# Scenario Planning

Plans under genuine uncertainty, building scenarios, identifying which assumptions are...

- Repository: https://github.com/cbrock84/headcount (2554 stars)
- Category: Testing
- License: MIT
- Last updated: 2026-10-18
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add cbrock84/headcount --skill scenario-planning -g -a claude-code -y`
Codex: `npx skills add cbrock84/headcount --skill scenario-planning -g -a codex -y`
Cursor: `npx skills add cbrock84/headcount --skill scenario-planning -g -a cursor -y`
Gemini CLI: `npx skills add cbrock84/headcount --skill scenario-planning -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add cbrock84/headcount`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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

The rest: https://github.com/cbrock84/headcount/blob/HEAD/plugins/corporate-strategy/skills/scenario-planning/SKILL.md