scenario-plan

scenario-plan is a command for coding agents from brainbytes-dev/everything-claude-finance. It costs 9 tokens per session (1,492 once invoked), scanned A, original, MIT.

A financial planning model that compares possible future outcomes using assumptions, probabilities, and decision trees. It can show base, upside, downside, or custom cases.

In plain words
What is it for?
Strategy planning, disruption response, investment choices, risk management, capital allocation, and financial continuity planning.
Why use it?
It makes uncertainty visible and shows which assumptions matter most before a business commits to a decision.

Command

Part of the everything-claude-finance plugin — 13 skills, 22 commands, 20 agents shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/brainbytes-dev/everything-claude-finance/scenario-plan
Clone the repo
git clone --depth 1 https://github.com/brainbytes-dev/everything-claude-finance

Or install everything-claude-finance, the plugin that ships this one along with the rest of its 13 skills, 22 commands, 20 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for scenario-plan

README.md
[![agentmods](https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-finance/scenario-plan.svg)](https://agentmods.dev/commands/brainbytes-dev/everything-claude-finance/scenario-plan)
Your own site
<a href="https://agentmods.dev/commands/brainbytes-dev/everything-claude-finance/scenario-plan"><img src="https://agentmods.dev/badge/commands/brainbytes-dev/everything-claude-finance/scenario-plan.svg" alt="Measured on agentmods" height="20"></a>
Per session 9 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,492 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00009 $0.01492
Opus 5 $0.00005 $0.00746
Sonnet 5 $0.00002 $0.00298
Haiku 4.5 $0.00001 $0.00149

Measured 4d ago against content hash 701197263460, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

scenario-plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 4d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/scenario-plan.md · 140 lines

How it starts

The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/scenario-plan — Financial Scenario Planning

What This Command Does

Builds structured financial scenario models with clearly defined assumptions, probability weightings, and decision trees. Compares outcomes across scenarios to support strategic decision-making. Identifies key variables that most influence outcomes and defines trigger points for scenario reassessment.

When to Use

  • Strategic planning and board strategy sessions
  • Market disruption response planning
  • Investment decision analysis under uncertainty
  • Risk management and contingency planning
  • Capital allocation decisions
  • Business continuity financial planning

How It Works

  1. Variable Identification: Maps key drivers that differ across scenarios
  2. Scenario Definition: Defines base, upside, and downside (or custom) scenarios with narrative
  3. Assumption Setting: Quantifies each driver per scenario
  4. Financial Modeling: Projects P&L, cash flow, and key metrics per scenario
  5. Probability Weighting: Assigns probabilities and calculates expected values
  6. Sensitivity Analysis: Identifies which variables have the largest impact on outcomes
  7. Decision Framework: Maps scenarios to strategic actions and trigger points

Example Usage

Input:

/scenario-plan topic:"European market expansion decision" scenarios:3 horizon:3-year

Output:

# Scenario Planning — European Market Expansion
## Decision: Enter DACH market via own subsidiary | Horizon: 2026–2028

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Scenario Definitions
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

| Scenario  | Narrative                               | Probability |
|-----------|-----------------------------------------|-------------|
| Bull      | Fast adoption, weak competition, strong EUR | 25%      |
| Base      | Steady growth, moderate competition     | 50%         |
| Bear      | Slow adoption, price war, regulatory delays | 25%      |

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Key Driver Assumptions
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

| Driver                  | Bull    | Base    | Bear    |
|-------------------------|---------|---------|---------|
| Year 1 customers        | 80      | 50      | 25      |
| Customer growth rate    | 60%/yr  | 40%/yr  | 15%/yr  |
| ARPU (EUR K/yr)         | 45      | 40      | 35      |
| Sales cycle (months)    | 3       | 5       | 8       |
| Customer churn          | 8%      | 12%     | 20%     |
| Gross margin            | 75%     | 70%     | 62%     |
| EUR/USD rate            | 1.15    | 1.08    | 1.02    |
| Headcount needed (Yr 1) | 12      | 15      | 15      |

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Financial Projections by Scenario
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

### Bull Case (25% probability)
| EUR M          | 2026   | 2027   | 2028   | Cumulative |
|----------------|--------|--------|--------|------------|
| Revenue        | 2.7    | 6.8    | 14.2   | 23.7       |
| EBITDA         | (1.5)  | 1.2    | 5.1    | 4.8        |
| Cumulative CF  | (3.2)  | (2.5)  | 1.8    |            |
| Breakeven      |        |        | Q2'28  |            |

### Base Case (50% probability)
| EUR M          | 2026   | 2027   | 2028   | Cumulative |
|----------------|--------|--------|--------|------------|
| Revenue        | 1.5    | 3.8    | 7.5    | 12.8       |
| EBITDA         | (2.2)  | (0.8)  | 1.8    | (1.2)      |
| Cumulative CF  | (3.8)  | (5.0)  | (3.8)  |            |
| Breakeven      |        |        | Q4'28  |            |

### Bear Case (25% probability)
| EUR M          | 2026   | 2027   | 2028   | Cumulative |
|----------------|--------|--------|--------|------------|
| Revenue        | 0.6    | 1.2    | 2.0    | 3.8        |
| EBITDA         | (2.8)  | (2.5)  | (2.0)  | (7.3)      |
| Cumulative CF  | (4.5)  | (7.2)  | (9.5)  |            |
| Breakeven      |        |        | Not reached |        |

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Expected Value & Decision Matrix
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

| Metric              | Prob-Weighted Value | Risk          |
|---------------------|---------------------|---------------|
| Expected NPV (3yr)  | EUR (0.8M)          | Marginal      |
| Expected NPV (5yr)  | EUR 4.2M            | Positive      |
| Max cash outlay     | EUR 9.5M (bear)     | Manageable    |
| Probability of loss | 35%                 | Moderate      |

### Decision: CONDITIONAL GO
Proceed if: (1) initial investment capped at EUR 5M, (2) 12-month review
gate at 30+ customers triggers full scale-up or exit, (3) partner
distribution model considered to reduce bear-case downside.

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
## Trigger Points for Reassessment
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

| Trigger                    | Action if triggered              |
|----------------------------|----------------------------------|
| < 20 customers by month 9  | Activate exit plan               |
| > 60 customers by month 9  | Accelerate investment            |
| Churn > 18% in any quarter | Pause expansion, fix product fit |
| Competitor enters at -30%  | Reassess pricing strategy        |

Read the full file on GitHub · 140 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 4d ago First seen · 140 lines · 9 tokens per session scan A 701197263460

Subscribe to this mod's changes

scenario-plan is a command published in the GitHub repository brainbytes-dev/everything-claude-finance (5 stars, last pushed 5mo ago), licensed MIT. It adds 9 tokens to every session and 1,492 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.