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Scenario Planning

CommunityPopular
cbrock84
scenario-planning

Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.

Overview

Publishercbrock84
Repositoryheadcount
Skill namescenario-planning
Stars
1.6K
Forks
237
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cbrock84 on GitHub. Read the source before you install it.

Installation

Install the Scenario Planning 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/cbrock84/headcount.git /tmp/headcount
mkdir -p .claude/skills
cp -r /tmp/headcount/plugins/corporate-strategy/skills/scenario-planning .claude/skills/scenario-planning
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scenario Planning 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 Scenario Planning 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 Scenario Planning 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.

Scenario planning

Forecasting produces one number and false confidence. Scenario planning produces a plan that survives being wrong, which is the realistic goal.

Separate what you know from what you are assuming

List the plan's assumptions explicitly, then sort them:

  • Predetermined — things that will happen regardless. Demographics, contracted commitments, technology already deployed. Plan around them; do not spend analysis on them.
  • Genuinely uncertain and load-bearing — the plan changes materially depending on how they resolve.

Almost every plan has two or three load-bearing uncertainties. Finding them is most of the value, and the exercise usually surfaces one nobody had articulated.

Build scenarios from the uncertainties, not from moods

The common failure is three scenarios named optimistic, base, and pessimistic — which is one scenario with the numbers scaled, and it teaches nothing.

Take the two most consequential uncertainties and build the quadrants. Each scenario should be internally coherent: if demand is high and supply is constrained, what else follows — pricing, competitor behavior, regulatory attention?

Give each a name that captures its logic. Names make scenarios usable in conversation, which is where they earn their keep.

Three or four scenarios. More cannot be held in mind; two collapses into best and worst.

Stress-test the plan against each

For every scenario: does the plan still work, what breaks first, and what would we wish we had done sooner?

The output is not a prediction. It is three things:

  • Robust moves — sensible in every scenario. Do these now, with confidence.
  • Contingent moves — right in some scenarios only. Prepare, do not commit.
  • Options — small investments that buy the right to act later. Deliberately underrated, because they look like indecision and are actually the cheapest way to handle uncertainty.

Early-warning indicators

For each scenario, name the observable signal that would show it is arriving — and specify it precisely enough to be checked. "Regulatory pressure increases" is not observable. "A second jurisdiction opens a consultation" is.

Assign each indicator an owner and a review cadence. Scenario work that produces no monitoring is a workshop, not a plan.

Revisit on the trigger, not the calendar

Most scenario planning is done once and filed. Its value comes from being revisited when an indicator fires — that is the moment the earlier thinking pays, because the options were identified before anyone was under pressure.

Never

  • Assign probabilities to scenarios and then plan only for the likeliest. That is forecasting with extra steps.
  • Build a scenario nobody in the room believes possible. It will be ignored, and the exercise loses credibility.
  • Let the exercise end without naming what to do on Monday in every scenario.

Frequently asked questions

What does the Scenario Planning AI skill do?

Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.

Why use Scenario Planning on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/corporate-strategy/skills/scenario-planning. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Scenario Planning?

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 Scenario Planning?

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

Is the Scenario Planning AI skill free?

Yes. It is published on GitHub by cbrock84 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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