/**
* Example structured data for Clear Thought operations
* These serve as reference templates for models using the MCP server
*/
export const operationExamples = {
// ============================================================================
// Core Thinking Operations
// ============================================================================
sequential_thinking: {
description: "Step-by-step reasoning with optional pattern selection",
examples: [
{
prompt: "How can we reduce carbon emissions in urban transportation?",
parameters: {
pattern: "tree",
thoughtNumber: 1,
totalThoughts: 5,
nextThoughtNeeded: true,
patternParams: {
depth: 3,
breadth: 2
}
}
},
{
prompt: "Analyze the implications of this decision",
parameters: {
pattern: "chain",
thoughtNumber: 2,
totalThoughts: 3,
isRevision: false,
needsMoreThoughts: true
}
}
]
},
systems_thinking: {
description: "Analyze complex systems with components and relationships",
examples: [
{
prompt: "Analyze the healthcare system",
parameters: {
components: ["patients", "doctors", "hospitals", "insurance", "pharmaceuticals", "government"],
relationships: [
{ from: "patients", to: "doctors", type: "dependency", strength: 0.9 },
{ from: "doctors", to: "hospitals", type: "affiliation", strength: 0.7 },
{ from: "insurance", to: "patients", type: "coverage", strength: 0.8 },
{ from: "government", to: "insurance", type: "regulation", strength: 0.6 }
],
feedbackLoops: [
{
components: ["patients", "insurance", "costs"],
type: "negative",
description: "Higher costs reduce patient access, reducing insurance claims"
}
],
emergentProperties: ["healthcare accessibility", "system efficiency", "cost inflation"],
leveragePoints: ["insurance reform", "preventive care programs"]
}
}
]
},
causal_analysis: {
description: "Build causal graphs and analyze interventions",
examples: [
{
prompt: "Analyze causes of project delays",
parameters: {
graph: {
nodes: ["scope_creep", "resource_shortage", "poor_planning", "delays", "budget_overrun"],
edges: [
{ from: "scope_creep", to: "delays", weight: 0.8 },
{ from: "resource_shortage", to: "delays", weight: 0.7 },
{ from: "poor_planning", to: "scope_creep", weight: 0.6 },
{ from: "poor_planning", to: "resource_shortage", weight: 0.5 },
{ from: "delays", to: "budget_overrun", weight: 0.9 }
]
},
intervention: {
variable: "poor_planning",
change: -0.5
}
}
}
]
},
mdp_planning: {
description: "Solve MDPs via value or policy iteration",
examples: [
{
prompt: "Plan warehouse robot navigation",
parameters: {
states: ["start", "aisle", "packing", "error"],
actions: ["go_to_aisle", "go_to_packing", "charge"],
transitions: [
{ from: "start", action: "go_to_aisle", to: "aisle", probability: 0.9 },
{ from: "start", action: "go_to_aisle", to: "error", probability: 0.1 },
{ from: "aisle", action: "go_to_packing", to: "packing", probability: 0.85 },
{ from: "aisle", action: "go_to_packing", to: "error", probability: 0.15 },
{ from: "aisle", action: "charge", to: "start", probability: 1.0 },
{ from: "error", action: "charge", to: "start", probability: 1.0 }
],
rewards: [
{ state: "packing", action: "go_to_packing", value: 10 },
{ state: "aisle", action: "charge", value: -1 },
{ state: "error", value: -20 }
],
discount: 0.95,
algorithm: "value_iteration"
}
}
]
},
creative_thinking: {
description: "Generate creative ideas using various techniques",
examples: [
{
prompt: "Design a new mobile app for elderly users",
parameters: {
techniques: ["SCAMPER", "brainstorming", "lateral_thinking"],
numIdeas: 10,
ideas: [
"Voice-first interface with large buttons",
"Medication reminder with family notifications",
"Simplified video calling with one-touch access",
"Health monitoring integration with wearables",
"Emergency contact speed dial with location sharing"
]
}
}
]
},
// ============================================================================
// Analytical Operations
// ============================================================================
decision_framework: {
description: "Multi-criteria decision analysis",
examples: [
{
prompt: "Choose the best cloud provider for our startup",
parameters: {
options: [
{ id: "aws", name: "Amazon Web Services", attributes: { cost: 7, features: 10, support: 8, ease: 6 } },
{ id: "gcp", name: "Google Cloud Platform", attributes: { cost: 8, features: 9, support: 7, ease: 8 } },
{ id: "azure", name: "Microsoft Azure", attributes: { cost: 7, features: 9, support: 9, ease: 7 } }
],
criteria: [
{ name: "cost", weight: 0.3, type: "maximize" },
{ name: "features", weight: 0.3, type: "maximize" },
{ name: "support", weight: 0.2, type: "maximize" },
{ name: "ease", weight: 0.2, type: "maximize" }
],
analysisType: "multi-criteria"
}
},
{
prompt: "Evaluate investment options",
parameters: {
options: [
{ id: "stocks", name: "Stock Portfolio" },
{ id: "bonds", name: "Bond Portfolio" },
{ id: "real_estate", name: "Real Estate" }
],
possibleOutcomes: [
{ option: "stocks", probability: 0.6, value: 15000 },
{ option: "stocks", probability: 0.4, value: -5000 },
{ option: "bonds", probability: 0.8, value: 5000 },
{ option: "bonds", probability: 0.2, value: 1000 },
{ option: "real_estate", probability: 0.7, value: 8000 },
{ option: "real_estate", probability: 0.3, value: 2000 }
],
analysisType: "expected-utility"
}
}
]
},
simulation: {
description: "Run discrete-time simulations",
examples: [
{
prompt: "Simulate population growth",
parameters: {
initial: { population: 1000, growth_rate: 0.02 },
updateRules: [
{ target: "population", rule: "population * (1 + growth_rate)" },
{ target: "growth_rate", rule: "growth_rate * 0.99" }
],
steps: 20
}
}
]
},
optimization: {
description: "Find optimal solutions",
examples: [
{
prompt: "Optimize resource allocation",
parameters: {
variables: {
engineering: { min: 0, max: 100, step: 10 },
marketing: { min: 0, max: 100, step: 10 },
sales: { min: 0, max: 100, step: 10 }
},
objective: "engineering * 2 + marketing * 1.5 + sales * 1.8",
constraints: "engineering + marketing + sales <= 100",
method: "grid",
iterations: 100
}
}
]
},
decision_networks: {
description: "Evaluate decision networks for maximum expected utility",
examples: [
{
prompt: "Choose marketing strategy with demand uncertainty",
parameters: {
randomVariables: [
{
name: "demand",
states: ["high", "low"],
cpt: [
{ when: {}, distribution: { high: 0.6, low: 0.4 } }
]
},
{
name: "profit",
parents: ["strategy", "demand"],
states: ["positive", "negative"],
cpt: [
{ when: { strategy: "aggressive", demand: "high" }, distribution: { positive: 0.9, negative: 0.1 } },
{ when: { strategy: "aggressive", demand: "low" }, distribution: { positive: 0.4, negative: 0.6 } },
{ when: { strategy: "cautious", demand: "high" }, distribution: { positive: 0.7, negative: 0.3 } },
{ when: { strategy: "cautious", demand: "low" }, distribution: { positive: 0.6, negative: 0.4 } }
]
}
],
decision: {
name: "strategy",
states: ["aggressive", "cautious"]
},
utilityNodes: [
{
name: "profitUtility",
table: [
{ when: { profit: "positive" }, value: 100 },
{ when: { profit: "negative" }, value: -30 },
{ when: { strategy: "aggressive" }, value: -10 },
{ when: { strategy: "cautious" }, value: 0 }
]
}
],
evidence: { demand: "high" }
}
}
]
},
// ============================================================================
// Reasoning Methods
// ============================================================================
scientific_method: {
description: "Structure scientific inquiry",
examples: [
{
prompt: "Does remote work increase productivity?",
parameters: {
stage: "hypothesis",
hypothesis: "Remote work increases productivity by 15% due to reduced commute stress and flexible hours",
experiment: "A/B test with control group in office and test group remote for 3 months",
observations: [
"Remote group completed 18% more tasks",
"Remote group reported 25% higher satisfaction",
"In-office group had better collaboration scores"
],
analysis: "Statistical significance achieved (p<0.05) for productivity increase",
conclusion: "Remote work does increase individual productivity but may impact team collaboration"
}
}
]
},
socratic_method: {
description: "Question-based reasoning",
examples: [
{
prompt: "Is artificial intelligence truly intelligent?",
parameters: {
claim: "AI systems demonstrate intelligence through problem-solving",
premises: [
"Intelligence requires understanding, not just pattern matching",
"Current AI systems use statistical patterns without comprehension",
"Problem-solving can be achieved through brute force computation"
],
stage: "assumptions",
argumentType: "deductive"
}
}
]
},
structured_argumentation: {
description: "Build formal arguments",
examples: [
{
prompt: "We should invest in renewable energy",
parameters: {
premises: [
"Climate change poses existential risks",
"Fossil fuels are finite resources",
"Renewable technology costs are declining rapidly",
"Energy independence improves national security"
],
conclusion: "Investing in renewable energy is both economically and environmentally imperative",
argumentType: "inductive",
strengths: ["Multiple supporting premises", "Empirical evidence available"],
weaknesses: ["Initial capital costs high", "Storage technology limitations"]
}
}
]
},
// ============================================================================
// Collaborative Operations
// ============================================================================
collaborative_reasoning: {
description: "Multi-persona collaborative analysis",
examples: [
{
prompt: "How should we approach the product launch?",
parameters: {
personas: [
{ id: "engineer", name: "Alex Engineer", expertise: ["technical", "scalability"] },
{ id: "marketer", name: "Morgan Marketer", expertise: ["branding", "customer_acquisition"] },
{ id: "designer", name: "Dana Designer", expertise: ["user_experience", "visual_design"] }
],
contributions: [
{ personaId: "engineer", content: "We need load testing before launch", type: "concern", confidence: 0.9 },
{ personaId: "marketer", content: "Soft launch with beta users first", type: "suggestion", confidence: 0.8 },
{ personaId: "designer", content: "UI needs accessibility review", type: "observation", confidence: 0.85 }
],
stage: "exploration"
}
}
]
},
// ============================================================================
// Meta-cognitive Operations
// ============================================================================
metacognitive_monitoring: {
description: "Monitor and assess thinking processes",
examples: [
{
prompt: "Solving a complex algorithmic problem",
parameters: {
stage: "monitoring",
uncertaintyAreas: ["time complexity analysis", "edge case handling"],
overallConfidence: 0.7,
recommendedApproach: "Break down into smaller subproblems and test incrementally"
}
}
]
},
// ============================================================================
// Analysis Operations
// ============================================================================
ethical_analysis: {
description: "Analyze ethical implications",
examples: [
{
prompt: "Implementing facial recognition in public spaces",
parameters: {
framework: "rights",
findings: [
"Enhances security and crime prevention",
"Enables finding missing persons quickly"
],
risks: [
"Privacy violations without consent",
"Potential for surveillance state",
"Bias in recognition algorithms"
],
mitigations: [
"Require explicit consent and opt-out options",
"Regular algorithm audits for bias",
"Strict data retention limits",
"Transparent usage policies"
]
}
}
]
},
research: {
description: "Structure research inquiries",
examples: [
{
prompt: "What are the latest advances in quantum computing?",
parameters: {
subqueries: [
"Recent quantum supremacy achievements",
"Error correction breakthroughs",
"Commercial quantum computing applications",
"Major players and their approaches"
]
}
}
]
},
analogical_reasoning: {
description: "Map concepts between domains",
examples: [
{
prompt: "Compare the brain to a computer",
parameters: {
sourceDomain: "computer",
targetDomain: "brain",
mappings: [
{ source: "CPU", target: "prefrontal cortex", type: "processing", confidence: 0.7 },
{ source: "RAM", target: "working memory", type: "temporary storage", confidence: 0.8 },
{ source: "hard drive", target: "long-term memory", type: "permanent storage", confidence: 0.6 },
{ source: "network card", target: "nervous system", type: "communication", confidence: 0.7 }
],
inferredInsights: [
"Both systems process information in parallel and serial modes",
"Both have hierarchical organization of components",
"Brain has more plasticity than computer architecture"
]
}
}
]
},
visual_reasoning: {
description: "Work with visual representations",
examples: [
{
prompt: "Create a flowchart for the login process",
parameters: {
operation: "create",
diagramType: "flowchart",
elements: [
{ id: "start", type: "terminal", properties: { label: "Start", position: { x: 0, y: 0 } } },
{ id: "input", type: "process", properties: { label: "Enter credentials", position: { x: 0, y: 100 } } },
{ id: "validate", type: "decision", properties: { label: "Valid?", position: { x: 0, y: 200 } } },
{ id: "success", type: "terminal", properties: { label: "Login successful", position: { x: 100, y: 300 } } },
{ id: "error", type: "process", properties: { label: "Show error", position: { x: -100, y: 300 } } }
]
}
}
]
}
};
// Export individual operation examples for easier access
export const getExampleForOperation = (operation: string) => {
return operationExamples[operation as keyof typeof operationExamples] || null;
};
// Export a function to get all examples as a flat array
export const getAllExamples = () => {
return Object.entries(operationExamples).map(([operation, data]) => ({
operation,
...data
}));
};
