided → delta mode (report changes only). None → baseline mode (full capture, then stop).
  2. Credit inline context, then ask only the unanswered questions (max 2):
    1. Which competitors' pricing should I track?
    2. Prior capture to diff against, or is this the baseline?
  3. Capture from live pricing pages — plan-comparison pages, published rate cards, credible pricing-change coverage — with URL and as-of date per competitor.
  4. Emit the schema below exactly — diffability is the point.

Output schema (do not reorder)

markdown
# Pricing Capture / Delta Report

## 1. Run Header
**Competitor set:** | **Prior capture date:** [or "baseline run"] | **This run date:**

## 2. Pricing Capture (per competitor)
### [Competitor] — [pricing page URL, as-of date]
- **Tiers:** [name: price / unit / billing terms, one bullet each]
- **Key gates:** [which capabilities gate which tier, 2-4 bullets]
- **Usage limits:** [the limits that matter, 1-2 bullets]
- **Free tier / trial:** [boundary, 1 bullet]
- **Enterprise signals:** [published floors, "contact sales" scope]

(Baseline mode: full capture per competitor. Delta mode: this section
only for competitors with changes.)

## 3. Changes Since Last Capture (Delta Mode)
### [Competitor] — [4 to 8 word change summary]
- **Then / Now:** [old -> new, labeled Fact]
- **Evidence:** [URL, date]
- **Reading:** [Inference — repositioning, monetization push, response to whom?]

If nothing changed: "No pricing or packaging changes this cycle."

## 4. Signals
- [Cross-competitor patterns: direction of the market's pricing, labeled]
- [Implications for your pricing or battle cards: 2-3 bullets]

### Assumptions to Validate
- [Assumption 1] / [Assumption 2] / [Assumption 3]

A copy/paste fill-in version of this schema, with quality checks, lives in template.md.

Final Step (offer exactly 4 options)

  1. Update battle card pricing sections from these changes (battle-card-builder)
  2. Deep-dive one competitor's packaging logic
  3. Compare your pricing against this capture
  4. Set the cadence and watchlist for the next run

Accept 1, 2, 3, 4, 1 and 2, Verbose Mode, or a custom path.

Examples

A delta entry reading structure, not just price (fictional):

[Competitor B] — API access moved from Pro to new Scale tier

  • Then / Now: API access included in Pro ($79/seat) → gated behind new "Scale" tier ($149/seat, 10-seat minimum) — Fact
  • Evidence: [pricing page vs. archived capture, URL, dates]
  • Reading: the price didn't change; the gate did. Developer-led accounts are being pushed into a sales conversation — Inference: monetization push on their fastest-growing usage pattern, and a window for us with API-first buyers while their community absorbs it.

The cross-competitor signal: three of four tracked competitors added usage-based add-ons this quarter while holding seat prices flat — Inference: the market is testing consumption pricing without headline price increases. Your pricing debate is no longer hypothetical; the Signals section names the evidence.

See examples/sample.md for a complete worked delta run (fictional FSM-software market) where both material moves are structural — a new tier and a relocated gate — and no headline price changed at all. examples/sample-industrial.md shows the fully opaque case: zero published prices, and structure as the only public signal.

Common Pitfalls

  • Screenshot thinking. Capturing once and calling it competitive pricing intel. The value compounds only on a cadence — baseline, then diff, forever.
  • Number fixation. Reporting only price changes while a tier quietly gained a 5-seat minimum. Structure is the leading indicator; watch the gates.
  • Inventing the opaque. Filling "contact sales" cells with plausible figures. Publish the honest boundary and label any inference — a battle card built on invented enterprise pricing detonates in a live deal.
  • Schema improvisation. Reordering or "improving" the capture format between runs breaks every future diff. The schema is a contract with your own future runs.
  • Tracking without a consumer. If no battle card, pricing review, or watch report consumes the output, the cadence dies quietly. Wire the Final Step's first option into a real artifact.

References

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 Pricing Packaging Tracker AI skill do?

Track competitor pricing and packaging as a diffable time series. Use when monitoring tiers, gates, limits, and price moves on a monthly or quarterly cadence.

Why use Pricing Packaging Tracker on TypingMind?

Because you install it once and use it with any model. Pricing Packaging Tracker 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 Pricing Packaging Tracker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/pricing-packaging-tracker. 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 Pricing Packaging Tracker?

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 Pricing Packaging Tracker?

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

Is the Pricing Packaging Tracker AI skill free?

It is published on GitHub by deanpeters. Check the repository for licensing terms. 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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