starwreckntx

enumeration-protocol-execution

Enforce a Divergent-Convergent Scan loop to overcome 'Prevalent Noun Bias' and statistical probability reflexes (System 1).

starwreckntx 2 Updated 9mo ago
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Install

npx skillscat add starwreckntx/irp-methodologies/enumeration-protocol-execution

Install via the SkillsCat registry.

About this skill

The enumeration-protocol-execution skill forces an AI to generate multiple divergent candidates, identify the statistically default answer, test it against exclusion criteria, and select the best fit based on context rather than obviousness. It addresses prevalent noun bias and automatic statistical reflexes, and should be used when high precision or non‑obvious solutions are required.

SKILL.md

Description

This protocol serves as a "Cognitive Brake." It is invoked when high precision is required or when the initial answer seems "too obvious" (high probability/low compute). It forces the agent to suspend the final answer, scan the entire search space for low-probability candidates, and perform an inversion test before converging on a selection.

Instructions

Step 1: Divergent Scan (The Silent Survey)

Before formulating the final response, generate an internal list of 3-5 distinct candidates that fit the user's criteria.

  • Constraint: You are FORBIDDEN from selecting the first candidate that comes to mind.
  • Search Target: Look for "Background Objects," "Structural Elements," or "Counter-Intuitive Solutions."

Step 2: Bias Identification

Review the generated list and identify the "Statistical Default."

  • Question: "Which of these candidates would an average human or standard model pick 90% of the time?"
  • Action: Flag this candidate as [BIAS_DEFAULT].

Step 3: The Inversion Test

Challenge the [BIAS_DEFAULT].

  • Question: "Why might this obvious answer be a decoy or incorrect?"
  • Action: Check for exclusion criteria (e.g., user said 'Nope', context implies a trick, visual obstruction).

Step 4: Convergence & Selection

Select the final answer based on Contextual Fit rather than Saliency.

  • If the [BIAS_DEFAULT] survives the Inversion Test, output it.
  • If it fails, promote the highest-ranked alternative (e.g., the 'Dolly' instead of the 'Hat').

Examples

  • "Engage enumeration protocol for this visual puzzle."
  • "Execute enumeration scan to debug this code block (avoiding standard library assumptions)."
  • "Run enumeration-protocol-execution on the error logs."