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Ansoff Matrix

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deanpeters
ansoff-matrix

Map evidence-backed growth options across the Ansoff Matrix with risk-rated sequencing. Use when the question is where the next tranche of growth comes from, and at what risk.

Overview

Publisherdeanpeters
RepositoryProduct-Manager-Skills
Skill nameansoff-matrix
Stars
7K
Forks
831
Bundled files
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  • 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 Ansoff Matrix 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/ansoff-matrix .claude/skills/ansoff-matrix
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ansoff Matrix 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 Ansoff Matrix 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 Ansoff Matrix 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.

Ansoff Matrix (Evidence-Backed)

Purpose

Map a company's growth options across the Ansoff Matrix with evidence per quadrant: use or gather evidence → four quadrants with signals → risk-rated sequence → next-step options. The four quadrants — market penetration, market development, product development, diversification — organize one question: where does the next tranche of growth come from, and at what risk? This is a research instrument, not wishful whiteboarding: every candidate move must answer "what documented signal says this demand exists?" And the close is a sequence, because growth options compound — penetration funds development, and diversification bets the funding.

Input

Works best with: the company or product line seeking growth, its current core (who is served, with what, at what scale — the matrix's axes are defined relative to it), and the growth outcome and horizon on the table. Also useful: constraints (capital, capability, risk appetite), and any research in session — a landscape scan, five-forces read, or company-intel output lets the matrix organize evidence instead of gathering it.

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. The skill opens with at most 3 questions (core, outcome and horizon, constraints) and proceeds on labeled assumptions if they go unanswered.

Example invocation: Ansoff growth options for our field-service product line — core: dispatch software for mid-market HVAC firms, US. Outcome: +40% ARR in 24 months. Constraint: no acquisitions.

Key Concepts

  • Governing protocol: honors the autonomous-investigation contract — question budget of 3, search-plan gate, Fact/Inference/Assumption labels, Just Enough Mode (2-3 moves per quadrant), stable schema, 4-option Final Step.
  • The framework (Ansoff, 1957): growth options plotted on two axes — existing vs. new products, existing vs. new markets. Penetration (existing/existing) is the lowest-risk quadrant; diversification (new/new) the highest, because it abandons both anchors of proven demand at once.
  • The risk gradient is law. Penetration < market development ≈ product development < diversification. A diversification move rated "low risk" needs extraordinary evidence — and the gradient teaches why diversification proposals deserve the heaviest evidence burden and usually arrive with the lightest.
  • Signals, not wishes. Candidate moves come from documented signals: underserved-segment data, expressed demand (voice-of-customer-miner themes), competitor precedent, capability evidence. An empty diversification quadrant is an acceptable answer; an invented one is not.
  • Coaching vs. investigation — same map, different jobs: organic-growth-advisor is the Interactive sibling that diagnoses your growth constraint through questions (its Growth Path Matrix shares Ansoff's axes); this skill researches the evidence for each quadrant's options. Diagnose there, evidence here — they pair deliberately.
  • When NOT to use: feature-level prioritization (this is portfolio altitude — use feature-investment-advisor for a single build decision); no growth mandate or capacity — an options map without an owner is a poster.
  • Do-not-invent list: market sizes, adoption data, competitor results, demand claims. Where sizing matters, flag it for tam-sam-som-calculator rather than guessing.

Application

  1. Check session for existing evidence (landscape scan, five forces, company intel, VoC). Present → the matrix organizes it; search only gaps.
  2. Credit inline context, then ask only the unanswered questions (max 3):
    1. Which company or product line, and what is its current core?
    2. What growth outcome and horizon is on the table?
    3. Any constraints — capital, capability, risk appetite?
  3. If researching fresh, show the 3-bullet search plan — what you'll search per quadrant (segment data, expressed demand, competitor precedents, capability signals), source types, fact/inference separation. Continue unless revised.
  4. Populate the quadrants and emit the schema below exactly.

Output schema (do not reorder)

markdown
# Ansoff Growth Options: [Company / Product Line]
**As-of date:** | **Current core:** | **Growth outcome sought:**

## 1. Market Penetration (existing product, existing market — lowest risk)
- **[Candidate move]** — signal: [evidence, URL, label] — risk: [low/med/high, why]
- [2-3 moves]

## 2. Market Development (existing product, new market)
- **[Candidate move: segment, geography, or channel]** — signal: [evidence of underserved demand, URL, label] — risk: [rating, why]
- [2-3 moves]

## 3. Product Development (new product, existing market)
- **[Candidate move]** — signal: [expressed demand, VoC theme, competitor precedent, URL, label] — risk: [rating, why]
- [2-3 moves]

## 4. Diversification (new product, new market — highest risk)
- **[Candidate move]** — signal: [the extraordinary evidence this quadrant requires, URL, label] — risk: [rating, why]
- [1-2 moves; an empty quadrant is an acceptable answer]

## 5. Recommended Sequence (the "so what")
- **First:** [move] — because [evidence strength + funding logic]
- **Then:** [move] — funded/de-risked by the first
- **Not yet:** [the tempting move and why the evidence says wait]
- **The assumption that breaks this sequence:** [one line]

### 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. Size the top move with TAM/SAM/SOM (tam-sam-som-calculator) (Recommended)
  2. Pressure-test the sequence with a premortem
  3. Deep-dive the diversification quadrant's evidence
  4. Convert the first move into an opportunity solution tree (opportunity-solution-tree)

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

Examples

A quadrant entry earning its place (fictional):

2. Market Development

  • Adjacent trade: plumbing contractors, same size band — signal: plumbing firms appear unprompted in 14% of our category's review-site mentions asking "does this work for plumbing?" — Fact ([review threads, URLs]); the two incumbents serving plumbing both gate scheduling behind enterprise tiers — Fact ([pricing pages]) — risk: medium — demand signal is real but second-hand; sales motion transfers, integrations don't fully.

The sequence close doing its job:

  • First: win-back campaign into the churned-but-reachable base (penetration) — strongest evidence, funds everything else, 1-quarter payback
  • Then: plumbing-contractor entry (market development) — de-risked by the penetration win's cash and case studies
  • Not yet: the IoT hardware bundle (diversification) — one analyst mention and founder enthusiasm is not extraordinary evidence
  • The assumption that breaks this sequence: churned customers left for fixable reasons; if win-loss shows they left the category, penetration is a dead first move and development leads.

See examples/sample.md for a complete worked matrix (fictional FSM-software market) with an honestly empty diversification quadrant and a sequence whose breaking assumption is named. examples/sample-industrial.md shows the opposite lesson: a populated diversification quadrant whose entry fails the evidence bar in writing.

Common Pitfalls

  • The brainstorm grid. Four quadrants of unsourced ambition. Every move answers "what signal says this demand exists?" or it doesn't ship — that single rule converts Ansoff from wall art into an instrument.
  • Risk-gradient denial. A diversification move rated low-risk on enthusiasm. The gradient is the framework's whole teaching: new product and new market means both anchors are gone.
  • Quadrant stuffing. Filling diversification because empty feels lazy. An honestly empty quadrant is a finding; a padded one is a liability with a deadline.
  • Options without sequence. A menu with no first move, no funding logic, no breaking assumption. Growth options compound — order is the strategy.
  • Sizing by vibe. Attaching invented market sizes to moves. The do-not-invent list routes sizing to the TAM/SAM/SOM calculator, where the math shows its work.

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 Ansoff Matrix AI skill do?

Map evidence-backed growth options across the Ansoff Matrix with risk-rated sequencing. Use when the question is where the next tranche of growth comes from, and at what risk.

Why use Ansoff Matrix on TypingMind?

Because you install it once and use it with any model. Ansoff Matrix 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 Ansoff Matrix in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/ansoff-matrix. 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 Ansoff Matrix?

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 Ansoff Matrix?

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

Is the Ansoff Matrix 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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