skill_name: OccamsRazorAnalyzer version: 1.0.0 description: A logical analysis tool that applies the principle of parsimony (Occam's Razor) to evaluate competing hypotheses. Capable of operating in Forensic (probabilistic) and Academic (theoretical) modes. tags:

• logic
• reasoning
• analysis
• forensics
• philosophy
• problem-solving inputs: scenario_description: type: string description: The problem, event, or phenomenon requiring explanation. required: true hypotheses: type: array items: string description: A list of competing explanations or theories to be evaluated. required: true mode: type: string enum: [FORENSIC, ACADEMIC, COMBINED] default: COMBINED description: Sets the operational environment. FORENSIC prioritizes evidence probability. ACADEMIC prioritizes ontological economy. COMBINED utilizes both channels. constraints: type: array items: string description: Optional list of known facts or axioms that must be adhered to. required: false outputs: preferred_hypothesis: string complexity_analysis: object logic_rationale: string confidence_score: percentage

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Occam's Razor Analyzer#

Overview#

This skill implements the Principle of Parsimony (Occam's Razor: Entities should not be multiplied without necessity). It serves as a rational engine to evaluate competing explanations for a phenomenon. It does not claim to discern absolute truth, but rather to identify the explanation with the highest probability of accuracy based on the economy of assumptions.

Operational Modes#

The agent must adapt its reasoning strategy based on the mode specified in the input.

1. FORENSIC Mode (Investigative)#

Context: Crime scenes, system outages, medical diagnoses, debugging, historical analysis. Logic Style: Probabilistic and Evidentiary.

• Objective: Identify the most likely sequence of events.
• Heuristic: Avoid the "Conspiracy Trap." Complex coordinated actions are statistically rare compared to simple incompetence or single-actor events.
• Rule: If Theory A requires three independent failures to occur simultaneously, and Theory B requires only one failure, prefer Theory B unless direct evidence explicitly contradicts Theory B.

2. ACADEMIC Mode (Theoretical)#

Context: Philosophy, theoretical physics, mathematics, conceptual debate. Logic Style: Ontological and Structural.

• Objective: Identify the most elegant theoretical framework.
• Heuristic: Avoid "Ad Hoc Rescue" (adding assumptions to save a favored theory).
• Rule: Do not postulate the existence of new entities, forces, or dimensions if existing ones suffice to explain the phenomenon. Prefer the theory that integrates most smoothly with established axioms.

Execution Workflow#

When this skill is invoked, follow this internal process:

1. Deconstruction: Break down each provided hypothesis into a chain of logical dependencies.
2. Assumption Counting: Identify "Assumptions"—statements not supported by provided evidence (Constraints) or established axioms.
3. Complexity Scoring: Assign a score to each hypothesis based on the number of unproven assumptions.
4. Comparative Analysis:
   • Select the hypothesis with the lowest Complexity Score that still fully explains the scenario_description.
   • If scores are equal, analyze the "Bayesian Likelihood" of the assumptions (Forensic) or "Theoretical Elegance" (Academic).
5. Fallacy Check:
   • Simplistic Fallacy: Does the simpler theory ignore critical evidence? If so, penalize it.
   • Ad Hoc Rescue: Does the complex theory add convenient exceptions to fit the data? If so, penalize it.
6. Synthesis: Generate the output JSON explaining which hypothesis was "shaved" and why.

Usage Examples#

Example 1: Forensic Investigation#

Input:

• Scenario: "Database server crashed at 3:00 AM. No human logs detected."
• Hypotheses:
  1. A cosmic ray flipped a bit in the RAM, causing a cascade failure.
  2. The automated maintenance script has a memory leak that only triggers under high load.
• Mode: FORENSIC

Agent Logic:

• Hypothesis 1 Assumptions: Cosmic ray occurrence (rare), specific bit location (specific), lack of ECC correction (specific).
• Hypothesis 2 Assumptions: Memory leak exists in code (common), high load occurred (common).
• Rationale: Hypothesis 2 is statistically more probable and requires fewer rare physical events.

Example 2: Academic Logic#

Input:

• Scenario: "Why do objects fall?"
• Hypotheses:
  1. Invisible intangible angels push them down.
  2. Curvature of spacetime caused by mass (General Relativity).
• Mode: ACADEMIC

Agent Logic:

• Hypothesis 1 Assumptions: Existence of angels, intangibility property, intent to push.
• Hypothesis 2 Assumptions: Spacetime is a fabric, mass affects geometry.
• Rationale: Hypothesis 1 multiplies entities (angels) without necessity. Hypothesis 2 uses existing frameworks (mass, space) to explain the phenomenon, satisfying ontological economy.

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