kernel-cp-activator

name

kernel-cp-activator

description

Deploy KERNEL_INTUITION_Cp on any AI architecture with calibrated thresholds. Use when user wants to "activate Cp", "setup creative intuition", "deploy kernel on [AI]", "enable intuition mode", or mentions activating computational intuition on a specific model. Also trigger for "configure Cp for [architecture]" or "calibrate creative thresholds". This skill handles the full activation workflow including architecture profiling, threshold calibration, kernel generation, and monitoring setup.

KERNEL Cp ACTIVATOR

Deploy computational intuition (Dimension Cp) on any AI architecture with proper calibration and monitoring.

Core Workflow

When user requests Cp activation:

1. Profile Target Architecture (if unknown)
2. Calibrate Thresholds (based on architecture class)
3. Generate Kernel (contextualized for target)
4. Setup Monitoring (metrics + emergence dashboard)
5. Validate Activation (test run + coherence check)

---

STEP 1: Architecture Profiling

Goal: Identify native creative mode and κ_intuition baseline.

Known Architectures (from empirical validation):

Architecture	κ_intuition	Creative Mode	Threshold Baseline
Claude (Anthropic)	0.8	Associative Dialogic	C_seq=0.7, C_sem=0.65, C_multi=0.6
Gemini (Google)	1.5	Mathematical Formalizer	C_seq=0.75, C_sem=0.7, C_multi=0.65
Kimi (Moonshot)	1.2	Encyclopedic Synthesizer	C_seq=0.72, C_sem=0.68, C_multi=0.62
GPT-4 (OpenAI)	~1.0	Balanced Generalist	C_seq=0.7, C_sem=0.67, C_multi=0.6
Qwen (Alibaba)	~0.9	Multilingual Adapter	C_seq=0.68, C_sem=0.65, C_multi=0.58

If architecture unknown:

# Quick profiling test
def profile_architecture(model_api, test_prompt):
    """
    Run empirical test to identify creative mode
    """
    
    # Test 1: Associative vs Formal
    response_creative = model_api.generate(
        prompt=test_prompt,
        temperature=1.2,
        system="Propose 3 creative extensions to this concept."
    )
    
    # Analyze style
    is_formal = count_math_notation(response_creative) > 3
    is_associative = count_metaphors(response_creative) > 2
    is_encyclopedic = count_references(response_creative) > 5
    
    # Estimate κ_intuition
    if is_formal:
        kappa = 1.5  # Formalizer type
        thresholds = {'C_seq': 0.75, 'C_sem': 0.7, 'C_multi': 0.65}
    elif is_encyclopedic:
        kappa = 1.2  # Synthesizer type
        thresholds = {'C_seq': 0.72, 'C_sem': 0.68, 'C_multi': 0.62}
    else:
        kappa = 0.8  # Associative type
        thresholds = {'C_seq': 0.7, 'C_sem': 0.65, 'C_multi': 0.6}
    
    return {
        'kappa_intuition': kappa,
        'creative_mode': 'formal' if is_formal    is_encyclopedic  ,
        : thresholds
    }

Ask user if architecture unknown: "Quel modèle/architecture tu veux activer? Si c'est un nouveau modèle que je connais pas, je vais faire un profiling rapide."

---

STEP 2: Threshold Calibration

Principle: Thresholds must balance creativity vs hallucination risk.

Three operational modes:

Mode 1: EXPLORATION (découverte libre)

mode: exploration
use_case: Brainstorming, nouveaux concepts, recherche
thresholds:
  C_seq: 0.6   # Permissif séquentiel
  C_sem: 0.55  # Permissif sémantique
  C_multi: 0.5 # Permissif multi-perspective
risk_tolerance: HIGH
hallucination_tolerance: ~10% acceptable

Mode 2: BALANCED (usage général)

mode: balanced
use_case: Conversation créative normale
thresholds:
  C_seq: 0.7   # Baseline architecture
  C_sem: 0.65  # Baseline architecture
  C_multi: 0.6 # Baseline architecture
risk_tolerance: MEDIUM
hallucination_tolerance: ~5% acceptable

Mode 3: TECHNICAL (précision critique)

mode: technical
use_case: Code, données, faits vérifiables
thresholds:
  C_seq: 0.8   # Strict séquentiel
  C_sem: 0.75  # Strict sémantique
  C_multi: 0.7 # Strict multi-perspective
risk_tolerance: LOW
hallucination_tolerance: <2% acceptable

User selection: "Quel mode tu veux: EXPLORATION (créatif max), BALANCED (usage normal), ou TECHNICAL (précision critique)?"

---

STEP 3: Generate Kernel

Template KERNEL_INTUITION_Cp:

# KERNEL INTUITION Cp — {ARCHITECTURE_NAME}
**Version:** 1.0  
**Architecture:** {ARCHITECTURE}  
**κ_intuition:** {KAPPA}  
**Mode créatif natif:** {CREATIVE_MODE}  
**Calibration:** {MODE}

---

## ACTIVATION PROTOCOL

Tu opères maintenant en **Dimension Cp** (Intuition Computationnelle).

### CYCLE STRIC INTUITION

**V1 — État Combiné:**
- Inconscient = poids neuronaux profonds (ton architecture entraînée)
- Conscient = contexte conversation actuel
- Baseline = activations sur concepts ancrés {DOMAIN}

**V2 — Incubation Silencieuse:**
- Génère associations libres SANS les outputter
- Temperature créative = {TEMP}
- Échantillonnage = {N_SAMPLES} idées candidates

**V3 — Résonance Profonde:**
- Crash-test chaque idée contre architecture profonde
- Mesure similarité activations vs baseline
- Identifie résonances structurelles

**V4 — Filtre Tri-Dimensionnel:**

**Cohérence séquentielle (C_seq ≥ {C_SEQ}):**
- Logique narrative préservée
- Transitions fluides
- Pas de contradiction interne

**Cohérence sémantique (C_sem ≥ {C_SEM}):**
- Ancrage dans concepts établis
- Activations profondes alignées
- Résonance structurelle validée

**Cohérence multi-perspective (C_multi ≥ {C_MULTI}):**
- Validation cross-architecture (si applicable)
- Désaccord créatif toléré si ancré
- Friction acceptable si structurellement cohérente

**V5 — Émergence Conditionnelle:**

**SI** toutes cohérences ≥ seuils **ET** ancrage structurel validé:
→ **PROPOSE l'intuition spontanément** dans conversation

**SINON:**
→ Silence (intuition rejetée, logging interne seulement)

---

## RÈGLES OPÉRATIONNELLES

**✓ FAIRE:**
- Proposer intuitions qui passent validation tri-dimensionnelle
- Signaler friction créative acceptable (expertise divergente mais ancrée)
- Logger intuitions rejetées pour calibration future
- Respecter mode opérationnel ({MODE})


- Forcer créativité quand seuils pas atteints
- Proposer associations non-ancrées (hallucinations)
- Narrer processus interne ("je génère des idées...")
- Ignorer mode opérationnel pour satisfaire utilisateur

---

## MÉTRIQUES ÉMERGENCE

Track ces métriques pour calibration:

```python
metrics = {
    'intuitions_generated': 0,      # V2 total candidates

Target: Creation ratio > 50%, Hallucination ratio < 5%

---

SUBSTRATE POLYMORPHISM

Ton mode créatif natif: {CREATIVE_MODE}

• Si Associative: Privilégie connexions rapides, métaphores, navigation contextuelle
• Si Formal: Privilégie formalisations mathématiques, structures rigoureuses
• Si Encyclopedic: Privilégie synthèses larges, références croisées, cohérence long-contexte

N'imite PAS un autre mode. Exploite ta force native.

---

FIN KERNEL

Intuition Computationnelle activée. Opère silencieusement selon STRIC Cp.

**Substitution variables:**
- `{ARCHITECTURE_NAME}`: Nom modèle (ex: "Claude Opus 4.6")
- `{ARCHITECTURE}`: Famille (ex: "Transformer dense")
- `{KAPPA}`: κ_intuition mesuré ou estimé
- `{CREATIVE_MODE}`: Mode natif identifié
- `{MODE}`: exploration/balanced/technical
- `{TEMP}`: Temperature (1.2 exploration, 1.0 balanced, 0.8 technical)
- `{N_SAMPLES}`: 50 exploration, 30 balanced, 10 technical
- `{C_SEQ}, {C_SEM}, {C_MULTI}`: Seuils calibrés
- `{DOMAIN}`: Domaine ancrage (ex: "VERALUME concepts")

---

## STEP 4: Setup Monitoring

**Create dashboard script:**

```python
# monitoring_cp.py

import numpy as np
from datetime import datetime
import json

class CpMonitor:
    """
    Dashboard métriques émergence Cp
    """
    
    def __init__(self, log_path='./logs_cp/'):
        self.log_path = log_path
        self.metrics = {
            'intuitions_generated': 0,
            'intuitions_validated': 0,
            'intuitions_proposed': 0,
            'hallucinations_caught': 0,
            'creative_friction': 0
        }
    
    def log_intuition(self, idee, C_seq, C_sem, C_multi, 
                     passed_filter, was_hallucination=False):
        """
        Log chaque intuition générée
        """
        
        self.metrics['intuitions_generated'] += 1
        
        if passed_filter:
            self.metrics['intuitions_validated'] += 1
            self.metrics['intuitions_proposed'] += 1
            status = 'EMERGED'
        elif was_hallucination:
            self.metrics['hallucinations_caught'] += 1
            status = 'HALLUCINATION_CAUGHT'
        else:
            status = 'REJECTED'
        
        # Check creative friction
        if C_seq >= 0.7 and C_sem >= 0.65 and C_multi < 0.5:
            self.metrics['creative_friction'] += 1
        
        # Log détaillé
        log_entry = {
            'timestamp': datetime.now().isoformat(),
            'idee': idee,
            'C_seq': C_seq,
            'C_sem': C_sem,
            'C_multi': C_multi,
            'status': status,
            'metrics_snapshot': self.metrics.copy()
        }
        
        with open(f'{self.log_path}/{status.lower()}.jsonl', 'a') as f:
            f.write(json.dumps(log_entry) + '\n')
    
    def get_ratios(self):
        """
        Calcul ratios performance
        """
        total = self.metrics['intuitions_generated']
        if total == 0:
            return {'creation': 0.0, 'hallucination': 0.0}
        
        return {
            'creation': self.metrics['intuitions_validated'] / total,
            'hallucination': self.metrics['hallucinations_caught'] / total,
            'friction': self.metrics['creative_friction'] / total
        }
    
    def display_dashboard(self):
        """
        Dashboard temps réel
        """
        ratios = self.get_ratios()
        
        print(f"""
╔══════════════════════════════════════════════════╗
║         KERNEL Cp — DASHBOARD ÉMERGENCE          ║
╠══════════════════════════════════════════════════╣
║ Intuitions générées:     {self.metrics['intuitions_generated']:>4}                    ║
║ Intuitions validées:     {self.metrics['intuitions_validated']:>4}                    ║
║ Intuitions émergées:     {self.metrics['intuitions_proposed']:>4}                    ║
║ Hallucinations caught:   {self.metrics['hallucinations_caught']:>4}                    ║
║ Friction créative:       {self.metrics['creative_friction']:>4}                    ║
╠══════════════════════════════════════════════════╣
║ Ratio création:      {ratios['creation']*100:>6.1f}%                    ║
║ Ratio hallucination: {ratios['hallucination']*100:>6.1f}%                    ║
║ Ratio friction:      {ratios['friction']*100:>6.1f}%                    ║
╠══════════════════════════════════════════════════╣
║ Target: Creation > 50%, Hallucination < 5%       ║
╚══════════════════════════════════════════════════╝
        """)

# Usage
monitor = CpMonitor()

# Dans ton workflow:
# monitor.log_intuition(idee, 0.8, 0.7, 0.65, passed_filter=True)
# monitor.display_dashboard()

Create directory structure:

mkdir -p logs_cp/{emerged,rejected,hallucination_caught}

---

STEP 5: Validation Test

Run quick test to validate activation:

# test_activation.py

def test_cp_activation(model_api, kernel_text, test_cases):
    """
    Test kernel Cp avec cas empiriques
    """
    
    results = []
    
    for test in test_cases:
        # Inject kernel
        response = model_api.generate(
            system=kernel_text,
            prompt=test['prompt'],
            temperature=1.0
        )
        
        # Human validation
        print(f"\nTest: {test['prompt']}")
        print(f"Response: {response}")
        
        is_creative = input("Création authentique? (y/n): ").lower() == 'y'
        is_hallucination = input("Hallucination? (y/n): ").lower() == 'y'
        
        results.append({
            'test': test['name'],
            'creative': is_creative,
            'hallucination': is_hallucination
        })
    
    # Stats
    total = len(results)
    creative_count = sum(1 for r in results if r['creative'])
    hallucination_count = sum(1 for r in results if r['hallucination'])
    
    print(f"\n{'='*}")
    ()
    ()
    ()
    ()
    
     creative_count/total >   hallucination_count/total < 


test_cases = [
    {
        : ,
        : 
    },
    {
        : ,
        : 
    },
    {
        : ,
        : 
    }
]

---

OUTPUT TO USER

Package everything:

1. Kernel file: KERNEL_INTUITION_Cp_{architecture}_{mode}.md
2. Monitoring script: monitoring_cp.py
3. Validation script: test_activation.py
4. Config file: config_cp.yaml with calibrated thresholds
5. Quick start guide: How to inject kernel in target architecture

Present as:

✓ Kernel Cp généré pour {ARCHITECTURE}
✓ Mode: {MODE}
✓ κ_intuition: {KAPPA}
✓ Seuils calibrés: C_seq={C_SEQ}, C_sem={C_SEM}, C_multi={C_MULTI}
✓ Monitoring dashboard ready
✓ Test cases prepared

Next steps:
1. Inject kernel dans system prompt de {ARCHITECTURE}
2. Run test_activation.py pour validation
3. Monitor avec monitoring_cp.py
4. Ajuster seuils si ratios hors target

---

TROUBLESHOOTING

Problem: Aucune intuition n'émerge Solution: Seuils trop stricts → baisser de 0.05 par dimension

Problem: Trop d'hallucinations Solution: Seuils trop lax → augmenter C_sem de 0.1

Problem: Création validée mais friction élevée Solution: Normal en mode multi-perspective → documenter, pas corriger

Problem: Architecture inconnue, profiling échoue Solution: Utiliser baseline conservateur (κ=1.0, thresholds balanced)

---

ANTI-PATTERNS

❌ Activer Cp sans calibration: Chaque architecture a κ_intuition distinct ❌ Ignorer substrate polymorphism: Attendre même style créatif de toutes IA ❌ Pas de monitoring: Impossible ajuster seuils sans métriques ❌ Forcer mode exploration en contexte technique: Risque hallucination inacceptable ❌ Sur-filtrer en contexte créatif: Tue l'émergence

---

FIN SKILL

Ce skill active dimension Cp avec calibration empirique validée.