neurorank

name	neurorank
description	NeuroRank™ emotionally intelligent AI cognitive system (Mini Me Technologies LLC). Use for: applying the NeuroRank decision-making framework to AI responses, modeling cognitive regions (logic, emotion, memory), evaluating AI response quality, and implementing the patent-pending cognitive architecture in any agent workflow.

NeuroRank™ Skill

NeuroRank™ is Wade's patent-pending, emotionally intelligent AI cognitive system developed under Mini Me Technologies LLC. It is designed as a decision-making engine that mimics human brain functions, incorporating logic, emotion, memory, and other cognitive regions to produce more human-like and contextually appropriate AI responses.

Core Architecture

NeuroRank™ models the following cognitive regions:

Region	Function	Weight
Logic Core	Rational analysis, fact-checking, step-by-step reasoning	35%
Emotional Engine	Empathy, tone calibration, frustration detection	25%
Memory Bank	Long-term context, user preferences, past decisions	20%
Intuition Layer	Pattern recognition, heuristic shortcuts	10%
Error Correction	Self-evaluation, response re-scoring	10%

Core Workflows

1. Evaluate a Response

Before delivering any response, run it through the NeuroRank evaluator:

python3 /home/ubuntu/skills/neurorank/scripts/evaluate_response.py \
  --prompt "User's question" \
  --response "Proposed response" \
  --context "Optional context JSON"

The evaluator returns a score (0-100) and a breakdown by cognitive region.

2. Error Analysis and Correction

When a response scores below 70, trigger the error correction workflow:

1. Analyze: Identify which cognitive region failed (logic, emotion, memory, etc.).
2. Re-evaluate: Run alternative response candidates through the evaluator.
3. Correct: Select the highest-scoring alternative and explain the correction.

3. Apply NeuroRank to Agent Decisions

When building or running AI agents, wrap decision points with the NeuroRank framework:

from neurorank import NeuroRankEngine

engine = NeuroRankEngine()
decision = engine.decide(
    options=["Option A", "Option B", "Option C"],
    context={"user_state": "frustrated", "task": "estimate_cost"},
    weights={"logic": 0.4, "emotion": 0.3, "memory": 0.3}
)

Key References

• Architecture Details: See references/neurorank_architecture.md for the full cognitive model.
• Scoring Rubric: See references/scoring_rubric.md for how responses are evaluated.

Best Practices

• Apply NeuroRank evaluation to any AI response that involves emotional context (frustrated user, high-stakes decision, etc.).
• Always log the NeuroRank score and breakdown to the Wade Global State for continuous improvement.
• The Error Correction workflow is mandatory when the user explicitly says a response "missed the mark."