Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ricneves-ai/flowgrammers-skillsnpx agentmods add skills/ricneves-ai/flowgrammers-skills/scenario-war-roomWrote 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.
[](https://agentmods.dev/skills/ricneves-ai/flowgrammers-skills/scenario-war-room)<a href="https://agentmods.dev/skills/ricneves-ai/flowgrammers-skills/scenario-war-room"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/scenario-war-room/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ricneves-ai/flowgrammers-skills/scenario-war-room"><img src="https://agentmods.dev/badge/skills/ricneves-ai/flowgrammers-skills/scenario-war-room.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.02467 |
| Opus 5 | $0.00044 | $0.01234 |
| Sonnet 5 | $0.00017 | $0.00493 |
| Haiku 4.5 | $0.00009 | $0.00247 |
Grade A, and why
scenario-war-room 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scenario War Room
Modele cenários hipotéticos em cascata por todas as funções do negócio. Não testes de estresse de premissa única — adversidade composta que mostra como um problema cria o próximo.
Keywords
scenario planning, war room, what-if analysis, risk modeling, cascading effects, compound risk, adversity planning, contingency planning, stress test, crisis planning, multi-variable scenario, pre-mortem
Início Rápido
python scripts/scenario_modeler.py # Construtor interativo de cenários com modelagem em cascata
Ou descreva o cenário:
/war-room "E se perdermos nosso cliente principal E perdermos a captação do T3?"
/war-room "E se 3 engenheiros saírem E precisarmos entregar até o T3?"
/war-room "E se nosso mercado encolher 30% E um concorrente captar R$250M?"
O que Este Framework Não É
- Não é um teste de estresse de premissa única (isso é
/em:stress-test) - Não é apenas modelagem financeira — cada função é modelada
- Não é apenas pior cenário — modela 3 níveis de severidade
- Não é paralisia por análise — produz hedges e gatilhos concretos
Framework: Modelo de Cascata em 6 Passos
Passo 1: Defina as Variáveis do Cenário (máx. 3)
Declare cada variável com:
- O que muda — específico, quantificado se possível
- Probabilidade — sua melhor estimativa
- Prazo — quando ocorre
Variável A: Cliente principal (28% ARR) envia aviso de rescisão de 60 dias
Probabilidade: 15% | Prazo: Dentro de 90 dias
Variável B: Captação Série A atrasada 6 meses além do fechamento alvo
Probabilidade: 25% | Prazo: T3
Variável C: Engenheiro principal pede demissão
Probabilidade: 20% | Prazo: Desconhecido
Passo 2: Mapeamento de Impacto por Domínio
Para cada variável, cada função relevante modela o impacto:
| Domínio | Responsável | Modela |
|---|---|---|
| Caixa e runway | CFO | Impacto no burn, mudança no runway, opções de bridge |
| Receita | CRO | Gap de ARR, risco de cascata de churn, pipeline |
| Produto | CPO | Impacto no roadmap, risco de PMF |
| Engenharia | CTO | Impacto na velocidade, risco de pessoa-chave |
| Pessoas | CHRO | Cascata de attrição, implicações do congelamento de contratação |
| Operações | COO | Capacidade, impacto nos OKRs, risco de processo |
| Segurança | CISO | Risco de prazo de conformidade |
| Mercado | CMO | Impacto no CAC, exposição competitiva |
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.
- 13d ago First seen · 226 lines · 87 tokens per session scan A ec16df15cdef
scenario-war-room is a skill published in the GitHub repository ricneves-ai/flowgrammers-skills (112 stars, last pushed 3mo ago), licensed MIT. It adds 87 tokens to every session and 2,467 once invoked, about $0.0004 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 2026-08-30.
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