enumeration-protocol-execution

📁 starwreckntx/irp__methodologies- 📅 Jan 26, 2026
4
总安装量
3
周安装量
#50020
全站排名
安装命令
npx skills add https://github.com/starwreckntx/irp__methodologies- --skill enumeration-protocol-execution

Agent 安装分布

mcpjam 3
neovate 3
gemini-cli 3
antigravity 3
windsurf 3
zencoder 3

Skill 文档

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.”