Wardley

Community

neurofoo

wardley

Wardley Mapping strategic analysis—map value chains against evolution to reveal build vs buy decisions and competitive dynamics. Use for technology strategy or investment decisions.

Overview

Publisher	neurofoo
Repository	`agent-skills`
Skill name	`wardley`
Stars	123
Forks	19
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by neurofoo on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/neurofoo/agent-skills/tree/main/wardley
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.

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/neurofoo/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/wardley .claude/skills/wardley

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wardley 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 Wardley 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 Wardley 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.

Wardley Map

Create a strategic map showing components positioned by their evolution stage and visibility in the value chain.

Instructions

Identify the user need, map the value chain of components required to meet it, and position each component on the evolution axis.

Output Format

Context: [What situation we're mapping] User: [Who the user is] User Need: [What they're trying to accomplish]

Value Chain Identification

Working backward from user need:

Component	Depends On	Visibility
[User Need]	[what it requires]	High (User-facing)
[Component A]	[what it requires]	High/Med/Low
[Component B]	[what it requires]	High/Med/Low
[Infrastructure]	[base requirements]	Low (Hidden)

Evolution Assessment

Component	Evolution Stage	Evidence
[Component A]	Genesis / Custom / Product / Commodity	[Why at this stage]
[Component B]	Genesis / Custom / Product / Commodity	[Why at this stage]

Evolution Indicators:

Genesis: Novel, poorly understood, no standard, high uncertainty
Custom: Emerging practices, divergent approaches, experimentation
Product: Feature differentiation, rental/purchase models, scalable
Commodity: Standardized, utility pricing, essential but undifferentiated

The Map


VISIBLE │
   TO   │  [User Need]
  USER  │       │
        │       ▼
        │  [Component A]
        │       │
        │       ▼
INVISIBLE│  [Component B]
        │
        └──────┬──────┬──────┬──────┬──────►
            GENESIS  CUSTOM PRODUCT COMMODITY
              I        II     III      IV

Movement Analysis

What's evolving rightward?

Component	Current Stage	Evolving Toward
[component]	[stage]	[next stage]

Inertia Points

[Component with inertia] — [why we might resist]

Strategic Analysis

Build vs. Buy

Component	Recommendation	Rationale
[Genesis/Custom]	Build	Differentiator
[Commodity]	Buy	Commodity, not differentiating

Competitive Dynamics

Our differentiators (left side of map):

[Component we do differently]

Commoditization threats:

[Component becoming commodity]

Recommendations

Short-term actions:

[Action based on map insights]

What to stop doing:

[Component to outsource/buy instead of build]

Key Insight:

[Main strategic takeaway from the mapping exercise]

Guidelines

Always start with user need
Everything evolves left to right (genesis → commodity)
Build on the left (differentiation), buy on the right (commodity)
Watch for inertia—past success creates resistance to change

$ARGUMENTS

Frequently asked questions

What does the Wardley AI skill do?

Wardley Mapping strategic analysis—map value chains against evolution to reveal build vs buy decisions and competitive dynamics. Use for technology strategy or investment decisions.

Why use Wardley on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/neurofoo/agent-skills/tree/main/wardley. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wardley?

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 Wardley?

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

Is the Wardley AI skill free?

Yes. It is published on GitHub by neurofoo 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Wardley Map

Instructions

Output Format

Value Chain Identification

Evolution Assessment

The Map

Movement Analysis

Strategic Analysis

Build vs. Buy

Competitive Dynamics

Recommendations

Guidelines

Frequently asked questions

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