Scenario War Room

What it does

When your agent uses it

• Facing complex risk scenarios
• Strategic decisions with major downside
• The user asks what if X AND Y both happen?

Example prompts

• “what if X AND Y both happen?”
• “/scenario-war-room”

Requirements

• Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

1. 1Define Scenario Variables (max 3)
2. 2Domain Impact Mapping
3. 3Cascade Effect Mapping
4. 4Severity Matrix
5. 5Trigger Points (Early Warning Signals)
6. 6Hedging Strategies

What it can do on your machine

Read from SKILL.md and the folder at commit 19392f7. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scenario War Room loads about 2k tokens when it runs, and up to ~4k if it reads all its reference files. Until then it costs about 86 tokens; SKILL.md has 660 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~86

When it runs · the whole SKILL.md, loaded when a task matches

~2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from alirezarezvani/claude-skills at commit 19392f7, republished under its MIT licence (© alirezarezvani). 660 words, ~1,986 tokens.

name

scenario-war-room

description

Cross-functional what-if modeling for cascading multi-variable scenarios. Unlike single-assumption stress testing, this models compound adversity across all business functions simultaneously. Use when facing complex risk scenarios, strategic decisions with major downside, or when the user asks 'what if X AND Y both happen?'

license

MIT

metadata.version

1.0.0

metadata.author

Alireza Rezvani

metadata.category

c-level

metadata.domain

strategic-planning

metadata.updated

2026-03-05

metadata.python-tools

scenario_modeler.py

metadata.frameworks

scenario-planning

python scripts/scenario_modeler.py   # Interactive scenario builder with cascade modeling

Variable A: Top customer (28% ARR) gives 60-day termination notice
  Probability: 15% | Timeline: Within 90 days

Variable B: Series A fundraise delayed 6 months beyond target close
  Probability: 25% | Timeline: Q3

Variable C: Lead engineer resigns
  Probability: 20% | Timeline: Unknown

TRIGGER: Customer churn ($560K ARR)
  ↓
CFO: Runway drops 14 → 8 months
  ↓
CHRO: Hiring freeze; retention risk increases (morale hit)
  ↓
CTO: 3 open engineering reqs frozen; roadmap slips
  ↓
CPO: Q4 feature launch delayed → customer retention risk
  ↓
CRO: NRR drops; existing accounts see reduced velocity → more churn risk
  ↓
CFO: [Secondary cascade — potential death spiral if not interrupted]

Trigger for Customer Churn Risk:
  - Sponsor goes dark for >3 weeks
  - Usage drops >25% MoM
  - No Q1 QBR confirmed by Dec 1

Trigger for Fundraise Delay:
  - <3 term sheets after 60 days of process
  - Lead investor requests >30-day extension on DD
  - Competitor raises at lower valuation (market signal)

Trigger for Engineering Attrition:
  - Glassdoor activity from engineering team
  - 2+ referral interview requests from engineers
  - Above-market offer counter-required in last 3 months

SCENARIO: [Name]
Variables: [A, B, C]
Most likely path: [which combination actually plays out, with probability]

SEVERITY LEVELS
Base (A only): [runway/ARR impact] — recovery: [X actions]
Stress (A+B): [runway/ARR impact] — recovery: [X actions]
Severe (A+B+C): [runway/ARR impact] — existential risk: [yes/no]

CASCADE MAP
[A → domain impact → B trigger → domain impact → end state]

EARLY WARNING SIGNALS
- [Signal 1 → which scenario it indicates]
- [Signal 2 → which scenario it indicates]
- [Signal 3 → which scenario it indicates]

HEDGES (take these actions now)
1. [Action] — cost: $X — impact: [what it buys] — owner: [role] — deadline: [date]
2. [Action] — cost: $X — impact: [what it buys] — owner: [role] — deadline: [date]
3. [Action] — cost: $X — impact: [what it buys] — owner: [role] — deadline: [date]

RECOMMENDED DECISION
[One paragraph. What to do, in what order, and why.]

© alirezarezvani, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (scripts, references) in c-level-advisor/skills/scenario-war-room of alirezarezvani/claude-skills.

• SKILL.md
• references/scenario-planning.md
• scripts/scenario_modeler.py

Open the folder on GitHubat commit 19392f7

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in alirezarezvani/claude-skills, which our catalogue first saw on October 7, 2026.

• LeoYeAI/openclaw-master-skillsskills/scenario-war-roomskills/c-level-advisor/scenario-war-room