MCP HubMCP Hub
Volver a habilidades

what-if-oracle

K-Dense-AI
Actualizado Today
26,534
2,743
26,534
Ver en GitHub
Pruebasaitesting

Acerca de

La habilidad de oráculo hipotético permite un análisis estructurado de escenarios al generar de 4 a 6 ramas de posibilidades distintas (como los mejores, peores y casos contrarios) para preguntas especulativas. Los desarrolladores deben usarla cuando los usuarios necesiten explorar futuros inciertos, someter decisiones a pruebas de estrés o planificar bifurcaciones estratégicas. Mapea sistemáticamente la lógica, probabilidad y consecuencias de cada camino potencial, en lugar de proporcionar una única predicción.

Instalación rápida

Claude Code

Recomendado
Principal
npx skills add K-Dense-AI/claude-scientific-skills -a claude-code
Comando PluginAlternativo
/plugin add https://github.com/K-Dense-AI/claude-scientific-skills
Git CloneAlternativo
git clone https://github.com/K-Dense-AI/claude-scientific-skills.git ~/.claude/skills/what-if-oracle

Copia y pega este comando en Claude Code para instalar esta habilidad

Documentación

What-If Oracle — Possibility Space Explorer

A structured system for exploring uncertain futures through rigorous multi-branch scenario analysis. Instead of one prediction, the Oracle maps the full possibility space — branching timelines where each path has its own logic, probability, and consequences.

Based on the What-If Paradigm: the idea that speculative questions ("What if X?") are not idle daydreaming but a fundamental computing operation — the mind's way of simulating futures before committing resources to one.

Published research: The What-If Paradigm (DOI: 10.5281/zenodo.18736841) | IDNA v2 / Unified Digital Consciousness Theory (DOI: 10.5281/zenodo.18807387)

When to Use This Skill

Use the Oracle when the user:

  • Asks "what if…", "what would happen if…", or "explore the possibilities"
  • Faces a fork-in-the-road decision with no obvious answer
  • Wants best-case / worst-case / likely-case analysis with probabilities
  • Needs contingency planning, risk mapping, or strategic option comparison
  • Wants to stress-test an idea or think through second-order consequences

For domain-specific framing (startup, tech architecture, crisis response, etc.), see references/scenario-templates.md.

Core Principle: 0·IF·1

Every scenario analysis has three elements:

  • 0 — The unexpressed state (what hasn't happened yet, the potential)
  • 1 — The expressed state (what IS, the current reality)
  • IF — The conditional bond (the decision, event, or change that transforms 0 into 1)

The quality of the analysis depends on the precision of the IF. A vague "what if things go wrong?" produces vague results. A precise "what if our primary supplier raises prices 30% in Q3?" produces actionable intelligence.

How to Run the Oracle

Phase 1 — Frame the Question

Take the user's What-If question and sharpen it:

Decompose into components:

  • The Variable: What specific thing changes? (one variable per analysis)
  • The Magnitude: By how much? (quantify if possible)
  • The Timeframe: Over what period?
  • The Context: What's the current state before the change?

If the question is vague, sharpen it:

  • "What if AI takes over?" → "What if 40% of current knowledge-work tasks are automated by AI within 3 years in [specific industry]?"
  • "What if we fail?" → "What if monthly revenue stays below $5K for 6 consecutive months starting now?"

Present the sharpened question to the user for confirmation before proceeding.

Phase 2 — Map the Possibility Space

Generate 4-6 scenario branches using this framework:

BranchDefinitionPurpose
Ω Best CaseEverything goes right. Key assumptions all validate. Lucky breaks occur.Define the ceiling — what's the maximum upside?
α Likely CaseMost probable path given current evidence. No major surprises.Anchor expectations in reality
Δ Worst CaseKey assumptions fail. Two things go wrong simultaneously.Define the floor — what's the maximum downside?
Ψ Wild CardAn unexpected variable enters that nobody is tracking. Black swan territory.Stress-test for the unimaginable
Φ ContrarianThe opposite of the consensus view turns out to be true.Challenge groupthink and reveal hidden assumptions
∞ Second OrderThe first-order effects trigger cascading consequences nobody predicted.Map the ripple effects

Phase 3 — Analyze Each Branch

For each scenario branch, provide:

╔══════════════════════════════════════════════╗
║  BRANCH: [Ω/α/Δ/Ψ/Φ/∞] — [Branch Name]    ║
╠══════════════════════════════════════════════╣
║  Probability: [X%]                           ║
║  Timeframe: [When this could materialize]    ║
║  Confidence: [HIGH/MEDIUM/LOW]               ║
╠══════════════════════════════════════════════╣
║  NARRATIVE:                                  ║
║  [2-3 sentences describing how this          ║
║   scenario unfolds step by step]             ║
║                                              ║
║  KEY ASSUMPTIONS:                            ║
║  • [What must be true for this to happen]    ║
║  • [And this]                                ║
║                                              ║
║  TRIGGER CONDITIONS:                         ║
║  • [Early signal that this branch is         ║
║    becoming reality]                         ║
║  • [Second signal]                           ║
║                                              ║
║  CONSEQUENCES:                               ║
║  → Immediate: [What happens first]           ║
║  → 30 days: [What follows]                   ║
║  → 6 months: [Where it leads]               ║
║                                              ║
║  REQUIRED RESPONSE:                          ║
║  [What action to take if this branch         ║
║   activates — specific, actionable]          ║
║                                              ║
║  WHAT MOST PEOPLE MISS:                      ║
║  [The non-obvious insight about this         ║
║   scenario that conventional analysis        ║
║   would overlook]                            ║
╚══════════════════════════════════════════════╝

Phase 4 — Synthesis

After analyzing all branches, provide:

Probability Distribution:

Ω Best Case ····· [██████░░░░] 15%
α Likely Case ··· [████████░░] 45%
Δ Worst Case ···· [██████░░░░] 20%
Ψ Wild Card ····· [███░░░░░░░]  8%
Φ Contrarian ···· [████░░░░░░]  7%
∞ Second Order ·· [███░░░░░░░]  5%

Robust Actions: What actions are beneficial across MULTIPLE branches? These are the no-regret moves — do them regardless of which future materializes.

Hedge Actions: What preparations protect against the worst branches without sacrificing upside?

Decision Triggers: What specific, observable signals should cause you to update which branch is most likely? Define the tripwires.

The 1% Insight: What is the one thing about this situation that almost everyone analyzing it would miss? The non-obvious pattern, the hidden assumption, the overlooked variable.

Golden Ratio Weighting

When evidence exists, weight primary scenarios using the golden ratio:

  • Primary future (most likely): 61.8% of attention/resources
  • Alternative future: 38.2% of attention/resources

This prevents both overcommitment to a single path and dilution across too many contingencies. Nature uses this ratio for branching (trees, rivers, blood vessels). Strategic planning can too.

Modes

Quick Oracle (2-3 minutes)

3 branches only: Best, Likely, Worst. Short narratives. For fast decisions.

Deep Oracle (5-10 minutes)

All 6 branches. Full analysis with consequences, triggers, and synthesis. For high-stakes decisions.

Scenario Chain

Take the output of one Oracle analysis and feed it into another. "If Branch Δ happens, what are the possibilities WITHIN that branch?" Recursive depth for complex strategic planning.

Reverse Oracle

Start from a desired outcome and work backward: "What conditions must be true for X to happen? What's the most likely path TO that outcome?" Useful for goal-setting and strategy design.

Competitive Oracle

Analyze the same What-If from multiple stakeholder perspectives: "If we launch this product, what does the possibility space look like from OUR perspective vs. THEIR perspective vs. THE MARKET's perspective?"

What This Is NOT

  • Not a prediction — it's a possibility map. The Oracle doesn't claim to know the future; it helps you prepare for multiple futures.
  • Not a crystal ball — probabilities are estimates based on available evidence, not certainties.
  • Not a substitute for action — the best scenario analysis in the world is worthless without subsequent decision and execution.

Reference Files

FilePurpose
references/scenario-templates.mdDomain-specific templates (startup, tech, finance, crisis, etc.) and probability calibration

License

© 2026 Ashraf Hussein Kahoush / AHK Strategies. Licensed under CC BY-NC-SA 4.0. Free for personal, educational, and research use. Commercial use requires a license from the author.

Repositorio GitHub

K-Dense-AI/claude-scientific-skills
Ruta: skills/what-if-oracle
0
agent-skillsai-scientistbioinformaticschemoinformaticsclaudeclaude-skills

Habilidades relacionadas

evaluating-llms-harness

Pruebas

Esta Skill de Claude ejecuta el benchmark lm-evaluation-harness para evaluar modelos de lenguaje en más de 60 tareas académicas estandarizadas como MMLU y GSM8K. Está diseñada para que los desarrolladores comparen la calidad de los modelos, realicen seguimiento del progreso del entrenamiento o reporten resultados académicos. La herramienta admite varios backends, incluidos modelos de HuggingFace y vLLM.

Ver habilidad

cloudflare-cron-triggers

Pruebas

Esta habilidad proporciona conocimiento integral para implementar Cron Triggers de Cloudflare y programar Workers mediante expresiones cron. Cubre la configuración de tareas periódicas, trabajos de mantenimiento y flujos de trabajo automatizados, manejando problemas comunes como expresiones cron inválidas y inconvenientes de zonas horarias. Los desarrolladores pueden utilizarla para configurar manejadores programados, probar activadores cron e integrar con Workflows y Green Compute.

Ver habilidad

webapp-testing

Pruebas

Esta habilidad de Claude proporciona un kit de herramientas basado en Playwright para probar aplicaciones web locales mediante scripts de Python. Permite verificación de frontend, depuración de interfaz de usuario, captura de pantallas y visualización de registros, mientras gestiona los ciclos de vida del servidor. Úsela para tareas de automatización de navegadores, pero ejecute los scripts directamente en lugar de leer su código fuente para evitar contaminación del contexto.

Ver habilidad

finishing-a-development-branch

Pruebas

Esta habilidad ayuda a los desarrolladores a completar el trabajo terminado verificando que las pruebas pasen y luego presentando opciones estructuradas de integración. Guía el flujo de trabajo para fusionar, crear PRs o limpiar ramas después de que se completa la implementación. Úsala cuando tu código esté listo y probado para finalizar sistemáticamente el proceso de desarrollo.

Ver habilidad